CN113033633B - Equipment type identification method combining power fingerprint knowledge and neural network - Google Patents
Equipment type identification method combining power fingerprint knowledge and neural network Download PDFInfo
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Abstract
The invention discloses a device type identification method combining power fingerprint knowledge and a neural network, which comprises the following steps: s1, acquiring voltage and current sampling data when equipment is used; s2, setting a time interval, and dividing the data obtained in the S1; s3, converting the data obtained in the S2 into common electrical characteristic quantities; s4, inputting the electrical characteristic quantity obtained in the S3 into a knowledge extraction model to obtain an electrical fingerprint knowledge point of the equipment; s5, encoding the electric power fingerprint knowledge points obtained in the S4, and splicing the electric power fingerprint knowledge points with the electric characteristic quantity obtained in the S3 to obtain a total characteristic vector; and S6, inputting the total characteristic vector obtained in the S5 into the trained neural network to obtain the equipment type. Compared with the traditional load identification method, the method organically combines the machine learning method and the knowledge driving method, can greatly reduce the requirement of data quantity, can accelerate the convergence data and judgment speed of the model, and can improve the accuracy of the model.
Description
Technical Field
The invention relates to a device type identification method combining power fingerprint knowledge and a neural network, and belongs to the technical field of load identification.
Background
With the gradual improvement of the electrical measurement system of the power grid, more and more electrical measurement devices are put into use, and load identification by utilizing monitoring data becomes possible. The load identification methods proposed at present are numerous, but mainly focus on a complete data-driven machine learning method, and less people consider combining expert experience and knowledge to perform load identification. For example, chinese patent application (publication No. CN 105974219B) proposes a load identification method based on full data driving by using voltage, current and power factor, and has a disadvantage that the three devices with relatively close data volumes cannot be identified; for example, chinese patent publication No. CN111914899A proposes to combine artificial rules with machine learning for recognition, which is combined with partial knowledge, but the knowledge is derived from a data-driven decision tree and is not interpretable expert experience and knowledge.
Based on this, how to reasonably combine the expert knowledge system and the machine learning method is the key point for improving the load identification accuracy.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the device type identification method combining power fingerprint knowledge and a neural network is provided to solve the technical problems in the prior art.
The technical scheme adopted by the invention is as follows: a method of device type identification incorporating power fingerprint knowledge and a neural network, the method comprising the steps of:
s1, acquiring voltage and current sampling data when equipment is used;
s2, setting a time interval, and cutting the voltage and current sampling data obtained in the step S1;
s3, converting the data obtained in the step S2 into common electrical characteristic quantities;
s4, inputting the electrical characteristic quantity obtained in the step S3 into a knowledge extraction model to obtain an electrical fingerprint knowledge point of the equipment;
and S5, coding the electric power fingerprint knowledge points obtained in the step S4, and splicing the electric power fingerprint knowledge points with the electric characteristic quantity obtained in the step S3 to obtain a total characteristic vector.
And S6, inputting the total feature vector obtained in the step S5 into a trained neural network to obtain the equipment type.
The voltage and current sampling data obtained in step S1 are original sampling data with a sampling frequency greater than 100 Hz.
The common electrical characteristic quantities in step S3 include active power, reactive power, apparent power, voltage amplitude, current amplitude, power factor, 0-31 th harmonic current amplitude and phase.
The electric power fingerprint knowledge points in the step S4 are as follows: electrical external characteristics, state-time varying characteristics, and the like. The electrical external characteristic description device represents characteristics of an external circuit, and is particularly represented by resistance, sensitivity, nonlinearity and the like, for example, most heating devices such as a hot water kettle and the like are resistance type devices, most devices containing a motor are sensitivity devices, and most devices with an electronic display screen and the like are nonlinearity devices such as a computer and a television. The state time-varying characteristic describes whether the electrical characteristic quantity of the equipment tends to be stable when the equipment works, for example, the working state of the equipment such as a hot water kettle, a charger and the like is stable, and a television and a computer are determined according to actual conditions.
