CN116644851A - Thermal power plant equipment control method and system combined with load optimization configuration - Google Patents

Abstract

The thermal power plant equipment control method and system combined with load optimization configuration belongs to the field of intelligent control, including: collecting and analyzing the user's historical power consumption and heating information, generating user thermal and electrical load; exchanging basic equipment information of thermal and electrical equipment, and constructing equipment operation Parameter mapping library; based on thermal and electrical loads, the fitting distribution of thermal load and electric load is performed, and the mapping library is called to calculate coal consumption; the control optimization of coal consumption is performed, and N optimization control schemes are retained; based on N types The scheme is selected to generate the selected control scheme; the equipment control of the thermoelectric equipment is carried out through the selected control scheme. This application solves the technical problem of low heating and electricity efficiency caused by the lack of comprehensiveness and accuracy of the control scheme in the prior art, realizes intelligent control and full life cycle management of thermal power plant equipment, optimizes equipment operation, reduces energy consumption, and improves equipment efficiency. Control the technical effects of comprehensiveness and efficiency.

Description

Thermal power plant equipment control method and system combined with load optimization configuration

technical field

The invention relates to the field of intelligent control, in particular to a thermal power plant equipment control method and system combined with load optimization configuration.

Background technique

Thermal power plants are an important part of my country's power and heating systems, and the rationality of their equipment control methods directly affects energy utilization efficiency and environmental protection levels. Traditional thermal power plant equipment control methods mainly rely on manual experience, and it is difficult to achieve high-precision monitoring, automatic optimal configuration, and intelligent control. The control scheme lacks comprehensiveness and accuracy, resulting in low control efficiency. Although the application of information technology and automatic control in thermal power plant equipment has gradually increased in recent years, most of the applications are concentrated in a certain control link, making it difficult to achieve comprehensive equipment monitoring and optimal control.

Contents of the invention

This application aims to solve the lack of comprehensive control schemes in the prior art by providing a thermal power plant equipment control method and system combined with load optimization configuration.

The lack of precision and precision lead to technical problems of inefficiency in heating and electricity.

In view of the above problems, the present application provides a thermal power plant equipment control method and system combined with load optimization configuration.

The first aspect disclosed in this application provides a thermal power plant equipment control method combined with load optimization configuration. The method includes collecting the user's historical power consumption and heat supply information, and analyzing based on the historical power consumption and heat supply information to generate the user's thermal power load; interact basic equipment information of thermoelectric equipment, and build equipment operation parameter mapping library according to the basic information, among which, the mapping parameters of equipment operation parameter mapping library include the mapping of coal consumption per unit time, electric load and heat load; The fitting distribution of load and electric load, and call the equipment operation parameter mapping library, and calculate the coal consumption through the formula; execute the control and optimization of coal consumption, and keep N kinds of optimal control schemes; save the N kinds of optimal control schemes The coal consumption of N optimal control schemes is used as the first reference data, and the equipment stability data of N optimal control schemes are used as the second reference data to select N optimal control schemes to generate the selected control scheme; by selecting the control scheme Perform device control for thermoelectric devices.

Another aspect disclosed by the present application provides a thermal power plant equipment control system combined with load optimization configuration. The system includes a user thermal and electrical load module, which is used to collect the user's historical power consumption and heat supply information, and based on the historical power consumption and heat supply information. Carry out analysis to generate user heat and power load; parameter mapping library construction module is used to exchange basic equipment information of thermal power equipment, and build equipment operation parameter mapping library according to basic information, wherein, the mapping parameters of equipment operation parameter mapping library include coal consumption per unit time Mapping with electric load and heat load; coal consumption calculation module 13, fitting distribution of heat load and electric load based on heat and electricity load, and calling equipment operation parameter mapping library, and calculating coal consumption through formula; optimization control scheme The module is used to execute the control optimization of coal consumption, and retains N kinds of optimal control schemes; the selected control scheme module is used to use the coal consumption of N kinds of optimal control schemes as the first reference data, and the N kinds of optimal control schemes The equipment stability data of the optimal control scheme is used as the second reference data to select N kinds of optimal control schemes to generate the selected control scheme; the thermoelectric equipment control module is used to control the equipment of the thermoelectric equipment through the selected control scheme .

One or more technical solutions provided in this application have at least the following technical effects or advantages:

Due to the analysis and generation of user thermoelectric load based on the user's historical power consumption and heating information, the user's actual demand can be controlled to meet the energy demand; the equipment operation parameter mapping library can be built to calculate the coal consumption according to the actual operation parameters of the thermal power equipment , to achieve accurate prediction of coal consumption; execute coal consumption control optimization, obtain multiple schemes, and realize scheme selection with a high degree of freedom; comprehensively consider factors such as coal consumption, equipment stability, and equipment call time to select a control scheme, Realize safe, stable and efficient scheme selection; the technical scheme for optimal control of thermal power plant equipment according to the control scheme solves the technical problem in the prior art that the efficiency of heating and power supply is low due to the lack of comprehensiveness and accuracy of the control scheme, and realizes the thermal power plant equipment Intelligent control and full life cycle management achieve the technical effects of optimizing equipment operation, reducing energy consumption, and improving equipment control comprehensiveness and efficiency.

The above description is only an overview of the technical solution of the present application. In order to better understand the technical means of the present application, it can be implemented according to the contents of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and understandable , the following specifically cites the specific implementation manner of the present application.

