CN110633870A - Harmonic early warning method, harmonic early warning device and terminal equipment - Google Patents
Harmonic early warning method, harmonic early warning device and terminal equipment Download PDFInfo
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Abstract
The application is applicable to the technical field of electric power, and provides a harmonic early warning method, a harmonic early warning device, terminal equipment and a computer readable storage medium, wherein the harmonic early warning method comprises 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. By the method and the device, the early warning of the harmonic waves of the power grid can be realized, and the influence of the harmonic waves on the power grid can be reduced.
Description
Technical Field
The present application belongs to the field of power technologies, and in particular, relates to a harmonic warning method, a harmonic warning apparatus, a terminal device, and a computer-readable storage medium.
Background
With the development of the power grid technology in China, power electronic equipment is widely applied to various fields, great convenience is brought to the life of people, the development of the frequency conversion technology brings great pollution to a power grid, and higher harmonic current and harmonic voltage generated by the frequency conversion equipment have great influence on the voltage of the power grid and even influence on the stable operation of the whole power grid.
In order to well manage the power grid harmonic wave, a method capable of early warning the power grid harmonic wave is needed to reduce the influence of the harmonic wave on the power grid.
Disclosure of Invention
In view of this, embodiments of the present application provide a harmonic early warning method, a harmonic early warning apparatus, a terminal device, and a computer-readable storage medium, so as to implement early warning on a power grid harmonic and reduce the influence of the harmonic on the power grid.
A first aspect of an embodiment of the present application provides a harmonic early warning method, including:
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.
A second aspect of the embodiments of the present application provides a harmonic early warning device, including:
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.
A 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 executable on the processor, where the processor implements the steps of the harmonic warning method provided in the first aspect of the embodiments of the present application when executing the computer program.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by one or more processors, implements the steps of the harmonic warning method provided in the first aspect of embodiments of the present application.
A fifth aspect of embodiments of the present application provides a computer program product comprising a computer program that, when executed by one or more processors, performs the steps of the method provided by the first aspect of embodiments of the present application.
The embodiment of the application provides a harmonic early warning method, which comprises the steps of obtaining 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; performing early warning action on the user side power grid based on the harmonic pollution degree; therefore, the early warning of the harmonic waves of the power grid can be realized, and the influence of the harmonic waves on the power grid can be reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an implementation of a harmonic warning method provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of an implementation of obtaining a training sample in a harmonic early warning method provided in an embodiment of the present application;
fig. 3 is a schematic block diagram of a harmonic warning apparatus provided in an embodiment of the present application;
fig. 4 is a schematic block diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of 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 that depart from 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 will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of an implementation of a harmonic warning method provided in an 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, wherein the training sample is an electric signal with a harmonic pollution degree as a label;
in the embodiment of the present application, a training sample composed of electrical signals with harmonic pollution degrees as labels is first obtained, for example, the training sample may include data corresponding to a plurality of electrical signals, and the data corresponding to each electrical signal has a label indicating the harmonic pollution degree corresponding to the corresponding electrical signal. The training sample is used for training a pre-constructed neural network model, so that the pre-constructed neural network model has the functions of receiving data of corresponding electric signals and outputting harmonic pollution labels of the corresponding electric signals based on the training and learning of the training sample.
Optionally, in an implementation manner, as shown in fig. 2, the obtaining of the training sample may be implemented based on the following steps:
202, 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;
In the embodiment of the application, a virtual electric signal can be randomly generated at first, and the characteristic parameters of the virtual electric signal are extracted; and inputting the corresponding virtual electric signal into a virtual power grid model which is constructed in advance, wherein the virtual power grid model can be a model obtained by simulating an actual power grid, and the virtual power grid model can present different states, such as the action states of devices such as a circuit breaker, a transformer and the like in the virtual power grid model, by receiving the virtual electric signal. The harmonic pollution degree of the current virtual power grid model can be calculated according to the state of the virtual power grid model, the harmonic pollution degree corresponds to the input virtual electric signals, the harmonic pollution degree is taken as a label, and the label and the characteristic parameters of the corresponding virtual electric signals form training data in a training sample.