The method for realizing the knowledge extraction model of the electrical external characteristics comprises the following steps:
(1) voltage harmonic U obtained according to step S3 i And current harmonics I i Calculating the impedance value Z of each sub-harmonic i ：
(2) Calculating the variation coefficient c of each harmonic impedance value v
Wherein sigma z The standard deviation of the impedance values for each harmonic,
the average value of each harmonic impedance value is obtained;
(3) if the coefficient of variation is more than or equal to 10%, judging that the external characteristic of the equipment is nonlinear, and if the coefficient of variation is less than 10%, entering the next step;
(4) according to the power factor cos phi obtained in the step S3, if the power factor is greater than or equal to 0.98, the external characteristic of the device is judged to be resistive, and if the power factor is less than 0.98, the external characteristic of the device is judged to be inductive.
The encoding method in step S5 may be any other encoding method, such as one-hot code or sequential encoding.
The neural network training mode in step S6 is: and (3) collecting voltage and current data of part of equipment in advance, obtaining total characteristic vector data through the steps S2-S5, and finally inputting the total characteristic vector data into the neural network for feedback training until the neural network can accurately identify 95% of training equipment.
The invention has the beneficial effects that: compared with the prior art, the method for identifying the equipment type by combining the power fingerprint knowledge with the neural network has the thought that the traditional load identification method depends on data driving, and a large amount of data needs to be input to train the network to achieve a certain effect.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific embodiments.
Example 1: as shown in fig. 1, a device type identification method combining power fingerprint knowledge and a neural network includes the following steps:
step S1, voltage and current data of 100 (including 20) household electric devices are collected in advance through an intelligent socket, the sampling frequency of the intelligent socket is 6.4kHz, the sampling precision is 0.5 level, and the sampling time is 10S.
And S2, setting a time interval of 0.02S, dividing the voltage and current sampling data of each device obtained in the step S1 into 500 data segments, and obtaining 5 ten thousand data segments by 100 devices.
S3, calculating the voltage and current sampling data segment obtained in the step S2 to obtain electrical characteristic quantity, such as 0-11 th harmonic U of voltage i 0-11 th harmonic of the current I i (where i represents the number of times), the apparent power S, the reactive power Q, and the power factor cos φ are calculated. Wherein the harmonic of the voltage and the current is calculated by a fast Fourier transform algorithm (FFT), and the apparent power S, the reactive power Q and the power factor cos phi are calculated according to the following formula:
S＝UI
where U and I are the voltage and current effective values, respectively.
And S4, inputting the electrical characteristic quantity obtained in the step S3 into a knowledge extraction model to obtain electrical fingerprint knowledge points of the equipment, such as electrical external characteristics and state time-varying characteristics. The knowledge extraction model of the electrical external characteristics is as follows:
(1) voltage harmonic U obtained according to step S3 i And current harmonics I i Calculating the impedance value Z of each sub-harmonic i 。
(2) Calculating the variation coefficient c of each harmonic impedance value v
Wherein σ z As a standard deviation of the impedance values of the respective harmonics,
is the average value of the impedance values of the harmonics.
(3) If the coefficient of variation is larger than or equal to 10%, judging that the external characteristic of the equipment is nonlinear, and if the coefficient of variation is smaller than 10%, entering the next step.
(4) According to the power factor cos phi obtained in the step S3, if the power factor is greater than or equal to 0.98, the external characteristic of the device is judged to be resistive, and if the power factor is less than 0.98, the external characteristic of the device is judged to be inductive.
And S5, encoding the electric power fingerprint knowledge points obtained in the step S4, and splicing the electric power fingerprint knowledge points with the electric characteristic quantity obtained in the step S3 to obtain a total characteristic vector. The coding mode adopts sequential coding, for example, the resistance of the electrical external characteristic is represented by 0, the sensitivity is represented by 1, and the nonlinearity is represented by 2; the steady operation of the state-varying characteristic is represented by 0, and the unstable operation is represented by 1. The total feature vector then includes: 0-11 th harmonic U of voltage i 0-11 th harmonic of the current I i (where i represents the number of times), calculating the apparent power S, the reactive power Q, the power factor cos phi, the electrical external characteristics, the state time varying characteristics, and a total of 52-dimensional data, wherein each voltage current harmonic comprises a 2-dimensional array (amplitude and phase).
S6, inputting the total feature vector obtained in the step S5 into a trained neural network model, wherein the training mode is as follows: inputting pre-collected equipment data into a neural network for judgment, and then continuously adjusting neural network parameters until 95% of training data can be identified by a network model. Wherein the neural network model parameters are set as: the number of the network layers is 3, the number of neurons in each layer is 52, 20 and 10 respectively, a full connection mode is adopted among the layers, and a neuron activation function is set to be a sigmoid function. And finally, inputting the data to be recognized into the trained model to obtain a recognition result.
