CN110633870A - A harmonic early warning method, harmonic early warning device and terminal equipment - Google Patents

Abstract

This application is applicable to the field of electric power technology, and provides a harmonic early warning method, a harmonic early warning device, terminal equipment, and a computer-readable storage medium. The harmonic early warning method includes: obtaining training samples, and the training samples are harmonic Wave pollution is the electrical signal of the label; based on the training samples, the convolutional neural network model is trained to obtain the trained neural network model; the collected electrical signal in the user-side power grid to be monitored is input into the trained neural network A model for obtaining the harmonic pollution degree in the user-side power grid; and performing an early warning action on the user-side power grid based on the harmonic pollution degree. The application can realize the early warning of the harmonics of the power grid, and is beneficial to reduce the influence of the harmonics on the power grid.

Description

A harmonic early warning method, harmonic early warning device and terminal equipment

technical field

The application belongs to the field of electric power technology, and in particular relates to a harmonic early warning method, a harmonic early warning device, terminal equipment and a computer-readable storage medium.

Background technique

With the development of my country's power grid technology, power electronic equipment is widely used in various fields, which brings great convenience to people's lives. The development of frequency conversion technology has brought great pollution to the power grid. High-order harmonic current and harmonic voltage have a great impact on the grid voltage, and even affect the stable operation of the entire power grid.

In order to control the harmonics of the power grid well, there is an urgent need for a method that can realize early warning of the harmonics of the power grid to reduce the impact of the harmonics on the power grid.

Contents of the invention

In view of this, an embodiment of the present application provides a harmonic early warning method, a harmonic early warning device, a terminal device, and a computer-readable storage medium, so as to realize early warning of harmonics in a power grid and reduce the impact of harmonics on the power grid.

The first aspect of the embodiments of the present application provides a harmonic early warning method, including:

Obtain a training sample, the training sample is an electrical signal labeled with a harmonic pollution degree;

Train the constructed convolutional neural network model based on the training samples to obtain the trained neural network model;

Inputting the collected electrical signals in the user-side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid;

An early warning action is performed on the user-side power grid based on the harmonic pollution degree.

The second aspect of the embodiment of the present application provides a harmonic early warning device, including:

An acquisition unit, configured to acquire a training sample, the training sample being an electrical signal labeled with a harmonic pollution degree;

The training unit is used to train the convolutional neural network model based on the training samples to obtain the trained neural network model;

A model analysis unit, configured to input the collected electrical signals in the user-side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid;

The early warning unit is configured to perform an early warning action on the user-side power grid based on the harmonic pollution degree.

The third aspect of the embodiments of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program The steps of the harmonic early warning method provided in the first aspect of the embodiment of the present application are realized.

The fourth aspect of the embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by one or more processors, the first embodiment of the present application is implemented. The steps of the harmonic early warning method provided by the aspect.

The fifth aspect of the embodiment of the present application provides a computer program product, the computer program product includes a computer program, and when the computer program is executed by one or more processors, it implements the described method steps.

The embodiment of the present application provides a harmonic early warning method. By obtaining training samples, the training samples are electrical signals labeled with harmonic pollution degree; based on the training samples, the constructed convolutional neural network model is trained to obtain the training After the neural network model; the electrical signal in the user-side power grid to be monitored is input into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid; based on the harmonic pollution degree, the The power grid at the user side performs early warning actions; thus, it is possible to realize early warning of harmonics in the power grid and help reduce the impact of harmonics on the power grid.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the accompanying drawings that need to be used in the descriptions of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are only for the present application For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without paying creative efforts.

Fig. 1 is a kind of implementation flow schematic diagram of the harmonic early warning method that the embodiment of the present application provides;

Fig. 2 is a kind of realization flowchart of obtaining training samples in the harmonic early warning method provided by the embodiment of the present application;

FIG. 3 is a schematic block diagram of a harmonic warning device provided in an embodiment of the present application;

Fig. 4 is a schematic block diagram of a terminal device provided by an embodiment of the present application.

Detailed ways

In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and/or components, but does not exclude one or more other features. , whole, step, operation, element, component and/or the presence or addition of a collection thereof.