Optionally, in an implementation manner, the step 201 may include:
2011, obtaining a preset fundamental wave, and randomly generating each subharmonic on the fundamental wave;
step 2012, the fundamental wave and each randomly generated harmonic are superimposed to obtain a virtual electrical signal.
In the embodiment of the application, the virtual electric signals can be obtained by superposing the randomly generated harmonics on the fundamental waves according to the preset fundamental waves, so that the process of obtaining electric signal training data from an actual power grid is avoided, the cost is saved, the method is more convenient, training samples can be diversified by controlling the harmonics of different types to be superposed, and the prediction accuracy of a neural network model is improved.
Optionally, in an implementation, the characteristic parameter of the virtual electrical signal may include 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.
Optionally, in an implementation, the state of the virtual power grid model may include 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.
In step 102, training the constructed convolutional neural network model based on the training sample to obtain a trained neural network model;
in the embodiment of the present application, the obtained training sample is used to train a pre-constructed convolutional neural network model, so that the training learning of the pre-constructed neural network model based on the training sample has the functions of receiving data of a corresponding electrical signal and outputting a harmonic pollution label of the corresponding electrical signal, that is, the trained neural network model is obtained.
For a pre-constructed Convolutional Neural Network (CNN), which is a type of feed-forward Neural network including convolution calculation and having a deep structure, the method has a characterization learning capability, and can perform translation invariant classification on input information according to a hierarchical structure thereof, and therefore, the method is also called a "translation invariant artificial Neural network". The convolutional neural network is constructed by imitating a visual perception mechanism of a living being, and can perform supervised learning and unsupervised learning, and the convolutional neural network can perform lattice characterization with smaller calculation amount due to parameter sharing of convolution kernels in hidden layers and sparsity of interlayer connection.
In the embodiment of the present application, the pre-constructed convolutional neural network may include an input layer, a hidden layer, and an output layer, where the input layer may process multidimensional data, for example, may receive and process multidimensional characteristic parameters corresponding to the electrical signals. The hidden layers may include convolutional layers, pooling layers, and fully-connected layers, that is, the hierarchical order of data processing in the pre-constructed convolutional neural network may be: input-convolutional layer-pooling layer-full-link layer-output.
The convolutional layer has the function of extracting the characteristics of input data, and can internally contain a plurality of convolutional kernels, and each element forming the convolutional kernels corresponds to a weight coefficient and a deviation amount, and is similar to a neuron of a feedforward neural network. Each neuron in the convolution layer is connected to a plurality of neurons in a closely located region in the previous layer, the size of which depends on the size of the convolution kernel, called the "receptive field". When the convolution kernel works, the convolution kernel regularly sweeps the input characteristics, matrix element multiplication summation is carried out on the input characteristics in the receptive field, and deviation amount is superposed. After the feature extraction is performed on the convolutional layer, the output feature map is transmitted to the pooling layer for feature selection and information filtering. The pooling layer contains a pre-set pooling function whose function is to replace the result of a single point in the feature map with the feature map statistics of its neighboring regions. The step of selecting the pooling area by the pooling layer is the same as the step of scanning the characteristic diagram by the convolution kernel, and the pooling size, the step length and the filling are controlled. The fully-connected layer is equivalent to the hidden layer in a 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 signals are transmitted to other fully-connected layers. The feature map loses spatial topology in the fully connected layer, is expanded into vectors and passes through the excitation function.
In the embodiment of the application, the structure and the working principle of the output layer of the pre-constructed convolutional neural network are the same as those of the output layer in the traditional feedforward neural network. For example, for harmonic pollution classification problems of the customer-side power grid, the output layer may output the classification labels using a logistic function or a normalized exponential function.
Optionally, the training the constructed convolutional neural network model based on the training sample, and obtaining the trained neural network model may include:
step 1021, inputting the characteristic parameters of the virtual electric signals into a constructed convolutional neural network model to obtain a harmonic pollution predicted value;
step 1022, 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 1023, after the convolutional neural network model is converged, obtaining a trained convolutional neural network model.
