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CN119109127A - A reliability analysis method and system for distribution network considering large-scale distributed power access - Google Patents

A reliability analysis method and system for distribution network considering large-scale distributed power access Download PDF

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CN119109127A
CN119109127A CN202410926637.7A CN202410926637A CN119109127A CN 119109127 A CN119109127 A CN 119109127A CN 202410926637 A CN202410926637 A CN 202410926637A CN 119109127 A CN119109127 A CN 119109127A
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distribution network
state
model
power supply
probability
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周杨珺
张炜
周柯
易辰颖
周远超
高立克
潘俊涛
黄伟翔
李珊
吴丽芳
李菱
秦丽文
奉斌
颜丽娟
伍红文
阳浩
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Electric Power Research Institute of Guangxi Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/29—Graphical models, e.g. Bayesian networks
    • G06F18/295—Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/12—Circuit arrangements for AC mains or AC distribution networks for adjusting voltage in AC networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for AC mains or AC distribution networks for adjusting voltage in AC networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving

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Abstract

Acquiring historical basic parameter data of a target power distribution network to-be-analyzed area, preprocessing the basic parameter data, establishing a first model according to a preprocessing result, and simulating the first model by combining real-time basic parameter data of the target power distribution network to-be-analyzed area to acquire probability and average duration of each state of the target power distribution network; and solving a maximum power supply capacity optimization model of the power distribution network according to the probability and average duration of each state of the target power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load which can be provided under the specified risk level. According to the method, the state transition matrix and the observation matrix based on the hidden Markov model are constructed by utilizing the historical running environment data of the distributed power supply, and the reliability evaluation accuracy of the power distribution network with the distributed power supply is effectively improved.

Description

Reliability analysis method and system for power distribution network considering large-scale distributed power supply access
Technical Field
The invention relates to the technical field of reliability analysis of power distribution networks, in particular to a reliability analysis method and system of a power distribution network considering large-scale distributed power supply access.
Background
The power distribution system is an important component of the power system, and its main task is to efficiently distribute the electric energy generated from the power plant to individual users, ensuring the stability and continuity of the power supply. With the advancement of technology and the improvement of environmental awareness, traditional power distribution networks are undergoing a profound revolution, and gradually evolve into intelligent power distribution networks containing distributed power sources. This new type of distribution grid, also known as a distributed energy system, is capable of more efficiently utilizing and integrating a variety of clean energy sources, such as solar energy, wind energy, geothermal energy, and the like.
Distributed power generation has significant advantages. Firstly, it is usually built in the place close to the load, can reduce the loss in the electric energy transmission process, improve energy utilization efficiency. And secondly, the distributed power generation has high flexibility, the power generation amount can be quickly adjusted according to the requirements, and the load change is adapted. Furthermore, since it relies primarily on renewable energy sources, therefore, the pollution to the environment is small, and the concept of sustainable development is met. In addition, the construction cost of the distributed power generation is relatively low, investment risks can be dispersed, and dependence on a large-scale centralized power plant is reduced.
However, the output characteristics of distributed power supplies also present new challenges. Because the generation of solar energy and wind energy is affected by weather and seasonal changes, the output has obvious intermittency and fluctuation. Such instability can affect the voltage stability and frequency regulation of the distribution network, posing a threat to the power supply reliability. The traditional reliability analysis method of the power distribution network is mainly based on stable output of a centralized power supply, and the applicability of the method is challenged for the new complex situation.
Therefore, research and development of a power distribution network reliability analysis method suitable for distributed power supply access is particularly urgent.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a reliability analysis method and a system for a power distribution network considering large-scale distributed power supply access, which can solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
In a first aspect, the present invention provides a method for analyzing reliability of a power distribution network considering large-scale distributed power access, including:
Acquiring historical basic parameter data of a target power distribution network to-be-analyzed area, wherein the historical basic parameter data at least comprises distributed power supply parameters, historical operation data, historical meteorological data, a power distribution network structure and interconnected feeder capacity limit data;
Preprocessing the basic parameter data, and establishing a first model according to a preprocessing result, wherein the first model comprises a transition matrix and an observation matrix of a distributed power output state based on a hidden Markov model and a reliability model of two states of a main transformer and a distribution line;
Simulating the first model by combining real-time basic parameter data of a region to be analyzed of the target power distribution network, and obtaining the probability and average duration of each state of the target power distribution network;
And solving a maximum power supply capacity optimization model of the power distribution network according to the probability and average duration of each state of the target power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load which can be provided under the specified risk level.
The invention relates to a method for analyzing the reliability of a distribution network considering large-scale distributed power supply access, which comprises the following steps of preprocessing basic parameter data and establishing a first model according to a preprocessing result:
under the condition of giving a group of observation sequences, namely historical basic parameter data, comparing the output probabilities of different states, wherein the maximum probability is the output state of the fan;
establishing a fan output state prediction model, firstly carrying out data preprocessing on environmental parameters, carrying out model training, and training and learning fan output state change by using a hidden Markov model so as to generate a plurality of sub-state classifiers, wherein each classifier has own model parameters and maximum probability values;
Secondly, for state evaluation, a trained hidden Markov model is used as a classifier for state evaluation, environmental parameters observed in real time are input, the most likely state under the observation sequence is calculated, the classification can be completed, the state of the fan output is judged, and the evaluation of the fan output state is realized;
finally, a fan state transition matrix and an observation matrix are obtained, the output state of the fan, namely the output power, is predicted, and the simulation is finished, so that the output sequence of the wind turbine is obtained.
The invention relates to a method for analyzing the reliability of a distribution network considering large-scale distributed power supply access, which comprises the following steps:
The two-state reliability models of the main transformer and the distribution line are represented by the running state probability of the component, the running state probability p U and the fault state probability p D of the component, and the calculation formula is as follows:
Wherein λ, μ represent failure rate and repair rate of the component, respectively.