Through the steps, the type of the load equipment can be identified according to the data measured by the measuring terminal.
The invention provides a device type identification method combining power fingerprint knowledge and a neural network, which is based on the idea that a traditional load identification method depends on data driving and needs to input a large amount of data to train the network to achieve a certain effect, and the invention provides the load identification method combining power fingerprint expert knowledge.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and therefore, the scope of the present invention should be determined by the scope of the claims.
Claims (8)
1. A device type identification method combining power fingerprint knowledge and a neural network is characterized in that: the method comprises the following steps:
s1, acquiring voltage and current sampling data when equipment is used;
s2, setting a time interval, and cutting the voltage and current sampling data obtained in the step S1;
s3, inputting the cut voltage and current sampling data obtained in the step S2 into a plurality of trained knowledge extraction models to obtain power fingerprint knowledge points of the equipment;
s4, converting the cut voltage and current sampling data obtained in the S2 into common electrical characteristic quantities;
s5, coding the electric power fingerprint knowledge points obtained in the S3, and splicing the electric power fingerprint knowledge points with the electric characteristic quantity obtained in the S4 to obtain a total characteristic vector;
s6, inputting the total feature vector obtained in the step S5 into a trained neural network to obtain the type of the equipment;
the electric power fingerprint knowledge points in the step S3 are as follows: electrical external and state-varying characteristics; the method for realizing the knowledge extraction model of the electrical external characteristics comprises the following steps:
(1) voltage harmonic U obtained according to step S3 i And current harmonics I i Calculating the impedance value Z of each sub-harmonic i ：
(2) Calculating the variation coefficient c of each harmonic impedance value v
Wherein σ z The standard deviation of each harmonic impedance value is shown, and z is the mean value of each harmonic impedance value;
(3) if the coefficient of variation is more than or equal to 10%, judging that the external characteristic of the equipment is nonlinear, and if the coefficient of variation is less than 10%, entering the next step;
(4) according to the power factor cos phi obtained in the step S3, if the power factor is greater than or equal to 0.98, the external characteristic of the device is judged to be resistive, and if the power factor is less than 0.98, the external characteristic of the device is judged to be inductive.
2. The method for device type identification combining power fingerprint knowledge and neural networks of claim 1, wherein: the voltage and current sampling data obtained in the step S1 are original sampling data with the sampling frequency of more than 100 Hz.
3. The method for identifying the type of the device by combining the power fingerprint knowledge and the neural network according to claim 1, wherein the method comprises the following steps: the common electrical characteristic quantities described in step S4 are active power, reactive power, apparent power, voltage amplitude, current amplitude, power factor, 0-31 th harmonic current amplitude and phase.
4. The method for identifying the type of the device by combining the power fingerprint knowledge and the neural network according to claim 1, wherein the method comprises the following steps: the electrical external characteristics are: the characteristics of the device embodied in the external circuit are described, embodied as resistive, inductive, nonlinear.
5. The method for identifying the type of the device by combining the power fingerprint knowledge and the neural network according to claim 1, wherein the method comprises the following steps: the state-time-varying characteristic is: it is described whether or not the electrical characteristic quantity tends to be stable when the apparatus is in operation.
6. The method for identifying the type of the device by combining the power fingerprint knowledge and the neural network according to claim 1, wherein the method comprises the following steps: the encoding method in step S5 adopts one-hot code, sequential encoding or any other encoding method.
7. The method for device type identification combining power fingerprint knowledge and neural networks of claim 1, wherein: the neural network training method in step S6 comprises the following steps: and (3) collecting voltage and current data of part of equipment in advance, obtaining total characteristic vector data through the steps S2-S5, and finally inputting the total characteristic vector data into the neural network for feedback training until the neural network can accurately identify 95% of training equipment.
8. The method for device type identification combining power fingerprint knowledge and neural networks of claim 1, wherein: the neural network model parameters are set as: the number of the network layers is 3, the number of neurons in each layer is 52, 20 and 10 respectively, a full connection mode is adopted among the layers, and a neuron activation function is set to be a sigmoid function.
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