Description of drawings

FIG. 1 provides a schematic flow diagram of a possible flow chart of a thermal power plant equipment control method combined with load optimization configuration according to an embodiment of the present application;

FIG. 2 provides a schematic flow diagram of the possible flow of generating user thermal and electrical loads in the thermal power plant equipment control method combined with load optimization configuration provided by the embodiment of the present application;

FIG. 3 provides a schematic flow diagram of a possible flow chart for generating a selected control scheme in a thermal power plant equipment control method combined with load optimization configuration according to an embodiment of the present application;

FIG. 4 provides a schematic diagram of a possible structure of a thermal power plant equipment control system combined with load optimization configuration according to an embodiment of the present application.

Explanation of reference numerals: user thermoelectric load module 11 , parameter mapping library construction module 12 , coal consumption calculation module 13 , optimal control scheme module 14 , selected control scheme module 15 , thermoelectric equipment control module 16 .

Detailed ways

The general idea of the technical solution provided by this application is as follows:

The embodiment of the present application provides a thermal power plant equipment control method and system combined with load optimization configuration, which collects historical data to analyze the power load, generates coal consumption according to the equipment operation data, and maps the power load and coal consumption. The amount of coal is controlled and optimized, and various control schemes are obtained, and the control scheme is selected based on coal consumption and equipment stability to optimize the control of thermal power equipment. Achieve the technical effects of optimizing equipment operation, reducing energy consumption, and improving equipment control comprehensiveness and efficiency.

After introducing the basic principles of the present application, various non-limiting implementations of the present application will be specifically introduced below in conjunction with the accompanying drawings.

Embodiment one

As shown in Figure 1, the embodiment of the present application provides a thermal power plant equipment control method combined with load optimization configuration, the method includes:

Step S100: collect the user's historical power consumption and heat supply information, and analyze based on the historical power consumption and heat supply information to generate the user's thermoelectric load;

Specifically, the historical power consumption information refers to the power consumption data of the user in a certain period in the past, including information such as power consumption and power consumption time. Historical heat supply information refers to the heat supply data of thermal power plants in the corresponding historical period of historical electricity consumption, including information such as heat supply and heating time.

Based on historical power consumption and heat supply information, time series analysis, regression analysis, time series model and other methods are used for analysis. For example, the average power consumption and electricity consumption of the user's historical data are calculated as the thermal and electrical load parameters representing the user's basic energy consumption level; Extract the characteristic parameters of the user's historical load curve, such as peak value, valley value, magnification, area, etc., which represent the changing law of the user's thermal power demand; analyze the periodic change of the user's historical data, and extract the characteristic parameters representing the periodic change, such as daily cycle, weekly Cycle and annual cycle parameters; collect data such as user location, population, building type, etc., and calculate user energy density parameters based on historical energy consumption information, breaking through the limitation of single user data, and representing the energy consumption level of the same type of user groups in the region. Through in-depth analysis of historical power consumption and heat supply information, the user's heat and power load is obtained, which provides a data basis for optimizing the control of thermal power plant equipment.

Step S200: Interact basic equipment information of thermoelectric equipment, and build equipment operating parameter mapping library according to the basic information, wherein, the mapping parameters of the equipment operating parameter mapping library include the mapping of coal consumption, electric load and thermal load per unit time;

Specifically, first, collect the basic performance parameters of various thermoelectric equipment in the thermal power plant, such as capacity, efficiency, coal consumption coefficient, etc., and build the equipment basic information database. Then, according to the parameters in the equipment basic information database, the mapping relationship between the operating parameters of each equipment under different operating conditions and the coal consumption, generated electricity and heat is established to form the equipment operating parameter mapping library. The mapping parameters in the equipment operating parameter mapping library are the corresponding relationship between the theoretical coal consumption, electricity production and heat supply per unit time of the equipment under normal working conditions, according to the capacity, thermal efficiency, and mechanical efficiency of the equipment , heat transfer efficiency and other parameters were determined.

For example, the basic information of a generator set is: installed capacity is 50MW, mechanical efficiency is 98%, power generation efficiency is 42%, then the mapping parameters in its operating parameter mapping library can be set as: power - coal consumption - power generation , and the corresponding parameter values can be respectively: 40MW-120t/h-16.8MW, 45MW-135t/h-18.9MW, 50MW-150t/h-21.0MW.

The mapping relationship between the operating parameters of the equipment under different working conditions is deduced through the basic information of the equipment, and the equipment operating parameter mapping library is established, reflecting the theoretical coal consumption and production capacity of the equipment under different output conditions, in order to optimize the equipment The foundation of operational control is laid.

Step S300: Carry out fitting distribution of heat load and electric load based on the heat and electricity load, and call the equipment operating parameter mapping library, and calculate coal consumption through the formula, the calculation formula is as follows:

Among them, B is the total coal consumption, T is the running time of thermoelectric equipment, is the electrical load of the jth thermoelectric device at time t, /> is the thermal load of the jth thermoelectric device at time t, /> The thermal load of the jth thermoelectric device at time t is /> , the electric load is /> coal consumption, /> is the number of thermoelectric equipment running in the whole plant at time t;

Specifically, firstly, according to the heat and electricity load of the user, the thermal load and electric load are fitted and distributed, and the electricity and heat required to be produced by various generator sets and boilers are determined within a given period T, and the output target of each equipment is obtained and . Then, call the device operation parameter mapping library to find the output target of each device /> and /> Coal consumption parameters under corresponding working conditions/> . Finally, the coal consumption parameters of each equipment at different times Integral summation is carried out to calculate the total coal consumption B of the thermal power plant in a given period T.