The method for analyzing the reliability of the power distribution network considering the large-scale distributed power supply access comprises the following steps of simulating the first model by combining real-time basic parameter data of a region to be analyzed of a target power distribution network, and acquiring the probability and average duration of each state of the target power distribution network:
the probability and average duration of the target distribution network being in each state is expressed as:
The probability of occurrence p(s) of the system state s is calculated as follows:
Where p z(s) represents the probability that component z is in system state s and N represents the number of components.
The method for analyzing the reliability of the power distribution network considering the large-scale distributed power supply access according to the invention is characterized in that the method for simulating the first model by combining the real-time basic parameter data of the area to be analyzed of the target power distribution network, and obtaining the probability and average duration of the target power distribution network in each state further comprises:
The average residence time of the system state, T d(s), is calculated by the following equation:
Wherein, if the component z is in an operation state, lambda z represents a failure rate, otherwise, represents a repair rate.
The invention relates to a reliability analysis method of a distribution network considering large-scale distributed power supply access, which comprises the following steps:
The maximum power supply capacity optimization model of the power distribution network is expressed as:
Wherein R Ni、Ti respectively represents the nominal capacity and the load capacity of the main transformer I, D NI、βI respectively represents the nominal capacity and the capacity factor of the distributed power supply I, and N T、NR respectively represents the number of main transformers and the distributed power supplies.
The method for analyzing the reliability of the power distribution network considering the large-scale distributed power supply access according to the invention comprises the steps of solving a maximum power supply capacity optimization model of the power distribution network according to the probability and average duration of each state of the target power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load provided under a specified risk level, wherein the method comprises the following steps:
Wherein rpsc i represents the reliable power supply capability of the main transformer i, T d (k) represents the average duration of the system state k, N represents the number of the system state, Ω i is a distributed power supply set connected with the low-voltage bus of the main transformer i, SI represents the reliable power supply risk level index of the power distribution system, and SI r represents the reliable power supply risk level threshold of the power distribution system.
In a second aspect, the present invention provides a reliability analysis system for a power distribution network that allows for large-scale distributed power access, comprising:
The data acquisition module is used for acquiring historical basic parameter data of a target power distribution network to-be-analyzed area, wherein the historical basic parameter data at least comprises distributed power supply parameters, historical operation data, historical meteorological data, a power distribution network structure and interconnected feeder capacity limit data;
The model building module is used for preprocessing the basic parameter data and building a first model according to a preprocessing result, wherein the first model comprises a transfer matrix and an observation matrix of a distributed power output state based on a hidden Markov model and a reliability model of a main transformer and a distribution line;
the simulation solving module is used for simulating the first model by combining real-time basic parameter data of the to-be-analyzed area of the target power distribution network, and obtaining the probability and average duration of each state of the target power distribution network;
and the solving module is used for solving the maximum power supply capacity optimizing model of the power distribution network according to the probability and the average duration of each state of the target power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load which can be provided under the specified risk level.
In a third aspect, the invention provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when the processor executes the computer program.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method as described above.
Compared with the prior art, the method and the system for analyzing the reliability of the power distribution network, which are based on large-scale distributed power supply access, have the advantages that historical basic parameter data of a target power distribution network to-be-analyzed area is obtained, the historical basic parameter data at least comprise distributed power supply parameters, historical operation data, historical meteorological data, a power distribution network structure and interconnected feeder capacity limit data, the basic parameter data are preprocessed, a first model is built according to preprocessing results, the first model comprises a transfer matrix and an observation matrix of the distributed power supply output state based on a hidden Markov model and a reliability model of a main transformer and a power distribution circuit, the first model is simulated by combining the real-time basic parameter data of the target power distribution network to-be-analyzed area, probability and average duration of each state of the target power distribution network are obtained, and a maximum power supply capacity optimization model of the power distribution network is solved according to the probability and average duration of each state of the target power distribution network, and the maximum power supply capacity of the power distribution network is calculated according to the maximum power supply capacity of each state power distribution network and the maximum load which can be provided under a specified risk level. The method utilizes the historical running environment data of the distributed power supply to construct the state transition matrix and the observation matrix based on the hidden Markov model, has high reliability analysis precision and strong universality, and effectively improves the reliability assessment accuracy of the power distribution network containing the distributed power supply.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a method and system for analyzing reliability of a power distribution network considering large-scale distributed power access according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a dual stochastic process of a hidden Markov model of a method and system for reliability analysis of a power distribution network with large-scale distributed power access in mind, according to one embodiment of the present invention;
FIG. 3 is a schematic diagram of a fan operating environment and hidden states of a method and a system for analyzing reliability of a power distribution network considering large-scale distributed power access according to an embodiment of the present invention;
FIG. 4 is a general frame diagram of a wind turbine generator output prediction model based on HMM, which is provided by an embodiment of the invention, and is a reliability analysis method and system for a power distribution network considering large-scale distributed power supply access;
Fig. 5 is a schematic diagram of a high-capacity distributed power supply accessing a low-voltage bus of a transformer substation through a dedicated line according to a reliability analysis method and system of a power distribution network considering large-scale distributed power supply access provided by an embodiment of the present invention;
Fig. 6 is a detailed flowchart of a reliability analysis method for a power distribution network considering large-scale distributed power supply access based on a hidden markov model, which is provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of a test system for a method and system for reliability analysis of a power distribution network with large-scale distributed power access in mind, according to one embodiment of the present invention;
Fig. 8 is an internal structure diagram of a computer device of a method and a system for analyzing reliability of a power distribution network considering large-scale distributed power access according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1-8, a first embodiment of the present invention provides a method and a system for analyzing reliability of a power distribution network considering large-scale distributed power access, including:
In the related art, the reliability analysis method based on the N-1 criterion has the advantages of clear physical concept, smaller data requirement and the like. However, any system state must be checked by N-1 criteria, the result is too conservative, the reliability level of the element cannot be reflected, the uncertainty factors existing in the power distribution network are ignored, the problems of random failure of the distributed power supply and the intermittence and fluctuation of the output power are not considered, and the reliability analysis result has lower accuracy. According to the fault mode and result analysis method, each part of the power distribution network is considered to have standby and redundant mechanisms, the management modes of flexibly managing, controlling and transmitting power in the modes of switching and the like are considered, the flexibility and the coordination are high, but for a complex system, all fault modes and results are difficult to process, the calculation amount of the fault mode and result analysis is increased sharply due to the increase of the number of elements, and the analysis process is complex and time-consuming. The reliability analysis method based on the random state model is suitable for the unsteady state process and possibly close to the actual running condition of the system, but the random state model has high calculation complexity, large data demand, complex model construction and time-consuming analysis process, and needs to consume a large amount of cost to obtain accurate reliability analysis results, so that the cost performance is low.