In the embodiment of the application, the pre-constructed convolutional neural network model is trained by using the training data, that is, supervised learning is performed by using a back propagation algorithm, so that the pre-constructed convolutional neural network model can learn the rule between the characteristic parameter of the virtual electric signal and the label value (harmonic pollution) of the characteristic parameter in the training sample, and further has the function of performing harmonic pollution degree classification based on the characteristic parameter of the virtual electric signal, and a predicted value of the harmonic pollution is output.
In step 103, 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;
in the embodiment of the application, the collected electric signals in the user side power grid to be monitored can be input into the trained neural network model, and the harmonic pollution degree in the user side power grid is evaluated through the trained neural network model based on the electric signals in the user side power grid, so that the harmonic pollution degree in the user side power grid is obtained.
Optionally, in an implementation manner, the inputting the acquired electric 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:
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.
In the embodiment of the application, the characteristic parameters are extracted from the electric signals in the user side power grid to be monitored, and the characteristic parameters are used as the data of the electric signals in the user side power grid and input into the trained neural network model so as to evaluate the harmonic pollution degree in the user side power grid. The characteristic parameter may include 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.
And in step 104, performing an early warning action on the user side power grid based on the harmonic pollution degree.
In the embodiment of the application, the harmonic pollution degree of the user side power grid is evaluated (classified) based on the electric signal of the user side power grid through the trained neural network model, and then early warning or corresponding action can be executed according to the classified structure. For example, when the harmonic pollution degree of the user-side power grid obtained by classification is greater than a preset value, an alarm signal can be output to remind relevant personnel that the current harmonic pollution degree of the power grid is too large. When the harmonic pollution degree of the classified user-side power grid is greater than a preset value, an indication signal can be output to indicate the corresponding device to perform a preset action, for example, disconnecting part of the devices from the power grid.
In view of the above, the embodiment of the application provides a harmonic early warning method, in which a training sample is obtained, and the training sample is an electrical 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; performing early warning action on the user side power grid based on the harmonic pollution degree; therefore, the early warning of the harmonic waves of the power grid can be realized, and the influence of the harmonic waves on the power grid can be reduced.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
The following are embodiments of the apparatus of the invention, reference being made to the corresponding method embodiments described above for details which are not described in detail therein.
Fig. 3 shows a schematic structural diagram of a harmonic warning device provided in an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:
a harmonic warning device 3 comprising: an acquisition unit 31, a training unit 32, a model analysis unit 33 and an early warning unit 34.
An obtaining unit 31, configured to obtain a training sample, where the training sample is an electrical signal with a harmonic pollution degree as a label;
the training unit 32 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 33 is configured to input the acquired electric signal in the user-side power grid to be monitored into the trained neural network model, so as to obtain a harmonic pollution degree in the user-side power grid;
and the early warning unit 34 is used for performing an early warning action on the user side power grid based on the harmonic pollution degree.
Optionally, the harmonic early warning device 3 may further include:
the extraction unit is used for randomly generating a virtual electric signal and extracting characteristic parameters of the virtual electric signal;
the power grid simulation unit is used for 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;
the calculating unit is used for calculating the harmonic pollution degree of the virtual electric signal based on the state of the virtual power grid model;
the obtaining unit 31 is further configured to obtain a preset number of virtual electrical signals and corresponding harmonic pollution degrees, and use the characteristic parameter of each virtual electrical signal and the corresponding harmonic pollution degree as a training sample.
Optionally, the harmonic early warning device 3 may further include:
the harmonic generation unit is used for acquiring a preset fundamental wave and randomly generating each subharmonic on the fundamental wave;
correspondingly, the extracting unit is specifically configured to superimpose the fundamental wave and each randomly generated harmonic to obtain a virtual electrical signal.
Optionally, the characteristic parameter of the virtual electrical signal includes 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.
Optionally, the state of the virtual power grid model includes 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.
Optionally, the harmonic early warning device 3 may further include:
the prediction unit is used for inputting the characteristic parameters of the virtual electric signals into the constructed convolutional neural network model to obtain a harmonic pollution prediction value;
the reverse updating unit is used for reversely updating parameters of each layer of the convolutional neural network model based on the difference between the harmonic pollution predicted value and the label value;
correspondingly, the training unit 32 is specifically configured to obtain the trained convolutional neural network model after the convolutional neural network model converges.