It should also be understood that the terminology used in the specification of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural referents unless the context clearly dictates otherwise.

It should also be further understood that the term "and/or" used in the description of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations .

As used in this specification and the appended claims, the term "if" may be construed as "when" or "once" or "in response to determining" or "in response to detecting" depending on the context . Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be construed, depending on the context, to mean "once determined" or "in response to the determination" or "once detected [the described condition or event] ]” or “in response to detection of [described condition or event]”.

In order to illustrate the technical solutions described in this application, specific examples are used below to illustrate.

Fig. 1 is a schematic diagram of the implementation process of a harmonic early warning method provided by the embodiment of the present application. As shown in the figure, the method may include the following steps:

In step 101, a training sample is obtained, and the training sample is an electrical signal labeled with a harmonic pollution degree;

In the embodiment of the present application, it is preferred to obtain training samples composed of electrical signals labeled with harmonic pollution degrees. For example, the training samples may include data corresponding to various electrical signals, and the data corresponding to each electrical signal has a label. This label indicates the degree of harmonic pollution corresponding to the corresponding electrical signal. The training sample is used to train the pre-built neural network model, which can make the pre-built neural network model have the function of receiving the data of the corresponding electrical signal and outputting the harmonic pollution label of the corresponding electrical signal based on the training and learning of the training sample .

Optionally, in an implementation manner, as shown in Figure 2, the acquisition of training samples above can be implemented based on the following steps:

Step 201, randomly generating a virtual electrical signal, and extracting characteristic parameters of the virtual electrical signal;

Step 202, input the virtual electrical signal into a pre-built virtual grid model to obtain the state of the virtual grid model;

Step 203, calculating the harmonic pollution degree of the virtual electrical signal based on the state of the virtual grid model;

Step 204: Obtain a preset number of virtual electrical signals and corresponding harmonic pollution degrees, and use the characteristic parameters of each virtual electrical signal and the corresponding harmonic pollution degrees as training samples.

In the embodiment of the present application, a virtual electrical signal can be randomly generated first, and the characteristic parameters of the virtual electrical signal can be extracted; and the corresponding virtual electrical signal can be input into a pre-built virtual grid model, which can be a reference to the actual grid The model obtained by simulation, by receiving the above-mentioned virtual electrical signal, the virtual power grid model will present different states, such as the action state of devices such as circuit breakers and transformers in the virtual power grid model. According to the state presented by the virtual grid model, the harmonic pollution degree of the current virtual grid model can be calculated. The harmonic pollution degree corresponds to the input virtual electrical signal. The harmonic pollution degree can be labeled as a label and related to the corresponding virtual electrical signal. The characteristic parameters of the training data constitute a training data in the training sample. In the same way, multiple virtual electrical signals can be generated to obtain multiple training data to form a training sample together.

Optionally, in an implementation manner, the above step 201 may include:

Step 2011, obtaining a preset fundamental wave, and randomly generating various harmonics on the fundamental wave;

Step 2012, superimposing the fundamental wave and randomly generated harmonics to obtain a virtual electrical signal.

In the embodiment of the present application, the virtual electrical signal can be obtained by superimposing randomly generated harmonics on the fundamental wave according to the preset fundamental wave, which eliminates the need to obtain electrical signal training data from the actual power grid and saves costs And it is more convenient, and at the same time, it can also diversify the training samples by controlling the superposition of different types of harmonics, which is conducive to improving the prediction accuracy of the neural network model.

Optionally, in an implementation manner, the above-mentioned characteristic parameters of the virtual electrical signal may include at least one of the following: the frequency of the fundamental wave, the frequency of each harmonic, the amplitude of each harmonic, and the phase angle of each harmonic , 2-50 harmonic voltage and harmonic current, three-phase voltage unbalance, voltage fluctuation and flicker, voltage deviation, voltage fundamental RMS and true RMS, current fundamental RMS and true RMS, fundamental wave active power, fundamental wave apparent power, true power factor.

Optionally, in an implementation manner, the state of the virtual power grid model may include at least one of the following: whether there is a circuit breaker malfunction, whether there is a transformer overheating, whether there is a motor burnout, whether there is an automatic device malfunction, the virtual The power quality received by each electrical equipment in the grid model.