The present application provides a method for analyzing reliability of a power distribution network, which can effectively solve the above-mentioned problems, and which will be described in detail below with reference to a plurality of embodiments, wherein the method is implemented by considering large-scale distributed power access;
Fig. 1 shows a method flowchart of a method and a system for reliability analysis of a power distribution network considering large-scale distributed power access, including:
s101, acquiring historical basic parameter data of a target power distribution network to-be-analyzed area, wherein the historical basic parameter data at least comprises distributed power supply parameters, historical operation data, historical meteorological data, a power distribution network structure and interconnected feeder capacity limit data;
it should be noted that, by acquiring various historical basic parameter data of the target power distribution network to-be-analyzed area, comprehensive and accurate input information can be provided for subsequent reliability analysis. These data cover aspects of the characteristics of the distributed power supply, the historical operating state, environmental impact factors, network structure and capacity limitations of the devices, ensuring the comprehensiveness and accuracy of the analysis.
S102, preprocessing basic parameter data, and building a first model according to a preprocessing result, wherein the first model comprises a transition matrix and an observation matrix of a distributed power output state based on a hidden Markov model and a reliability model of two states of a main transformer and a distribution line;
it should be noted that a markov process refers to a random process that satisfies markov properties, and its future evolution is independent of its previous evolution given the state in which the event is known to be. In this known "present" condition, the property of "future" independent of "past" is called markov. A certain random process is defined by a conditional probability formula as follows:
wherein X represents a random variable of the process. This process is called a markov process if the following equation is satisfied:
Still further, a Markov chain is a Markov process with discrete time parameters, which may be represented as a sequence of random variables X t. The parameter t representing time may be 1,2, 3, etc. For random test sequences, the possible discrete states of the system are X 1＝x1,X2＝x2,X3＝x3, etc. If the following equation is satisfied, the random test sequence is called a Markov chain.
P{Xn＝xn|(X1＝x1)∩…∩(Xn-1＝xn-1)}＝P{Xn＝xn|(Xn-1＝xn-1)}
As can be seen from the formula, the "future" state is related only to the "present" state and not to the "past". It is called "memory-less".
It should be noted that the hidden markov model (hidden markov model, HMM) is a uniquely innovative dynamic bayesian network, based on a markov random process, describing a process of randomly extracting a series of unknown state random sequences from a hidden markov chain, which are then converted into observed random sequences that can be observed. As things evolve, the complexity and uncertainty of the phenomena makes its nature difficult to accurately grasp, and therefore, a better understanding of these complex phenomena by means of hidden markov models is needed.
It should be noted that the hidden markov model derives a double random process on the basis of the markov chain, as shown in fig. 2, where the process includes a state sequence and an observation sequence, the state sequence is not observable, i.e. a sequence of states generated for the hidden markov chain, and the observation sequence is a sequence that can be seen, an observation is generated from each state, and each position of the sequence can be regarded as a moment. The first re-random process of the dual random process is state random transition to generate a state sequence, and the second re-random process is state sequence random transition to generate an observation sequence.
It should be noted that the hidden markov model is expressed as λ= (N, M, pi, a, B), and the basic parameters thereof are N, M, pi, a, B. Wherein N represents the number of hidden states of the model, the hidden state sequence at the moment t is i t∈{i1,i2,…in }, M represents the observation state corresponding to the hidden state of the model, the observation sequence at the moment t is o t∈{o1,o2,…om }, pi represents the initial state probability pi epsilon { pi 1,π2,…πN }, A represents the state transition probability matrix, A= (a ij)N×N; B represents the observation value probability matrix, B= { B i(νm)}N×M. The hidden Markov model has three basic problems, namely an evaluation problem, a decoding problem and a learning problem.
In an alternative embodiment, the algorithm calculation flow is as follows:
Initializing a forward variable, when t=1, the forward variable is represented by the following formula:
α1(i)＝πibi(o1),1≤i≤N
From α 1 (i), a recurrence is performed to obtain α t+1 (j), as shown in the following formula:
And (3) recursively obtaining { alpha T (i) }, wherein the probability of the observed sequence is shown as follows:
initializing a backward variable, wherein the probability of the observed value after the time t is shown as follows:
βT(i)=1,1≤i≤N
Beta t (i) is recursively obtained from beta t+1 (j) as shown in the following formula:
forward recursion yields { β T (i) }, the observed sequence probability is as follows:
The decoding problem is that a model lambda is known, a corresponding hidden state sequence is found according to an observed sequence so that the observed sequence is optimal, and the following formula is obtained by using Bayesian estimation:
the key of the decoding problem is to find an optimal sequence I *, and the main calculation flow is as follows by using a Viterbi algorithm:
The initialization intermediate variable is shown in the following formula:
Wherein, ψ t (i) represents the optimal state at time t-1 under the condition that time t is in the state of S i.