For example, there are three thermal power plants with installed capacity of 45MW, 35MW, and 50MW respectively, and the coal consumption corresponding to the installed capacity is: B1=120t/h, B2=110t/h, B3=150t/h, if the three equipment Continuously operating at the power under the installed capacity, the coal consumption of the whole plant per hour =120+110+150=380t/h; the coal consumption of the whole plant at each moment/> Integrating on the time axis, the total coal consumption B in a given period T can be obtained. If the given period T is 10 hours, the total coal consumption of the 3 thermal power plant equipment under different working conditions per hour in the cycle is: /> =344t/h, /> =375t/h, /> =399t/h, /> =327t/h, /> =398t/h, /> =407t/h, /> =365t/h, /> =390t/h, /> =370t/h, /> =425t/h, the total coal consumption is 3800t by summing up the coal consumption in all time periods.

Fitting and distribution of thermal load and electrical load based on thermal and electrical load, and according to the equipment operating parameter mapping library, calculate the coal consumption of each equipment under different working conditions within a certain period, and provide data support for the thermal power plant to formulate a control plan, so as to realize the control of different Optimization is carried out under the thermal power plant equipment control scheme to improve the comprehensiveness and accuracy of thermal power plant equipment control.

Step S400: Execute coal consumption control optimization, and save N optimization control schemes;

Specifically, various control factors that affect coal consumption are collected according to the actual situation of the thermal power plant, and on the premise of satisfying the given thermal and electrical load of the user, by adjusting these control factors, a scheme that can minimize the actual coal consumption is found. For example, a thermal power plant has three generating units with different efficiencies, and the power allocation scheme will affect the total coal consumption. Then, under the condition of ensuring that the user load is met, the power allocation scheme of the generating units that minimizes the total coal consumption can be found through calculation . For another example, if a thermal power plant has two types of coal with different calorific values to choose from, it can be determined which type of coal to choose under different loads to reduce coal consumption through calculation.

First, according to the actual situation of the thermal power plant, collect all the control factors that affect coal consumption, such as the power and number of generator sets, boiler load, fan speed, output of furnace feeder, etc., and establish a mapping model between these factors and coal consumption. Secondly, according to the equipment characteristics and operating constraints, determine the adjustable range of each control factor, and provide limiting conditions for the subsequent optimization of the scheme. Then, using genetic algorithm, simulated annealing algorithm and other optimization algorithms, on the premise of satisfying the given output conditions, through calculation and iteration to find the control factor parameter scheme that can minimize the coal consumption predicted by the model. Then, the control scheme obtained by optimization is substituted into the coal consumption model of the thermal power plant for calculation, and it is verified that it can effectively reduce coal consumption. At the same time, a thermodynamic test is carried out to ensure that it meets various constraints for safe and stable operation. Finally, among all the verified control schemes, the N schemes with the smallest coal consumption are selected as the alternative schemes for the optimal operation of the thermal power plant, and stored in the database of the control system to provide important data support for subsequent scheme selection and adjustment.

Use the optimization algorithm to find various control schemes and their parameters that can effectively reduce the coal consumption of thermal power plants. These alternative schemes can achieve lower coal consumption, so that the control scheme is no longer a single selection scheme, and the control scheme is improved. Comprehensiveness, thereby improving the accuracy of the control scheme, thereby optimizing the operation of thermal power plant equipment, reducing energy consumption, and improving the energy conversion efficiency of thermal power plants.

Step S500: Using the coal consumption of the N optimal control schemes as the first reference data, and the equipment stability data of the N optimal control schemes as the second reference data, perform the N optimal control schemes Scheme selection of schemes to generate selected control schemes;

Specifically, the obtained N coal consumption optimization control schemes are selected according to the two standards of coal consumption and equipment stability, and a better optimal control scheme is selected as the selected control scheme. First, the theoretical coal consumption data corresponding to the N control schemes are used as the first reference data, indicating the minimum coal consumption that each scheme can achieve under the same output conditions, and the scheme with the smallest coal consumption is selected. Then, collect and analyze and calculate the equipment parameter data of N control schemes in thermodynamic inspection and simulation verification, in which the equipment parameter data includes outlet temperature, pressure, flow, etc. ) influence and stability, as the second reference data, select the scheme with the least influence and stability on the equipment. Finally, the first reference data and the second reference data are comprehensively judged, and a control scheme with excellent coal consumption and equipment stability is selected as the selected control scheme of the present invention. The selected scheme can complete the set output task with a relatively low coal consumption, and will not have a great impact on the stable operation of the equipment. plan.

Compared with the existing single scheme selection method, when selecting the control scheme, not only the economic index of coal consumption is considered, but also the safety factor of equipment stability is taken into consideration, and the control scheme is comprehensive and accurate by adopting multiple comprehensive judgment methods , to ensure the stability of the control scheme while improving the energy conversion efficiency.

Step S600: Perform device control of the thermoelectric device through the selected control scheme.