In step 102, the convolutional neural network model constructed based on the training samples is trained to obtain the trained neural network model;

In the embodiment of the present application, the training samples obtained above are used to train the pre-built convolutional neural network model, so that the pre-built neural network model has the data of receiving corresponding electrical signals and output corresponding The function of the harmonic pollution label of the electrical signal is to obtain the trained neural network model.

For the pre-built Convolutional Neural Networks (CNN), it is a kind of feed-forward neural network that includes convolution calculations and has a deep structure. Variation classification, so it is also called "translation invariant artificial neural network. Convolutional neural network imitates the biological visual perception mechanism construction, and can perform supervised learning and unsupervised learning. The convolution kernel parameters in the hidden layer are shared and layered The sparsity of inter-connections enables convolutional neural networks to grid features with a small amount of computation.

In the embodiment of the present application, the pre-built convolutional neural network may include an input layer, a hidden layer and an output layer, and its input layer may process multi-dimensional data, for example, it may receive and process multi-dimensional characteristic parameters corresponding to the above electrical signals. Its hidden layer can include convolutional layer, pooling layer and fully connected layer, that is, the hierarchical order of data processing in this pre-built convolutional neural network can be: input-convolutional layer-pooling layer-full Connection layer - output.

Among them, the function of the convolutional layer is to perform feature extraction on the input data, which can contain multiple convolution kernels, and each element that makes up the convolution kernel corresponds to a weight coefficient and a bias, similar to a feedforward neural network of neurons. Each neuron in the convolutional layer is connected to multiple neurons in the adjacent area in the previous layer. The size of the area depends on the size of the convolution kernel, which is called the "receptive field". When the convolution kernel is working, it will regularly scan the input features, perform matrix element multiplication and summation on the input features in the receptive field, and superimpose the deviation. After feature extraction in the convolutional layer, the output feature map is passed to the pooling layer for feature selection and information filtering. The pooling layer contains a preset pooling function, whose function is to replace the result of a single point in the feature map with the feature map statistics of its adjacent regions. The pooling layer selects the pooling area in the same step as the convolution kernel scanning feature map, controlled by the pooling size, step size, and padding. The fully connected layer is equivalent to the hidden layer in the traditional feedforward neural network. The fully connected layer is located at the last part of the hidden layer of the convolutional neural network and only passes signals to other fully connected layers. Feature maps lose their spatial topology in fully connected layers, are expanded into vectors and passed through activation functions.

In the embodiment of the present application, the structure and working principle of the output layer of the pre-built convolutional neural network are the same as the output layer of the traditional feedforward neural network. For example, for the harmonic pollution classification problem of the user-side power grid, the output layer can use a logistic function or a normalized exponential function to output classification labels.

Optionally, the above-mentioned training of the constructed convolutional neural network model based on the training samples, and obtaining the trained neural network model may include:

Step 1021, input the characteristic parameters of the virtual electrical signal into the constructed convolutional neural network model to obtain the predicted value of harmonic pollution;

Step 1022, reversely update the parameters of each layer of the convolutional neural network model based on the difference between the predicted harmonic pollution value and the label value;

Step 1023, after the convolutional neural network model converges, obtain a trained convolutional neural network model.

In the embodiment of the present application, the pre-built convolutional neural network model is trained by using the training data, that is, the supervised learning is performed through the back propagation algorithm, so that the pre-built convolutional neural network model can be learned into the virtual circuit in the training sample. The law between the characteristic parameters of the signal and its label value (harmonic pollution), and then has the function of classifying the degree of harmonic pollution based on the characteristic parameters of the virtual electrical signal, and outputs the predicted value of harmonic pollution.

In step 103, input the electrical signal collected in the user-side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid;

In the embodiment of the present application, the collected electrical signals in the user-side power grid to be monitored can be input into the trained neural network model, and the trained neural network model is based on the electrical signals in the user-side power grid. The harmonic pollution degree is evaluated to obtain the harmonic pollution degree in the user-side power grid.

Optionally, in an implementation manner, the inputting the collected electrical signal in the user-side power grid to be monitored into the trained neural network model, and obtaining the harmonic pollution degree in the user-side power grid may include:

The collected characteristic parameters of the electrical signals in the user-side power grid to be monitored are input into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid.