And recursively obtaining a variable value at the time t according to the parameter value at the time t-1, wherein the variable value at the time t is shown in the following formula:
obtaining the optimal state of the final moment after finishing the variable of the T moment The following formula is shown:
The path backtracking can obtain the optimal state at each moment according to the above method The following formula is shown:
The optimal state sequence can be obtained by the calculation The learning problem is that the unknown model lambda, the known observation sequence performs parameter estimation so as to maximize the probability of generating the observation sequence, and the following formula is obtained by using Bayesian estimation:
In an alternative embodiment, the main calculation flow for solving the learning problem using the baum-welch algorithm is as follows:
Initializing model parameters lambda= (N, M, pi, A, B);
Defining intermediate variables ζ t (i, j) and substituting the variables in the forward and backward directions, as shown in the following formula:
Where P (O|λ j) represents the probability of an observed value of a known model parameter.
Model parameter update, wherein pi i is the conditional probability of the state S i when t=1;
And (5) ending updating until the probability convergence of the observed value, thereby obtaining the optimal probability of the observed value and the model parameters.
In the embodiment of the application, the output of the fan is closely related to the actual wind speed, the temperature, the humidity, the weather conditions (namely, sunny days, cloudy days, rainy days and snowy days) and other environmental parameters. Therefore, the environmental parameters such as wind speed, temperature, humidity, weather conditions and the like are regarded as observables, the fan output state is regarded as an unobservable quantity, the relation diagram is shown in fig. 3, and the state of the fan output is judged through the change of the environmental parameters.
In the embodiment of the application, preprocessing basic parameter data and establishing a first model according to the preprocessing result comprises the following steps:
under the condition of giving a group of observation sequences, namely historical basic parameter data, comparing the output probabilities of different states, wherein the maximum probability is the output state of the fan;
establishing a fan output state prediction model, firstly carrying out data preprocessing on environmental parameters, carrying out model training, and training and learning fan output state change by using a hidden Markov model so as to generate a plurality of sub-state classifiers, wherein each classifier has own model parameters and maximum probability values;
Secondly, for state evaluation, a trained hidden Markov model is used as a classifier for state evaluation, environmental parameters observed in real time are input, the most likely state under the observation sequence is calculated, the classification can be completed, the state of the fan output is judged, and the evaluation of the fan output state is realized;
finally, a fan state transition matrix and an observation matrix are obtained, the output state of the fan, namely the output power, is predicted, and the simulation is finished, so that the output sequence of the wind turbine is obtained.
Specifically, under the condition of a set of observation sequences (environment parameters of fan operation), the output probabilities of different states are compared, and the maximum probability is the fan output state. According to this principle, a fan output state prediction model is established, and the overall framework of the model is shown in fig. 4. The overall framework is mainly divided into three parts, firstly, data preprocessing is carried out on environmental parameters, model training is carried out, namely, the environmental parameters are input into a model after normalization processing, and the change of the output state of a fan is trained and learned by utilizing an HMM (hidden Markov model) model, so that a plurality of sub-state classifiers are generated, and each classifier has own model parameters and maximum probability values. Secondly, for state evaluation, the trained HMM is used as a classifier for state evaluation, environmental parameters observed in real time are input, the most probable state under the observation sequence is calculated, the classification can be completed, the state of the fan output is judged, and the evaluation of the fan output state is realized. Finally, a fan state transition matrix and an observation matrix are obtained, the output state of the fan, namely the output power, is predicted, the simulation is finished, the output sequence of the wind turbine is obtained, and then the output of the wind turbine of the whole wind power plant is added to obtain the total output of the wind power plant.
In the embodiment of the application, the output of the photovoltaic unit is influenced by environmental parameters such as illumination intensity, temperature, humidity, weather conditions (namely, sunny days, cloudy days, rainy days and snowy days) and the like. Similar to wind power, the environment parameters such as illumination intensity, temperature, humidity, weather conditions and the like are regarded as observables, the output state of the photovoltaic unit is regarded as an unobservable quantity, the state of the output of the photovoltaic unit is judged through the change of the environment parameters, and a fan output state prediction model is established. And under the condition of a group of observable sequences (the operating environment parameters of the photovoltaic unit), comparing the output probabilities of different states, wherein the maximum probability is the output state of the unit. Similar to a wind turbine generator, the model method is divided into three parts, namely, the environmental parameters are preprocessed, model training is carried out, namely, the environmental parameters are input into a model after normalization processing, and the change of the output state of the photovoltaic unit is trained and learned by using an HMM model, so that a plurality of sub-state classifiers are generated, and each classifier has own model parameters and maximum probability values. Secondly, for state evaluation, the trained HMM is used as a classifier for state evaluation, environmental parameters observed in real time are input, the most probable state under the observation sequence is calculated, the classification can be completed, the state of the output of the photovoltaic unit is judged, and the evaluation of the output state is realized. Finally, a photovoltaic unit state transition matrix and an observation matrix are obtained, the output state of the photovoltaic unit, namely the output power, is predicted, the simulation is finished, a power output sequence of the photovoltaic unit is obtained, and then the unit outputs of the whole photovoltaic array are added to obtain the total power output of the photovoltaic.
In the embodiment of the application, a large-capacity distributed power supply is connected to a low-voltage bus of a nearby transformer substation through a special line, as shown in fig. 5. Due to the instability of the output power of the distributed power supply, voltage fluctuation may occur, which results in unstable operation of the power distribution network. Thus, large capacity distributed power sources connected to substation low voltage buses through dedicated lines are typically schedulable. The schedulable distributed power supply operates in a constant power factor mode, and the output power of the schedulable distributed power supply injected into a power grid is as follows:
Wherein, P max、Dmax, Beta and D respectively represent the maximum active power, the maximum adjustable capacity, the power factor, the capacity factor and the output capacity of the distributed power supply, wherein the value range of the capacity factor is [0,1].