Specifically, the obtained selected control scheme is sent to the thermal power plant equipment control system for real-time adaptive regulation of thermal power equipment to realize automatic operation of the equipment. First, send the parameter setting values of each control factor in the selected control scheme, such as the power distribution coefficient of the generator set, the fan speed, the coal amount of the coal feeder, etc., to the corresponding equipment control system to guide the equipment to achieve the work required by the scheme state. Then, the thermal power plant detects various equipment operating parameters, such as outlet temperature, pressure, flow, etc., and feeds back the detected data to the control system. The control system adopts PID control algorithm, etc., and compares the feedback data with the set value of the selected control scheme in real time. Calculate the control deviation. Then, the control system sends control signals to each equipment control system according to the control deviation, such as incremental signals to adjust the working parameters of each equipment, such as valve opening, fan frequency, etc., and each equipment corrects the working parameters according to the control signals, so that the actual The working conditions gradually meet the requirements of the selected control scheme. Finally, through the above-mentioned closed-loop control, the actual working status of each device is gradually consistent with the selected control scheme. So far, the selected control scheme has been successfully applied to the automatic operation management of the thermal power plant to complete the online regulation and guide the efficient, low-carbon and stable operation of the thermal power plant.

By converting the given selected control scheme into automatic equipment control instructions online, and then intelligently adjust equipment, it can drive thermal power plant equipment to achieve optimal operation status more quickly and accurately, and can dynamically track load changes to ensure stable operation and realize optimized equipment. The technical effects of operation, reduced energy consumption, and improved equipment control comprehensiveness and efficiency.

Further, as shown in Figure 2, the embodiment of the present application also includes:

Step S110: Perform time-series trend analysis based on the historical power consumption and heat supply information, and obtain the basic thermoelectric load by fitting;

Step S120: Set the interception window, and read the window power consumption and heating information in the interception window;

Step S130: Set a fluctuation tolerance value for fluctuations according to the window electricity and heat supply information;

Step S140: Generate user thermoelectric load based on the fluctuation tolerance value and the basic thermoelectric load.

Specifically, the user's long-term historical electricity consumption and heat supply data are time-series analyzed to extract the main periodic change rules of the data, and the curve fitting method is used to obtain the basic heat and power load curve, which represents the user's basic heat and power demand level. Time series analysis can use techniques such as Fourier transform to analyze the periodic components of historical data, and extract the main changing laws such as daily cycle, weekly cycle and annual cycle. The fitting of the basic thermoelectric load curve can be realized by using Polynomial curve, exponential curve, cubic B-spline curve and other methods to obtain a smooth representative curve.

Set a reasonable time window, read the user's electricity consumption and heat supply historical data in the latest time window as the window power consumption and heat supply information, and the length of the time window depends on the data change frequency, which can be set to nearly 1 week, half-month or one-month data. Window power consumption and heat supply information includes the user's actual energy consumption data in the latest time window, which is closer to the user's current actual demand and reflects the user's current heat and power demand level and change trend. Calculate the change range of user energy consumption according to the window power consumption and heat supply information, and use it as the fluctuation tolerance value representing the user’s thermoelectric load change. The fluctuation tolerance value can be set as the standard deviation, variance or outlier limit of the window data, etc., for Measure the magnitude of changes in user energy consumption. The fluctuation tolerance value is inversely proportional to the window length, and the shorter the window, the greater the fluctuation tolerance value of the current user's energy consumption change.

The basic heat and electricity load represents the average level of users' long-term heat and electricity demand, and the fluctuation tolerance value represents the change range of users' energy demand in the near future. Thermal and electrical loads generate user thermal and electrical loads. For example, for the superposition method, the fluctuation tolerance value is superimposed on the basic thermoelectric load curve to generate a user thermoelectric load with a dynamic range. For example, if the basic thermoelectric load is 50MW and the fluctuation tolerance value is ±10%, then the user thermoelectric load Can be set to 45-55MW.

The user's thermoelectric load is generated through the fluctuation tolerance value and the basic thermoelectric load, which includes not only the parameters of the user's basic energy demand, but also the dynamic parameters of the energy consumption change in the near future, comprehensively and accurately reflecting the user's current and future thermoelectric demand in a certain period of time. The optimal control and scheduling of thermal power plants provide ideal basic input conditions, thereby improving the accuracy of subsequent control schemes.

Further, the embodiment of the present application also includes:

Step S510: reading the historical working data of the thermoelectric device;

Step S520: Divide the historical work data into equipment life cycle nodes to generate multi-node identifiers;

Step S530: Set the time decay coefficient, and calculate the real data value of the historical working data based on the time decay coefficient and the multi-node identification, and obtain the real value calculation result;

Step S540: Perform operation stability calculation according to the real value calculation result and the historical work data to obtain equipment stability data.

Specifically, read the historical operation data of key equipment in thermal power plants from the historical database, such as process parameter data such as valve opening, water supply, bearing temperature, inlet temperature and pressure, etc. These data include the long-term operation of the equipment. Work information reflects the dynamic changes of equipment operating status. According to the theoretical life cycle of the equipment, the historical operation data is divided into different stages, such as the early run-in period, the middle period of run-in period, the late run-in period, the early stage of stability, the middle period of stability, the late period of stability, and the frequent failure period. These stages correspond to the performance and stability changes of the device, and the current operating stage of the device can be accurately judged according to the divided device life cycle. For the division of equipment life cycle, technologies such as expert judgment method and cluster analysis method are used to obtain multi-node identification according to equipment life cycle division.

