CN112016684A - Electric power terminal fingerprint identification method of deep parallel flexible transmission network - Google Patents

Electric power terminal fingerprint identification method of deep parallel flexible transmission network Download PDF

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CN112016684A
CN112016684A CN202010780676.2A CN202010780676A CN112016684A CN 112016684 A CN112016684 A CN 112016684A CN 202010780676 A CN202010780676 A CN 202010780676A CN 112016684 A CN112016684 A CN 112016684A
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殷林飞
马晨骁
罗仕逵
高放
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Abstract

The invention provides a power terminal fingerprint identification method of a deep parallel flexible transmitting network. The method is based on a deep flexible transmitting network model combining a flexible transmitting network and deep learning; and in training, the method uses different deep flexible transmitting network parameters to carry out parallel learning. When a power terminal is newly connected into the power system, the method completes the identification of the power terminal according to the fingerprint of the power terminal. The method can identify the power terminal accessed to the power system, provides reference for safe operation and control and load prediction of the power system, and effectively improves the intelligent level of the power system.

Description

Electric power terminal fingerprint identification method of deep parallel flexible transmission network
Technical Field
The invention belongs to the field of intellectualization and control of an electric power system, and relates to a method for learning and identifying fingerprints of an electric power terminal by using a deep learning model of a flexible transmitting network in parallel, which is suitable for load identification and intellectualization level improvement of the electric power system.
Background
With the rapid development of computer science, the intelligent level of the power system is also continuously improved. The current smart grid technology has penetrated into various links of power generation, power transmission, power distribution, power transformation and power utilization. Based on the smart grid technology, the operating efficiency and stability of the power system are improved. Under the background of rapid development in the field of artificial intelligence nowadays, one direction of improvement of the power grid intelligence level is to combine with artificial intelligence. However, training of the artificial intelligent model requires large-scale data to support, and the data source of the current power grid is often power grid equipment, so that the abundance of power grid operation data is effectively improved by adding the power terminal equipment into the data source, and a foundation is laid for improving the intelligence level of the power system. One important data in the power terminal data is the type, name of the power terminal accessing the power grid. With the large-scale application of terminal monitoring equipment such as an intelligent ammeter and the like, the problem of obtaining power consumption data of a power grid terminal does not exist. Therefore, the research on the effective power terminal fingerprint identification method for identifying the power consumption data has important guiding significance for knowing the power consumption characteristics of users, improving the intelligent level of a power grid and adjusting a power generation plan.
Disclosure of Invention
The invention provides a power terminal fingerprint identification method of a deep parallel flexible transmitting network, which uses a deep learning model of the parallel flexible transmitting network to learn and identify the fingerprint of a power terminal, and comprises the following steps:
acquiring power utilization data: collecting power consumption data of voltage and current of each power terminal in different working states when the power terminal operates independently;
a data preprocessing step: carrying out Fourier transform on the voltage and current waveforms obtained in the electricity data acquisition step to form a 4n matrix as a fingerprint characteristic of the power terminal;
training: the method aims to establish a deep parallel flexible transmitting network model, establish a voltage and current Fourier transform matrix model and a data set corresponding to the name of a power terminal and train the model in parallel; when the fingerprint data of the power terminal is trained, a deep flexible transmitting network model based on the combination of a flexible transmitting network and deep learning is used, and the number of the used flexible transmitting network neurons is not less than three. And in training, parallel learning is carried out by using different deep flexible transmitting network parameters, and the number of parallel models is not less than three. The flexible transmitting network neuron framework comprises two feedforward channel parameters (w, v) and an iterative memory intensity parameter M, and each parallel deep flexible transmitting network training step specifically comprises the following steps:
(1) a feed-forward process: taking the t-th neuron iteration for the first time as an example, the feedforward channel has two parameters (w, v), the memory strength of the neuron itself has an iteration parameter M, and after the feedforward function is described in a complex number, the following feedforward process function is obtained as