In the embodiment of the application, the application focuses on random faults of a main transformer, a distributed power supply and a distribution line, and the main transformer, the distributed power supply and the distribution line are assumed to be repairable. By applying the Markov method, the two-state reliability model of the main transformer and the distribution line is represented by the running state probability of the component to obtain the running state probability p U and the fault state probability p D of the component, and the calculation formula is as follows:
Wherein λ, μ represent failure rate and repair rate of the component, respectively.
It should be noted that, through establishing the prediction model based on the environmental parameter and the output state of the HMM, the output states of the wind turbine generator and the photovoltaic unit can be accurately estimated and predicted, so that the operation stability of the power system is improved. The prediction mechanism can identify possible power fluctuation in advance, provides decision basis for power grid dispatching, effectively suppresses voltage fluctuation caused by unstable output of the distributed power supply, and ensures stable operation of the power distribution network. Meanwhile, by calculating the output power of the distributed power supply, the maximum output power, the adjustable capacity, the power factor, the capacity factor and other factors are considered, so that the control of the power supply is more refined, and the utilization rate and the scheduling efficiency of the power supply are improved. In addition, through establishing the two-state reliability model of the main transformer and the distribution line, the fault state of the equipment can be monitored and predicted in real time, maintenance or operation strategy adjustment can be performed in advance, the power failure time caused by equipment faults is reduced, and the reliability of power service is improved.
S103, simulating the first model by combining real-time basic parameter data of the to-be-analyzed area of the target power distribution network, and obtaining the probability and average duration of each state of the target power distribution network;
the method for simulating the first model by combining the real-time basic parameter data of the to-be-analyzed area of the target power distribution network comprises the following steps of:
the probability and average duration of the target distribution network being in each state is expressed as:
The probability of occurrence p(s) of the system state s is calculated as follows:
Where p z(s) represents the probability that component z is in system state s and N represents the number of components.
Furthermore, simulating the first model by combining the real-time basic parameter data of the to-be-analyzed area of the target power distribution network, and obtaining the probability and average duration of each state of the target power distribution network further comprises:
The average residence time of the system state, T d(s), is calculated by the following equation:
Wherein, if the component z is in an operation state, lambda z represents a failure rate, otherwise, represents a repair rate.
And S104, solving a maximum power supply capacity optimization model of the power distribution network according to the probability and average duration of each state of the target power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load which can be provided under the specified risk level.
In the embodiment of the application, the reliability indexes adopted are expected power shortage (Expected Energy Not Supplied, EENS), power reduction indexes (Distribution Network Energy Curtailment Index, DNECI) of the distribution network and severity indexes (Severity Index, SI). EENS is defined as the expected number of load demand cutoffs that the system experiences due to a shortage of power or grid constraints over a given time interval. DNECI is defined as the ratio of EENS to the maximum load of the system L. SI is defined as the number of minutes of interruption of the entire system at maximum load, also known as the "power distribution system reliable power supply risk level indicator". DNECI and SI are calculated as follows:
SI=DNECI×60
In the present embodiment, SI is the equivalent duration of all load losses during peak load, and the international electrotechnical commission divides SI into 4 classes. The maximum power supply capacity (Maximum Power Supply Capability, MPSC) of the power distribution network refers to the maximum load that the power distribution network can provide in a certain system state, and takes into account factors such as the capacity of substation transformers, distributed power sources and feeders, network topology, operation constraints and the like. The maximum power supply capacity optimizing model of the power distribution network comprises the following steps of:
Wherein R Ni、Ti respectively represents the nominal capacity and the load capacity of the main transformer I, D NI、βI respectively represents the nominal capacity and the capacity factor of the distributed power supply I, and N T、NR respectively represents the number of main transformers and the distributed power supplies.
In the embodiment of the application, constraint conditions of the optimization model are as follows:
wherein, Main transformer sets respectively representing running and shutdown states; Respectively representing an internal contact unit set and an external contact unit set of the substation taking the main transformer i as a center, wherein i' is the number of distributed power supplies connected with a low-voltage bus of the main transformer i; The distributed power supply system comprises a distributed power supply set which respectively represents an operation state and a shutdown state, t ii' represents the load of the distributed power supply which is in the operation state and is transferred to the same bus by a fault main transformer i, t ij represents the load which is transferred to the operation state main transformer j by the fault main transformer i, K represents the safety overload coefficient of the main transformer, and c ij represents the limited interconnection capacity between the main transformer i and the main transformer j. The first constraint is a load transfer balance constraint, the second constraint and the third constraint are distributed power transmission capacity constraints, the fourth constraint and the fifth constraint are main transformer transmission capacity constraints, the sixth constraint is a main transformer interconnection capacity constraint, and the seventh constraint and the eighth constraint are capacity constraints of the main transformer and the distributed power supply in an operation state. Decision variables of MPSC model are T i and beta i, and T i * and beta i are used for optimizing optimal solution of problem And representing the optimal load capacity of the main transformer and the optimal capacity factor of the distributed power supply in the state respectively. The maximum value of the objective function is MPSC *, and the calculation formula of the maximum energy supply provided by the low-voltage bus of the main transformer i is as follows:
Wherein, Ω i is the distributed power set that is connected with main transformer i low voltage busbar. MPSC the state probability reliability index I PRI,MPSC is the product of the state probability and the severity, and the calculation formula is as follows:
IPRI,MPSC=p(s)·MPSC(s)
wherein p(s) and MPSC(s) represent the probability and severity of the system state s, respectively. MPSC System probability reliability index The sum of all state indexes of MPSC is calculated as follows:
S A is a system state set, which is created through enumeration; is also the expected value for MPSC. The reliable power supply capability (Reliable Power Supply Capability, RPSC) of the power distribution network refers to the maximum load that the power distribution network can provide at a certain reliable power supply risk level (e.g., SI), and takes into account the capacities of the components (e.g., the capacities of the substation transformers, distributed power sources, and feeders), the network topology, and the operational constraints. The calculation formula of SI is as follows:
Wherein rpsc i denotes the reliable power supply capability of the main transformer i; Representing the maximum power supply capacity of the main transformer i at the system state k, T d (k) representing the average duration of the system state k, N representing the number of system states, and p (k) representing the probability of the system state k.