In the embodiment of the present application, the characteristic parameter is extracted from the electrical signal in the user-side power grid to be monitored, and the characteristic parameter is input into the trained neural network model as the data of the electrical signal in the user-side power grid to perform user-side Assessment of the degree of harmonic pollution in the grid. The characteristic parameters may include at least one of the following: fundamental frequency, frequency of each harmonic, amplitude of each harmonic, phase angle of each harmonic, 2-50 harmonic voltage and harmonic current, three Phase voltage unbalance, voltage fluctuation and flicker, voltage deviation, voltage fundamental RMS and true RMS, current fundamental RMS and true RMS, fundamental active power, fundamental apparent power, true power factor.

In step 104, an early warning action is performed on the user-side power grid based on the harmonic pollution degree.

In the embodiment of this application, the trained neural network model evaluates (classifies) the harmonic pollution degree of the user-side power grid based on the electrical signal of the user-side power grid, and then can carry out early warning or perform corresponding actions according to the classified structure . For example, when the classified harmonic pollution degree of the user-side power grid is greater than a preset value, an alarm signal can be output to remind relevant personnel that the current harmonic pollution of the power grid is too large. When the classified harmonic pollution degree of the user-side power grid is greater than a preset value, an indication signal may also be output to instruct corresponding devices to perform predetermined actions, such as disconnecting some devices from the power grid.

It can be seen from the above that the embodiment of the present application provides a harmonic early warning method. By obtaining training samples, the training samples are electrical signals labeled with harmonic pollution degrees; based on the training samples, the convolutional neural network model constructed training, obtaining a trained neural network model; inputting electrical signals in the user-side grid to be monitored into the trained neural network model to obtain the degree of harmonic pollution in the user-side grid; based on the harmonic pollution Early warning action can be performed on the user-side power grid; thus, the early warning of the harmonics of the power grid can be realized, and the impact of the harmonics on the power grid can be reduced.

It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present application.

The following are device embodiments of the present invention. For details that are not exhaustively described therein, reference may be made to the corresponding method embodiments above.

Fig. 3 shows a schematic structural diagram of the harmonic warning device provided by the embodiment of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

A harmonic early warning device 3 includes: an acquisition unit 31 , a training unit 32 , a model analysis unit 33 and an early warning unit 34 .

An acquisition unit 31, configured to acquire a training sample, the training sample being an electrical signal labeled with a harmonic pollution degree;

The training unit 32 is used to train the convolutional neural network model based on the training samples to obtain the trained neural network model;

A model analysis unit 33, configured to input the collected electrical signals in the user-side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid;

The early warning unit 34 is configured to perform an early warning action on the user-side power grid based on the harmonic pollution degree.

Optionally, the harmonic warning device 3 may also include:

An extraction unit, configured to randomly generate a virtual electrical signal and extract characteristic parameters of the virtual electrical signal;

A power grid simulation unit, configured to input the virtual electrical signal into a pre-built virtual power grid model to obtain the state of the virtual power grid model;

a calculation unit, configured to calculate the harmonic pollution degree of the virtual electrical signal based on the state of the virtual grid model;

The acquiring unit 31 is further configured to acquire a preset number of virtual electrical signals and corresponding harmonic pollution degrees, and use the characteristic parameters of each virtual electrical signal and the corresponding harmonic pollution degrees as training samples.

Optionally, the harmonic warning device 3 may also include:

a harmonic generating unit, configured to obtain a preset fundamental wave, and randomly generate harmonics on the fundamental wave;

Correspondingly, the extraction unit is specifically configured to superimpose the fundamental wave and randomly generated harmonics to obtain a virtual electrical signal.

Optionally, the characteristic parameters of the virtual electrical signal include at least one of the following: fundamental frequency, frequency of each harmonic, amplitude of each harmonic, phase angle of each harmonic, 2-50th harmonic Harmonic voltage and harmonic current, three-phase voltage unbalance, voltage fluctuation and flicker, voltage deviation, voltage fundamental RMS and true RMS, current fundamental RMS and true RMS, fundamental active power, fundamental Apparent power, true power factor.