Optionally, the model analysis unit 33 is further configured to input the acquired characteristic parameters of the electric signal in the user-side electric network to be monitored into the trained neural network model, so as to obtain the harmonic pollution degree in the user-side electric network.
Therefore, the embodiment of the application provides a harmonic early warning device, by acquiring a training sample, 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; performing early warning action on the user side power grid based on the harmonic pollution degree; therefore, the early warning of the harmonic waves of the power grid can be realized, and the influence of the harmonic waves on the power grid can be reduced.
Fig. 4 is a schematic block diagram of a terminal device provided in an embodiment of the present application, and only a part related to the embodiment of the present application is shown for convenience of description. As shown in fig. 4, the terminal device 4 may be a software unit, a hardware unit or a combination of software and hardware unit built in the terminal device such as a mobile phone, a tablet computer, a notebook computer, a computer, etc., or may be integrated as a separate hanger into the terminal device such as the mobile phone, the tablet computer, the notebook computer, the computer, etc.
The terminal device 4 includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40 executes the computer program 42 to implement the steps in the above-mentioned embodiments of the harmonic warning method, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements 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.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the terminal 4. For example, the computer program 42 may be divided into an acquisition unit, a training unit, a model analysis unit and an early warning unit, and each unit has the following specific functions:
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.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is merely used as an example, and in practical applications, the foregoing function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the terminal device is divided into different functional units or modules to perform all or part of the above-described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the terminal device may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Other units or modules can be referred to the description of the embodiment shown in fig. 4, and are not described again here.
The terminal device includes, but is not limited to, a processor 40, and a memory 41. Those skilled in the art will appreciate that fig. 4 is only one example of a terminal device 4 and does not constitute a limitation of terminal device 4 and may include more or fewer components than shown, or some components may be combined, or different components, for example, the terminal device may also include an input device, an output device, a network access device, a bus, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal device. The memory 41 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the above-described terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present 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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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111724290A (en) * | 2020-06-24 | 2020-09-29 | 山东建筑大学 | Environment-friendly equipment identification method and system based on deep hierarchical fuzzy algorithm |
| CN112907105A (en) * | 2021-03-10 | 2021-06-04 | 广东电网有限责任公司 | Early warning method and device based on service scene |
| CN113094636A (en) * | 2021-04-21 | 2021-07-09 | 国网福建省电力有限公司 | Interference user harmonic level estimation method based on massive monitoring data |
| CN113378483A (en) * | 2021-07-12 | 2021-09-10 | 广东电网有限责任公司 | Power grid data early warning method, device, equipment and storage medium |
| CN115128345A (en) * | 2022-07-01 | 2022-09-30 | 费莱(浙江)科技有限公司 | Power grid safety early warning method and system based on harmonic monitoring |
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2019
- 2019-09-24 CN CN201910905296.4A patent/CN110633870A/en not_active Withdrawn
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111724290A (en) * | 2020-06-24 | 2020-09-29 | 山东建筑大学 | Environment-friendly equipment identification method and system based on deep hierarchical fuzzy algorithm |
| WO2021258636A1 (en) * | 2020-06-24 | 2021-12-30 | 山东建筑大学 | Deep hierarchical fuzzy algorithm-based environmental protection equipment recognition method and system |
| CN111724290B (en) * | 2020-06-24 | 2023-09-26 | 山东建筑大学 | Environment-friendly equipment identification method and system based on depth layering fuzzy algorithm |
| CN112907105A (en) * | 2021-03-10 | 2021-06-04 | 广东电网有限责任公司 | Early warning method and device based on service scene |
| CN113094636A (en) * | 2021-04-21 | 2021-07-09 | 国网福建省电力有限公司 | Interference user harmonic level estimation method based on massive monitoring data |
| CN113378483A (en) * | 2021-07-12 | 2021-09-10 | 广东电网有限责任公司 | Power grid data early warning method, device, equipment and storage medium |
| CN115128345A (en) * | 2022-07-01 | 2022-09-30 | 费莱(浙江)科技有限公司 | Power grid safety early warning method and system based on harmonic monitoring |
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