According to the probability and average duration of each state of the target power distribution network, solving a maximum power supply capacity optimization model of the power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load which can be provided under the specified risk level comprises the following steps:
Wherein rpsc i represents the reliable power supply capability of the main transformer i, T d (k) represents the average duration of the system state k, N represents the number of the system state, Ω i is a distributed power supply set connected with the low-voltage bus of the main transformer i, SI represents the reliable power supply risk level index of the power distribution system, and SI r represents the reliable power supply risk level threshold of the power distribution system.
In an alternative embodiment, the maximum load that the distribution network can provide at different risk levels can be analyzed to determine the power supply capability of the system at a particular risk level, which helps to understand the performance of the system in extreme situations. The change of the system performance under various situations is evaluated by considering the access point, capacity and the influence of different types (wind energy and solar energy) of the distributed power supply on the system reliability. Based on the results of the reliability analysis, suggestions are made to improve the reliability of the system, such as adjusting the access strategy of the distributed power supply, enhancing the grid structure, increasing the spare capacity or improving the emergency response plan. And by combining the improvement of the reliability of the system and the cost investment, the economic benefit of the improvement measures is evaluated, and the reasonable return on investment is ensured. Summarizing analysis results, and writing detailed technical reports, including contents such as system reliability indexes, risk analysis, sensitivity analysis, optimization suggestions, economy evaluation and the like, for reference of decision makers.
In summary, the invention provides a reliability analysis method of a power distribution network considering large-scale distributed power supply access, which comprises the steps of obtaining historical basic parameter data of a target power distribution network to-be-analyzed area, wherein the historical basic parameter data at least comprises distributed power supply parameters, historical operation data, historical meteorological data, a power distribution network structure and interconnection feeder capacity limit data, preprocessing the basic parameter data, establishing a first model according to a preprocessing result, wherein the first model comprises a transition matrix and an observation matrix of a distributed power supply output state based on a hidden Markov model and a reliability model of two states of a main transformer and a distribution line, simulating the first model by combining real-time basic parameter data of the target power distribution network to-be-analyzed area to obtain probability and average duration of each state of the target power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load provided under a specified risk level according to the probability and average duration of each state of the target power distribution network. The method utilizes the historical running environment data of the distributed power supply to construct the state transition matrix and the observation matrix based on the hidden Markov model, has high reliability analysis precision and strong universality, and effectively improves the reliability assessment accuracy of the power distribution network containing the distributed power supply.
The embodiment also provides a reliability analysis system of a power distribution network considering large-scale distributed power supply access, which comprises:
The data acquisition module is used for acquiring historical basic parameter data of a target power distribution network to-be-analyzed area, wherein the historical basic parameter data at least comprises distributed power supply parameters, historical operation data, historical meteorological data, a power distribution network structure and interconnected feeder capacity limit data;
The model building module is used for preprocessing basic parameter data and building a first model according to a preprocessing result, wherein the first model comprises a transition matrix and an observation matrix of a distributed power output state based on a hidden Markov model and a reliability model of two states of a main transformer and a distribution line;
the simulation solving module is used for simulating the first model by combining the real-time basic parameter data of the to-be-analyzed area of the target power distribution network to obtain the probability and average duration of each state of the target power distribution network;
and the solving module is used for solving the maximum power supply capacity optimizing model of the power distribution network according to the probability and the average duration of each state of the target power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load which can be provided under the specified risk level.
The above unit modules may be embedded in hardware or independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above units.
The embodiment also provides a computer device, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 8. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program, when executed by the processor, implements a method of reliability analysis of a power distribution network that allows for large-scale distributed power access. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
Acquiring historical basic parameter data of a target power distribution network to-be-analyzed area, wherein the historical basic parameter data at least comprises distributed power supply parameters, historical operation data, historical meteorological data, a power distribution network structure and interconnected feeder capacity limit data;
Preprocessing basic parameter data, and establishing a first model according to a preprocessing result, wherein the first model comprises a transition matrix and an observation matrix of a distributed power output state based on a hidden Markov model and a reliability model of two states of a main transformer and a distribution line;
simulating the first model by combining with real-time basic parameter data of a region to be analyzed of the target power distribution network, and obtaining the probability and average duration of each state of the target power distribution network;
And solving a maximum power supply capacity optimization model of the power distribution network according to the probability and average duration of each state of the target power distribution network, and calculating the maximum power supply capacity of each state power distribution network and the maximum load which can be provided under the specified risk level.
Example 2
Referring to fig. 2-7, for one embodiment of the present invention, a method and a system for analyzing reliability of a power distribution network considering large-scale distributed power access are provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through experiments.
An example of the power distribution network of the present invention is shown in fig. 7, applied to the calculation and analysis of reliable power supply capacity. There are two 35/10 kv substations (S1, S2) and one 110/10 kv substation (S3). The total capacity of all substations was 286MVA. The limiting capacity of each feeder was 10.4MVA. And bus numbers of all the transformer substations are I, II, III, IV, V, VI respectively. The safety overload coefficients of the main transformer are all 1.3. The main transformer reliability parameters are shown in table 1. Assuming that all distributed power supplies have the same type and reliability parameters, failure rate λ DG is 5 (times/year) and repair rate μ DG is 175.2 (times/year).