Optionally, the state of the virtual grid model includes at least one of the following: whether there is a circuit breaker malfunction, whether there is a transformer overheating, whether there is a motor burnout, whether there is a malfunction of an automatic device, whether there is any power consumption in the virtual grid model The quality of power received by the device.

Optionally, the harmonic warning device 3 may also include:

A prediction unit, configured to input the characteristic parameters of the virtual electrical signal into the constructed convolutional neural network model to obtain a harmonic pollution prediction value;

A reverse update unit, configured to reversely update the parameters of each layer of the convolutional neural network model based on the difference between the harmonic pollution prediction value and the label value;

Correspondingly, the training unit 32 is specifically configured to obtain a trained convolutional neural network model after the convolutional neural network model converges.

Optionally, the model analysis unit 33 is further configured to input the collected characteristic parameters of electrical signals in the user-side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid.

It can be seen from the above that the embodiment of the present application provides a harmonic early warning device. By obtaining training samples, the training samples are electrical signals labeled with harmonic pollution degrees; based on the training samples, the convolutional neural network model constructed training, obtaining a trained neural network model; inputting electrical signals in the user-side grid to be monitored into the trained neural network model to obtain the degree of harmonic pollution in the user-side grid; based on the harmonic pollution Early warning action can be performed on the user-side power grid; thus, the early warning of the harmonics of the power grid can be realized, and the impact of the harmonics on the power grid can be reduced.

FIG. 4 is a schematic block diagram of a terminal device provided by an embodiment of the present application. For ease of description, only parts related to the embodiment of the present application are shown. As shown in Figure 4, the terminal device 4 can be a software unit, a hardware unit or a combination of software and hardware built in terminal devices such as mobile phones, tablet computers, notebooks, computers, etc., or can be integrated into the mobile phone as an independent pendant , tablets, notebooks, computers and other terminal equipment.

The terminal device 4 includes: a processor 40 , a memory 41 and a computer program 42 stored in the memory 41 and operable on the processor 40 . When the processor 40 executes the computer program 42 , the steps in the above-mentioned embodiments of the harmonic warning method are implemented, for example, steps 101 to 104 shown in FIG. 1 . Alternatively, when the processor 40 executes the computer program 42, it realizes the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the units 31 to 34 shown in FIG. 3 .

Exemplarily, the computer program 42 can be divided into one or more modules/units, and the one or more modules/units are stored in the memory 41 and executed by the processor 40 to complete this invention. The one or more modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the terminal 4 . For example, the computer program 42 can be divided into an acquisition unit, a training unit, a model analysis unit and an early warning unit, and the specific functions of each unit are as follows:

An acquisition unit, configured to acquire a training sample, the training sample being an electrical signal labeled with a harmonic pollution degree;

The training unit is used to train the convolutional neural network model based on the training samples to obtain the trained neural network model;

A model analysis unit, configured to input the collected electrical signals in the user-side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid;

The early warning unit is configured to perform an early warning action on the user-side power grid based on the harmonic pollution degree.

Those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units, Module completion means that the internal structure of the terminal device is divided into different functional units or modules, so as to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into one processing unit, or each unit may exist separately physically, or two or more units may be integrated into one unit, and the above-mentioned integrated units may adopt hardware It can also be implemented in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the above terminal device, reference may be made to the corresponding process in the foregoing method embodiments, and details are not repeated here.

For other units or modules, reference may be made to the description in the embodiment shown in FIG. 4 , and details are not repeated here.

The terminal device includes, but is not limited to, a processor 40 and a memory 41 . Those skilled in the art can understand that FIG. 4 is only an example of the terminal device 4, and does not constitute a limitation to the terminal device 4. It may include more or less components than those shown in the figure, or combine certain components, or have different Components, for example, the terminal device may also include an input device, an output device, a network access device, a bus, and the like.

The processor 40 may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

The storage 41 may be an internal storage unit of the terminal device 4 , such as a hard disk or memory of the terminal device 4 . The memory 41 can also be an external storage device of the terminal device 4, such as a plug-in hard disk equipped on the terminal device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc. Further, the memory 41 may also include both an internal storage unit of the terminal device 4 and an external storage device. The memory 41 is used to store the computer program and other programs and data required by the terminal device. The memory 41 can also be used to temporarily store data that has been output or will be output.