Due to the long time span of historical work data collection, the performance and status of equipment will change during this process. Directly using historical work data for analysis will introduce a large deviation. In order to reduce this deviation, it is necessary to consider the timeliness of the data. Set The time decay coefficient weights the data of different time periods, and calculates the real value of the data reflecting the current equipment performance. First, determine the time decay function according to the period of the thermal power plant equipment, such as an exponential function, etc., then determine the relative change and decay rate of the importance of data in different periods according to the data change, and calculate the weight of the data in different periods. Among them, the historical data time The closer it is, the more weight it is given. Finally, according to the multi-node identification, set the weight obtained according to the time decay coefficient for the historical working data in different periods, and calculate the real value of the data to obtain the real value calculation result.

After obtaining the real value calculation result after time decay processing, the current operating status of the device can be accurately judged. Using Markov process and other technologies combined with real value calculation results and historical work data to analyze equipment failure rate, work fluctuation and other indicators, and further calculate the operation stability of the equipment, to provide judgment basis for the subsequent selection of equipment control schemes.

Further, the embodiment of the present application also includes:

Step S541: Make an abnormal call based on the historical work data, and record the fault frequency, fault value, operating fluctuation frequency, and operating fluctuation value, wherein the fault value is a representative value of the severity of the fault, and the operating fluctuation value is Characteristic value of stable output for power generation or heat supply;

Step S542: Perform real feature adjustment of the fault frequency, the fault value, the operating fluctuation frequency, and the operating fluctuation value based on the actual value calculation result;

Step S543: Calculate the operation stability of the equipment according to the adjusted fault frequency, the fault value, the operating fluctuation frequency, and the operating fluctuation value to obtain equipment stability data.

Specifically, according to the historical working data of the thermoelectric equipment, various faults and work fluctuation events encountered by the equipment can be detected, and called abnormally. By analyzing historical work data, statistics and records indicators such as failure frequency, failure value, work fluctuation frequency and work fluctuation value. Among them, the fault value indicates the severity of the fault event, which can be quantitatively described by indicators such as downtime or economic loss; the work fluctuation value indicates the fluctuation degree of equipment power generation or heating output, which can be described by indicators such as the standard deviation of output power or temperature.

The obtained calculation result of the real value of the time attenuation coefficient can reflect the real working state of the equipment in the current period. According to the calculation result of the true value, weighted summation or probability statistics are used to correct the recorded indicators such as fault frequency, fault value, operating fluctuation frequency and operating fluctuation value, so as to reduce the error caused by time changes and obtain the results of these indicators. real eigenvalues. After obtaining the true characteristics of the failure index and the operating fluctuation index, the operation stability of the equipment can be accurately evaluated, and then the Markov process or gray correlation analysis and other technologies can be used to calculate the equipment based on the fault frequency, fault value, operating fluctuation frequency and operating fluctuation value The operation stability of the equipment is used, and the calculation results are used as equipment stability data to provide a basis for the selection of equipment control schemes and further improve the accuracy of the control schemes.

Further, as shown in Figure 3, the embodiment of the present application also includes:

Step S550: setting a duration threshold based on the user's thermoelectric load;

Step S560: judging whether the call durations of thermoelectric devices in the N optimal control schemes all meet the duration threshold;

Step S570: When there is a thermoelectric device that cannot meet the duration threshold for the thermoelectric device calling time, perform an abnormal calling identification of the thermoelectric device corresponding to the optimization control scheme;

Step S580: Use the abnormal call identification result as the third reference data, select the N optimization control schemes according to the first reference data, the second reference data and the third reference data, and generate a selection Set up a control plan.

Specifically, user thermoelectric load reflects the power and heat demand at the user end, and its changing trend and magnitude directly affect the invocation of thermoelectric equipment. According to the data statistical characteristics of the user's thermoelectric load, set the upper limit value of the thermoelectric equipment call time, which is the duration threshold. At the same time, the setting of the duration threshold should meet the basic needs of the user's thermoelectric load without causing the equipment to overload.

The remaining N optimization control schemes correspond to different call durations of thermoelectric equipment by performing coal consumption control optimization. Judgment is made on N optimization control schemes. If the device call time in a certain scheme exceeds the set duration threshold, it indicates that the scheme is not feasible. If it is judged that a thermoelectric device with a call time exceeding the threshold in a certain plan is found, it is necessary to use Boolean variables or nomenclature to identify the abnormal call of the plan and related devices, and exclude the abnormal plan in the subsequent plan screening.

The first reference data obtained according to coal consumption and the second reference data obtained according to equipment stability are used to evaluate the economy and safety of each plan, and the third reference data obtained according to the time threshold is used to judge the feasibility of the plan . Synthesize the three types of reference data, use technologies such as fuzzy evaluation method or trade-off method to screen and evaluate N kinds of optimal control schemes, select an economical, safe and feasible selected control scheme, and improve the accuracy and control of equipment control schemes quality.

Further, the embodiment of the present application also includes:

Step S581: setting the short-time start-up correlation coefficient of each thermoelectric device based on the basic information of the device;

Step S582: Set an incremental correlation value, calculate the influence value of the abnormal call according to the device abnormal call identifier, the short-time start correlation coefficient and the incremental correlation value, and generate the third reference data according to the calculation result of the influence value .