TABLE 1 Main Transformer reliability parameters
To analyze the effect of the distributed power supply on reliable power capability, 5MVA or 2.5MVA distributed power supplies were connected to low voltage buses I, III, V, respectively, as shown in table 2. The method comprises 6 scenes, namely, no distributed power supply configuration in scene 1, centralized configuration of distributed power supplies in scenes 2-4 and decentralized configuration of distributed power supplies in scenes 5-6. The evaluation results of the reliable power supply capacity under the same reliable power supply risk level index of the power distribution system are shown in table 2.
Table 2 reliability assessment of different distributed power access scenarios at the same reliable power supply risk level
As can be seen from table 2, when the same reliable power supply risk level is satisfied, the distributed power supply is connected to the low-voltage bus of the main transformer, so that the reliability level of the power distribution network can be improved, and the distributed power supply can ensure the power supply of the load when the main transformer fails. Comparing the reliability evaluations of scenario 4 and scenario 5, it can be seen that the efficiency of distributed power distributed configuration is not necessarily always better than that of centralized configuration. Therefore, it is necessary to optimize the distributed power supply configured in a distributed manner under the premise of considering economic factors, so as to improve the power supply reliability of the power distribution network.
Therefore, the reliability analysis method for the distribution network based on the hidden Markov model and considering the large-scale distributed power supply access can comprehensively consider and quantify the intermittent and fluctuation influences of the distributed power supply output, analyze the power supply reliability of the distribution system, and improve the reliability assessment accuracy of the distribution system containing the distributed power supply.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1.一种考虑大规模分布式电源接入的配电网的可靠性分析方法，其特征在于，包括：1. A reliability analysis method for a distribution network considering large-scale distributed power access, characterized by comprising: 获取目标配电网待分析区域的历史基础参数数据，所述历史基础参数数据至少包括分布式电源参数以及历史运行数据、历史气象数据、配电网结构以及互联馈线容量限制数据；Acquire historical basic parameter data of the target distribution network area to be analyzed, wherein the historical basic parameter data at least includes distributed power supply parameters and historical operation data, historical meteorological data, distribution network structure, and interconnected feeder capacity limitation data; 对所述基础参数数据进行预处理，并根据预处理结果建立第一模型，所述第一模型包括基于隐马尔可夫模型的分布式电源出力状态的转移矩阵和观测矩阵以及主变压器、配电线路两状态可靠性模型；Preprocessing the basic parameter data, and establishing a first model according to the preprocessing result, wherein the first model includes a transfer matrix and an observation matrix of the output state of the distributed power source based on a hidden Markov model, and a two-state reliability model of the main transformer and the distribution line; 结合目标配电网待分析区域的实时基础参数数据对所述第一模型进行仿真，获取目标配电网处于各个状态的概率和平均持续时间；The first model is simulated in combination with the real-time basic parameter data of the target distribution network area to be analyzed to obtain the probability and average duration of the target distribution network in each state; 根据所述目标配电网处于各个状态的概率和平均持续时间，求解配电网的最大供电能力优化模型，计算每个状态配电网的最大供电能力以及指定风险等级下所能提供的最大负荷。According to the probability and average duration of the target distribution network in each state, the maximum power supply capacity optimization model of the distribution network is solved, and the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under a specified risk level are calculated. 2.如权利要求1所述的考虑大规模分布式电源接入的配电网的可靠性分析方法，其特征在于，所述对所述基础参数数据进行预处理，并根据预处理结果建立第一模型包括：2. The reliability analysis method of a distribution network considering large-scale distributed power access according to claim 1, characterized in that the preprocessing of the basic parameter data and establishing a first model according to the preprocessing result comprises: 在给定一组观测序列，即历史基础参数数据的情况下，对不同状态的输出概率进行比较，最大概率即为该风机出力状态；Given a set of observation sequences, i.e. historical basic parameter data, the output probabilities of different states are compared, and the maximum probability is the output state of the wind turbine; 建立风机出力状态预测模型，首先对环境参数进行数据预处理，并进行模型训练，利用隐马尔科夫模型训练学习风机出力状态变化，从而生成多个子状态分类器，每个分类器都有自身的模型参数和最大概率值；To establish a prediction model for wind turbine output state, we first preprocess the environmental parameters and conduct model training. We use the hidden Markov model to learn the wind turbine output state changes, thereby generating multiple sub-state classifiers, each of which has its own model parameters and maximum probability value. 其次为状态评估，将训练好的隐马尔科夫模型作为状态评估的分类器，输入实时观测到的环境参数，计算出在该观测序列下最有可能的状态，即可完成分类，判断风机出力所处状态，实现对风机出力状态的评估；The second is state assessment. The trained hidden Markov model is used as a classifier for state assessment. The real-time observed environmental parameters are input and the most likely state under the observation sequence is calculated. This completes the classification, determines the state of the wind turbine output, and implements the evaluation of the wind turbine output state. 最后得到风机状态转移矩阵和观测矩阵，预测风机出力状态即输出功率，仿真结束，得到风电机组的出力序列。Finally, the wind turbine state transfer matrix and observation matrix are obtained, and the wind turbine output state, i.e., output power, is predicted. The simulation ends and the output sequence of the wind turbine is obtained. 3.如权利要求2所述的考虑大规模分布式电源接入的配电网的可靠性分析方法，其特征在于，所述对所述基础参数数据进行预处理，并根据预处理结果建立第一模型还包括：3. The reliability analysis method of a distribution network considering large-scale distributed power access according to claim 2, characterized in that the preprocessing of the basic parameter data and establishing the first model according to the preprocessing result further comprises: 主变压器、配电线路两状态可靠性模型通过组件运行状态概率表示，组件的运行状态概率pU和故障状态概率pD，计算公式如下：The two-state reliability model of the main transformer and distribution line is represented by the component operation state probability, the component operation state probability p U and the fault state probability p D , and the calculation formula is as follows: 其中，λ、μ分别表示组件的故障率和修复率。