In the above-mentioned embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not detailed or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

In the embodiments provided in this application, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components May be combined or may be integrated into another system, or some features may be omitted, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated module/unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments in the present application can also be completed by instructing related hardware through computer programs. The computer programs can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps in the above-mentioned various method embodiments can be realized. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable Excluding electrical carrier signals and telecommunication signals.

The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still implement the foregoing embodiments Modifications to the technical solutions described in the examples, or equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the application, and should be included in the Within the protection scope of this application.

Claims (10)

1. A harmonic early warning method is characterized by comprising the following steps:

acquiring a training sample, wherein the training sample is an electric signal with a harmonic pollution degree as a label;

training the constructed convolutional neural network model based on the training sample to obtain a trained neural network model;

inputting the collected electric signals in the user side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user side power grid;

and carrying out early warning action on the user side power grid based on the harmonic pollution degree.

2. The harmonic warning method of claim 1 wherein the obtaining training samples comprises:

randomly generating a virtual electric signal, and extracting characteristic parameters of the virtual electric signal;

inputting the virtual electric signal into a virtual power grid model which is constructed in advance to obtain the state of the virtual power grid model;

calculating the harmonic pollution degree of the virtual electric signal based on the state of the virtual power grid model;

the method comprises the steps of obtaining a preset number of virtual electric signals and corresponding harmonic pollution degrees, and using characteristic parameters of each virtual electric signal and the corresponding harmonic pollution degrees as training samples.

3. The harmonic warning method of claim 2, wherein the randomly generating the virtual electrical signal comprises:

acquiring a preset fundamental wave, and randomly generating each harmonic wave on the fundamental wave;

and superposing the fundamental wave and each randomly generated harmonic wave to obtain a virtual electric signal.

4. The harmonic warning method of claim 2 wherein the characteristic parameters of the virtual electrical signal comprise at least one of: fundamental wave frequency, frequency of each harmonic wave, amplitude of each harmonic wave, phase angle of each harmonic wave, voltage and harmonic current of 2-50 harmonic waves, unbalance degree of three-phase voltage, voltage fluctuation and flicker, voltage deviation, effective value and true effective value of voltage fundamental wave, effective value and true effective value of current fundamental wave, active power of fundamental wave, apparent power of fundamental wave and true power factor.

5. The harmonic warning method of claim 2, wherein the state of the virtual grid model comprises at least one of: whether circuit breaker malfunction exists or not, whether transformer overheating exists or not, whether motor burnout exists or not, whether automatic device malfunction exists or not, and the quality of electric energy received by each electric device in the virtual power grid model.

6. The harmonic pre-warning method of claim 2, wherein the training the constructed convolutional neural network model based on the training samples to obtain the trained neural network model comprises:

inputting the characteristic parameters of the virtual electric signals into the constructed convolutional neural network model to obtain a harmonic pollution predicted value;

reversely updating parameters of each layer of the convolutional neural network model based on the difference between the harmonic pollution predicted value and the tag value;

and after the convolutional neural network model is converged, obtaining a trained convolutional neural network model.

7. The harmonic pre-warning method according to claim 6, wherein the step of inputting the collected electric signals in the user-side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user-side power grid comprises:

and inputting the acquired characteristic parameters of the electric signals in the user side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user side power grid.

8. A harmonic warning device, comprising:

the acquisition unit is used for acquiring a training sample, wherein the training sample is an electric signal with a harmonic pollution degree as a label;

the training unit is used for training the constructed convolutional neural network model based on the training sample to obtain a trained neural network model;

the model analysis unit is used for inputting the collected electric signals in the user side power grid to be monitored into the trained neural network model to obtain the harmonic pollution degree in the user side power grid;

and the early warning unit is used for carrying out early warning action on the user side power grid based on the harmonic pollution degree.

9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the harmonic warning method as claimed in any one of the preceding claims 1 to 7.

10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the harmonic pre-warning method as set forth in any one of the preceding claims 1 to 7.

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