Specifically, the start-up process of thermoelectric equipment often needs to consume a large amount of start-up energy, and frequent start-up and stop will bring a large burden to the equipment and affect its service life. According to the basic parameters such as the capacity and power of the thermal power equipment, set the maximum number of times that each thermal power plant equipment is allowed to start in a short time, that is, the short-term start-up correlation coefficient. Among them, the short-time start-up correlation coefficient of the equipment with larger capacity should be generally set get smaller.

First, set an incremental correlation value, which represents a coefficient that gradually increases the resistance of the device to subsequent startups during continuous use. Then, combined with the obtained abnormal call identifier and the set short-time startup correlation coefficient, the influence value of the abnormal call of the equipment in each scheme is calculated. The greater the impact value, the more serious the negative impact of the optimal control scheme on the equipment is. Finally, the calculation result of the influence value of each scheme is used as the third reference data, which is used as the basis for generating the selected control scheme, and is used to select the scheme with a small influence value on the equipment after the control scheme is implemented, so as to avoid excessive load on the equipment .

By considering the start-up correlation of thermoelectric equipment, it is possible to accurately evaluate the potential impact of different control schemes on the equipment, avoid selecting the scheme that will accelerate equipment degradation among the N optimal control schemes, and provide a basis for judging the optimal selection of equipment control schemes, which is conducive to improving Improve the safety and utilization efficiency of equipment, and improve the comprehensive control of thermal power plant equipment.

Further, the embodiment of the present application also includes:

Step S710: Analyzing equipment anomalies based on equipment control results and preset fitting results;

Step S720: performing abnormal clustering on the equipment abnormality analysis results;

Step S730: Generate maintenance feedback information through abnormal clustering results;

Step S740: Perform maintenance of the thermoelectric device through the maintenance feedback information.

Specifically, the device control result refers to the actual working state of the thermoelectric device after being controlled by the selected control scheme, and the preset fitting result represents the expected working state of the thermoelectric device after being controlled according to the selected control scheme. State estimation, data mining and other technical means are used to compare and analyze the equipment control results and preset fitting results, and detect abnormalities in the working process of the equipment, such as excessive temperature, excessive vibration, and low output power.

Equipment abnormality analysis will detect multiple abnormal parameters or indicators, and methods such as K-means clustering, hierarchical clustering, and DBSCAN clustering need to be used to classify and aggregate these abnormalities to generate abnormal categories. Abnormal clustering is to classify abnormal situations according to abnormal causes or effects, and provide reference for subsequent maintenance plan formulation. After obtaining the abnormal clustering results, determine the root cause of the abnormality of the thermoelectric equipment, and generate maintenance feedback information corresponding to the abnormal category to guide equipment maintenance. Among them, the maintenance feedback information should include abnormal parts, abnormal cause analysis and maintenance suggestions. Then, according to the maintenance feedback information, operations such as detecting and locating the abnormal equipment, replacing parts or adjusting parameters are performed to eliminate the abnormality and ensure that the thermoelectric equipment returns to normal working state.

Through the analysis of the difference between the equipment control results and the preset fitting results, it is possible to accurately detect equipment abnormalities, and provide refined and targeted decision-making basis for equipment maintenance through abnormal clustering and maintenance feedback, which can minimize Reduce equipment downtime, effectively improve equipment efficiency and operational stability.

In summary, the thermal power plant equipment control method combined with load optimization configuration provided by the embodiment of the present application has the following technical effects:

Collect the user's historical power consumption and heat supply information, and analyze based on the historical power consumption and heat supply information to generate the user's thermoelectric load, which can accurately predict the user's thermoelectric load and provide an important reference for subsequent equipment operation management. Interact basic equipment information of thermoelectric equipment, build equipment operation parameter mapping library according to basic information, can provide data support for the calculation of equipment coal consumption under different loads, and improve the comprehensiveness and accuracy of subsequent control schemes; heat load based on thermoelectric load Fitting and distribution of electrical loads, calling the equipment operating parameter mapping library, and calculating coal consumption through formulas, the corresponding total coal consumption of equipment can be accurately calculated according to the thermal and electrical loads in different situations, providing a basis for the subsequent accurate generation of control schemes Data support; implement coal consumption control optimization, and retain N kinds of optimal control schemes, can obtain a variety of control schemes with the lowest coal consumption, avoid single selection of control schemes, and enrich control schemes; combine N kinds of optimal control schemes The coal consumption of the scheme is used as the first reference data, and the equipment stability data of the N optimal control schemes are used as the second reference data to select the N optimal control schemes and generate the selected control scheme, which is the control scheme. Select and limit various limiting factors to improve the rationality and accuracy of the control scheme; through the selection of the control scheme for equipment control of thermal power equipment, the coal consumption can be minimized while ensuring the stable operation of the equipment, so as to optimize the operation of the equipment, The technical effect of reducing energy consumption and improving equipment control comprehensiveness and efficiency.