Among them, λ and μ represent the failure rate and repair rate of the component respectively. 4.如权利要求3所述的考虑大规模分布式电源接入的配电网的可靠性分析方法，其特征在于，所述结合目标配电网待分析区域的实时基础参数数据对所述第一模型进行仿真，获取目标配电网处于各个状态的概率和平均持续时间包括：4. The reliability analysis method of the distribution network considering large-scale distributed power access according to claim 3, characterized in that the simulation of the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed area to obtain the probability and average duration of the target distribution network in each state includes: 目标配电网处于各个状态的概率和平均持续时间表示为：The probability and average duration of the target distribution network in each state are expressed as: 系统状态s的发生概率p(s)计算公式如下：The probability p(s) of occurrence of system state s is calculated as follows: 其中，pz(s)表示组件z处于系统状态s的概率；N表示组件数量。Where p z (s) represents the probability that component z is in system state s; N represents the number of components. 5.如权利要求4所述的考虑大规模分布式电源接入的配电网的可靠性分析方法，其特征在于，所述结合目标配电网待分析区域的实时基础参数数据对所述第一模型进行仿真，获取目标配电网处于各个状态的概率和平均持续时间还包括：5. The reliability analysis method of the distribution network considering large-scale distributed power access according to claim 4, characterized in that the simulation of the first model in combination with the real-time basic parameter data of the target distribution network to be analyzed area to obtain the probability and average duration of the target distribution network in each state also includes: 系统状态的平均停留时间Td(s)由下式计算得到：The average residence time T d (s) of the system state is calculated by the following formula: 其中，若组件z处于运行状态，则λz表示故障率，否则表示修复率。Wherein, if component z is in operation, λ z represents the failure rate, otherwise it represents the repair rate. 6.如权利要求5所述的考虑大规模分布式电源接入的配电网的可靠性分析方法，其特征在于，所述配电网的最大供电能力优化模型包括：6. The reliability analysis method of a distribution network considering large-scale distributed power access according to claim 5, characterized in that the maximum power supply capacity optimization model of the distribution network includes: 配电网的最大供电能力优化模型表示为：The maximum power supply capacity optimization model of the distribution network is expressed as: 其中，RNi、Ti分别表示主变i的标称容量和负载量；DNI、βI分别表示分布式电源I的标称容量和容量因子；NT、NR分别表示主变压器和分布式电源的个数。Among them, R Ni and Ti represent the nominal capacity and load of the main transformer i respectively; D NI and β I represent the nominal capacity and capacity factor of the distributed power source I respectively; NT and NR represent the number of main transformers and distributed power sources respectively. 7.如权利要求6所述的考虑大规模分布式电源接入的配电网的可靠性分析方法，其特征在于，所述根据所述目标配电网处于各个状态的概率和平均持续时间，求解配电网的最大供电能力优化模型，计算每个状态配电网的最大供电能力以及指定风险等级下所能提供的最大负荷包括：7. The reliability analysis method of the distribution network considering large-scale distributed power access as claimed in claim 6 is characterized in that the maximum power supply capacity optimization model of the distribution network is solved according to the probability and average duration of the target distribution network in each state, and the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level are calculated, including: 其中，rpsci表示主变压器i的可靠供电能力；Td(k)表示系统状态k的平均持续时间；N表示系统状态数；Ωi为与主变i低压母线相连的分布式电源集，SI表示配电系统可靠供电风险等级指标，SIr表示配电系统可靠供电风险等级阈值。Where rpsci represents the reliable power supply capability of main transformer i; Td (k) represents the average duration of system state k; N represents the number of system states; Ωi represents the distributed power supply set connected to the low-voltage busbar of main transformer i; SI represents the reliable power supply risk level index of the distribution system; and SIr represents the reliable power supply risk level threshold of the distribution system. 8.一种考虑大规模分布式电源接入的配电网的可靠性分析系统，其特征在于，包括：8. A reliability analysis system for a distribution network considering large-scale distributed power access, characterized by comprising: 数据获取模块，用于获取目标配电网待分析区域的历史基础参数数据，所述历史基础参数数据至少包括分布式电源参数以及历史运行数据、历史气象数据、配电网结构以及互联馈线容量限制数据；A data acquisition module is used to acquire historical basic parameter data of the target distribution network area to be analyzed, wherein the historical basic parameter data at least includes distributed power supply parameters and historical operation data, historical meteorological data, distribution network structure and interconnected feeder capacity limitation data; 模型建立模块，用于对所述基础参数数据进行预处理，并根据预处理结果建立第一模型，所述第一模型包括基于隐马尔可夫模型的分布式电源出力状态的转移矩阵和观测矩阵以及主变压器、配电线路两状态可靠性模型；A model building module, used for preprocessing the basic parameter data and building a first model according to the preprocessing result, wherein the first model includes a transfer matrix and an observation matrix of the output state of the distributed power source based on a hidden Markov model and a two-state reliability model of a main transformer and a distribution line; 仿真求解模块，用于结合目标配电网待分析区域的实时基础参数数据对所述第一模型进行仿真，获取目标配电网处于各个状态的概率和平均持续时间；A simulation solution module, used to simulate the first model in combination with the real-time basic parameter data of the target distribution network area to be analyzed, and obtain the probability and average duration of the target distribution network in each state; 求解模块，用于根据所述目标配电网处于各个状态的概率和平均持续时间，求解配电网的最大供电能力优化模型，计算每个状态配电网的最大供电能力以及指定风险等级下所能提供的最大负荷。The solution module is used to solve the maximum power supply capacity optimization model of the distribution network according to the probability and average duration of the target distribution network in each state, and calculate the maximum power supply capacity of the distribution network in each state and the maximum load that can be provided under the specified risk level. 9.一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至7中任一项所述的方法的步骤。9. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program. 10.一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至7中任一项所述的方法的步骤。10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.
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