Embodiment two

Based on the same inventive concept as the thermal power plant equipment control method combined with load optimization configuration in the foregoing embodiments, as shown in FIG. 4 , the embodiment of the present application provides a thermal power plant equipment control system combined with load optimization configuration. The control system includes:

The user thermoelectric load module 11 is used to collect the user's historical power consumption and heat supply information, and analyze based on the historical power consumption and heat supply information to generate the user thermoelectric load;

The parameter mapping library construction module 12 is used for exchanging basic equipment information of thermoelectric equipment, and constructing a equipment operating parameter mapping library according to the basic information, wherein, the mapping parameters of the equipment operating parameter mapping library include coal consumption and electric load per unit time and heat load mapping;

The coal consumption calculation module 13 performs fitting distribution of thermal load and electric load based on the thermal and electrical load, and calls the equipment operation parameter mapping library, and performs coal consumption calculation through a formula, and the calculation formula is as follows:

Among them, B is the total coal consumption, T is the running time of thermoelectric equipment, is the electrical load of the jth thermoelectric device at time t, /> is the thermal load of the jth thermoelectric device at time t, /> The thermal load of the jth thermoelectric device at time t is /> , the electric load is /> coal consumption, /> is the number of thermoelectric equipment running in the whole plant at time t;

The optimization control scheme module 14 is used to perform control optimization of coal consumption, and retain N kinds of optimization control schemes;

The selected control scheme module 15 is used to use the coal consumption of the N kinds of optimal control schemes as the first reference data and the equipment stability data of the N kinds of optimal control schemes as the second reference data to carry out the Describe the scheme selection of N kinds of optimal control schemes, and generate the selected control scheme;

The thermoelectric device control module 16 is configured to perform device control of the thermoelectric device through the selected control scheme.

Further, the embodiment of the present application also includes:

The basic thermoelectric load module performs time-series trend analysis based on the historical power consumption and heat supply information, and obtains the basic thermoelectric load by fitting;

The interception window module is used to set the interception window and read the window power consumption and heating information in the interception window;

A fluctuation tolerance value module, configured to set a fluctuating fluctuation tolerance value according to the window electricity and heat supply information;

A user thermoelectric load module is configured to generate a user thermoelectric load based on the fluctuation tolerance value and the basic thermoelectric load.

Further, the embodiment of the present application also includes:

A historical working data module, configured to read historical working data of the thermoelectric device;

A multi-node identification module, configured to divide the historical work data into nodes in the equipment life cycle to generate a multi-node identification;

A real value calculation module, configured to set a time decay coefficient, perform data real value calculation of the historical working data based on the time decay coefficient and the multi-node identification, and obtain a real value calculation result;

The equipment stability data module is used to perform operation stability calculation according to the real value calculation result and the historical work data to obtain equipment stability data.

Further, the embodiment of the present application also includes:

The work failure recording module is used to make an abnormal call through the historical work data, and record the failure frequency, failure value, work fluctuation frequency, and work fluctuation value, wherein the failure value is a representative value of the severity of the failure, and the The working fluctuation value is a stable output characteristic value for power generation or heat supply;

A real feature adjustment module, configured to adjust the real feature of the fault frequency, the fault value, the operating fluctuation frequency, and the operating fluctuation value based on the actual value calculation results;

The stability data acquisition module is used to calculate the operation stability of the equipment according to the adjusted fault frequency, the fault value, the operating fluctuation frequency, and the operating fluctuation value, and obtain equipment stability data.

Further, the embodiment of the present application also includes:

A duration threshold setting module, configured to set a duration threshold based on the user's thermoelectric load;

A control scheme judging module, configured to judge whether the call durations of thermoelectric devices in the N optimal control schemes all meet the duration threshold;

The abnormal call identification module is used to identify the abnormal call of the thermoelectric device corresponding to the optimization control scheme when there is a thermoelectric device that cannot meet the duration threshold for the call time of the thermoelectric device;

The selected control scheme module is used to use the abnormal call identification result as the third reference data, and perform the N optimization control schemes according to the first reference data, the second reference data and the third reference data Scenario selection to generate the selected control scenario.

Further, the embodiment of the present application also includes:

Start the correlation coefficient module, set the short-term start correlation coefficient of each thermoelectric device based on the basic information of the equipment;

The third reference data module is used to set the incremental correlation value, calculate the influence value of the abnormal call according to the abnormal call identification of the device, the short-time startup correlation coefficient and the incremental correlation value, and generate the abnormal call according to the calculation result of the influence value. The third reference data.

Further, the embodiment of the present application also includes:

Equipment abnormal analysis module, based on equipment control results and preset fitting results for equipment abnormal analysis;

Abnormal clustering module, used for abnormal clustering of equipment abnormal analysis results;

A maintenance feedback information module, configured to generate maintenance feedback information through abnormal clustering results;

The inspection and maintenance module is used to perform inspection and maintenance of the thermoelectric equipment through the inspection feedback information.

In summary, any step of the method described above can be stored in an unlimited computer memory as a computer instruction or program, and can be called and identified by an unlimited computer processor to implement any one of the embodiments of the present application method, no redundant restrictions are made here.

Further, the above-mentioned first or second may not only represent a sequence relationship, but may also represent a specific concept, and/or refer to individual or all selection among multiple elements. Apparently, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the application belong to the scope of the application and its equivalent technology, the application intends to include these modifications and variations.

Claims (8)

1. The thermal power plant equipment control method in conjunction with load optimization configuration, it is characterized in that, described method comprises: Collect the user's historical power consumption and heat supply information, and analyze based on the historical power consumption and heat supply information to generate the user's thermoelectric load; Interacting basic equipment information of thermoelectric equipment, constructing equipment operation parameter mapping library according to the basic information, wherein, the mapping parameters of the equipment operation parameter mapping library include the mapping of coal consumption per unit time and electric load and heat load; Based on the thermoelectric load, the fitting distribution of thermal load and electrical load is carried out, and the operation parameter mapping library of the equipment is called, and the coal consumption is calculated by the formula. The calculation formula is as follows: Among them, B is the total coal consumption, T is the running time of thermoelectric equipment, /> is the electrical load of the jth thermoelectric device at time t, /> is the thermal load of the jth thermoelectric device at time t, The thermal load of the jth thermoelectric device at time t is /> , the electric load is /> coal consumption, is the number of thermoelectric equipment running in the whole plant at time t; Execute the control optimization of coal consumption, and retain N kinds of optimal control schemes; The coal consumption of the N kinds of optimal control schemes is used as the first reference data, and the equipment stability data of the N kinds of optimal control schemes is used as the second reference data, and the schemes of the N kinds of optimal control schemes are carried out Select to generate the selected control scheme; Device control of the thermoelectric device is performed by the selected control scheme. 2. The method of claim 1, further comprising: Perform time series trend analysis based on the historical power consumption and heat supply information, and obtain the basic thermoelectric load by fitting; Set the interception window, and read the window power consumption and heating information in the interception window; Set a fluctuation tolerance value for fluctuations according to the electricity consumption and heating information of the window; A user thermoelectric load is generated based on the fluctuation tolerance value and the basic thermoelectric load. 3. The method of claim 1, further comprising: Reading the historical working data of the thermoelectric device; Carrying out device life cycle node division on the historical work data to generate multi-node identifiers; Setting a time decay coefficient, performing data real value calculation of the historical working data based on the time decay coefficient and the multi-node identification, and obtaining a real value calculation result; Perform operation stability calculation according to the actual value calculation result and the historical work data to obtain equipment stability data. 4. The method of claim 3, further comprising: Abnormal calls are made through the historical work data, and the fault frequency, fault value, operating fluctuation frequency, and operating fluctuation value are recorded, wherein the fault value is a representative value of the severity of the fault, and the operating fluctuation value is for power generation or Characteristic value of stable output of heat supply; Real feature adjustment of the fault frequency, the fault value, the operating fluctuation frequency, and the operating fluctuation value is performed through the actual value calculation result; The operation stability of the equipment is calculated according to the adjusted fault frequency, the fault value, the operating fluctuation frequency, and the operating fluctuation value to obtain equipment stability data. 5. The method of claim 1, further comprising: Setting a duration threshold based on the user's thermoelectric load; Judging whether the call durations of thermoelectric devices in the N optimal control schemes all meet the duration threshold; When there is a thermoelectric device that cannot meet the duration threshold during the thermoelectric device calling time, the abnormal call identification of the thermoelectric device corresponding to the optimization control scheme is carried out; Taking the abnormal call identification result as the third reference data, performing the selection of the N optimization control schemes according to the first reference data, the second reference data and the third reference data, and generating the selected control scheme . 6. The method of claim 5, further comprising: Setting the short-time start-up correlation coefficient of each thermoelectric device based on the basic information of the device; Set an incremental correlation value, calculate the impact value of the abnormal call according to the device abnormal call identifier, the short-time startup correlation coefficient, and the incremental correlation value, and generate the third reference data according to the calculation result of the impact value. 7. The method of claim 1, further comprising: Analyze equipment anomalies based on equipment control results and preset fitting results; Abnormal clustering of equipment abnormal analysis results; Generate maintenance feedback information through abnormal clustering results; The inspection and maintenance of the thermoelectric device is performed through the inspection feedback information. 8. A thermal power plant equipment control system combined with load optimization configuration, characterized in that the system includes: A user thermoelectric load module, the user thermoelectric load module is used to collect the user's historical power consumption and heat supply information, and analyze based on the historical power consumption and heat supply information to generate the user thermoelectric load; A parameter mapping library construction module, the parameter mapping library construction module is used to interact with basic equipment information of thermoelectric equipment, and construct a equipment operation parameter mapping library according to the basic information, wherein, the mapping parameters of the equipment operation parameter mapping library include unit time Mapping of internal coal consumption and electrical and thermal loads; Coal consumption calculation module, the coal consumption calculation module performs fitting distribution of thermal load and electric load based on the thermal and electrical load, and calls the equipment operating parameter mapping library to calculate coal consumption through the formula, the calculation formula is as follows : Among them, B is the total coal consumption, T is the running time of thermoelectric equipment, /> is the electrical load of the jth thermoelectric device at time t, /> is the thermal load of the jth thermoelectric device at time t, The thermal load of the jth thermoelectric device at time t is /> , the electric load is /> coal consumption, is the number of thermoelectric equipment running in the whole plant at time t; An optimization control scheme module, the optimization control scheme module is used to perform coal consumption control optimization, and retain N kinds of optimization control schemes; A selected control scheme module, the selected control scheme module is used to use the coal consumption of the N optimal control schemes as the first reference data, and use the equipment stability data of the N optimal control schemes as the second 2. Refer to the data, carry out the scheme selection of the N kinds of optimal control schemes, and generate the selected control scheme; A thermoelectric device control module for device control of the thermoelectric device via the selected control scheme.