Figure BDA0002620070540000021
Wherein
Figure BDA0002620070540000022
For the output of the current iteration,
Figure BDA0002620070540000023
for the memory strength of the next iteration, sigma is a sigmoid excitation function and comprises a real part function and an imaginary part function, and both alpha and beta are adjustable parameters of the parallel model;
(2) and (3) a back propagation process: after one iteration is completed, the flexible transmitting network parameters are corrected according to the iteration result.
First, the gradient of the model is calculated,
Figure BDA0002620070540000024
wherein
Figure BDA0002620070540000025
For transmission in the reverse directionDifference of broadcast correction
Figure BDA0002620070540000026
In order to perform the dot-product operation,
Figure BDA0002620070540000027
for the activated point-state derivative vector,
Figure BDA0002620070540000028
and
Figure BDA0002620070540000029
the method for calculating the core for back propagation comprises two methods respectively connected with
Figure BDA00026200705400000210
And
Figure BDA00026200705400000211
an associated counter-propagating conduit;
correcting W, V by a given step length eta
Figure BDA00026200705400000212
The loss function E should be calculated after the back propagation process is finished. The loss function E is an integral loss function E (W, V) of the flexible transmit network neuron model, which fits the loss by the square of the difference of the real and predicted values, specifically:
Figure BDA00026200705400000213
wherein Y istIn order to be the output signal of the final layer,
Figure BDA00026200705400000214
is a real signal;
Figure BDA00026200705400000215
is a vector consisting of 0 and 1, ifThe device number is k, then
Figure BDA00026200705400000216
Is 1, otherwise is 0.
And judging whether the continuous iteration or the training reaches the expected index according to the loss function calculation result. The last iteration is carried out on M after the expected index is reached,
Figure BDA00026200705400000217
an identification step: and inputting a Fourier transform matrix of the voltage and current waveforms of the power terminal into the deep parallel flexible transmitting network classifier model after the training step is completed, judging and calculating parallel calculation results obtained by parallel models with different parameters according to an accumulation sum mode to obtain the maximum possible power terminal number, and taking the maximum possible power terminal number as an identification result.
The fingerprint identification method for the power terminal can learn voltage and current waveforms of a large number of power terminal devices, namely the fingerprints of the power terminal, and accurately identify the power terminal devices when the power terminal devices are accessed. Compared with a Macarocke-Pettes model, the flexible transmission network model has the advantages that the operation speed and the prediction accuracy are remarkably improved by introducing the flexible transmission network model, and the requirements of high precision and low calculation power are met under the condition that a large number of power terminals are connected. Through the identification of the fingerprint of the power terminal, the smart grid can sense the information of the power terminal equipment accessed to the power grid, so that more references are provided for the operation of the smart grid.
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FIG. 1 is a general design flow diagram of the method of the present invention.
Fig. 2 is a schematic diagram of a flexible transmission network of the method of the present invention.
Detailed Description
The invention provides a power terminal fingerprint identification method of a deep parallel flexible transmitting network, which is explained in detail by combining the attached drawings as follows:
FIG. 1 is a general design flow diagram of the method of the present invention. Firstly, voltage and current waveforms under different operating conditions are obtained from a large number of electric power terminal devices, waveform characteristics are learned through a deep parallel flexible transmitting network, electric power terminal fingerprint identification after intervention of new devices to generate new waveforms is achieved, and the method is specifically achieved through the following steps.
Step S1, collecting working conditions of the power terminal equipment needing to be learned, connecting the power terminal equipment to the voltage and current monitoring equipment, and applying different working conditions to the power terminal equipment to obtain voltage and current waveforms of the power terminal equipment under different working conditions. And carrying out high-frequency acquisition on the waveform, and storing to form test data.
And step S2, processing the voltage and current data acquired at high frequency in the step S1, performing discrete Fourier decomposition operation on the voltage and current data to obtain amplitude and phase information of each harmonic of a voltage and current waveform, and integrating the amplitude and phase information to form a 4n matrix which is regarded as the fingerprint of the power terminal equipment. Because the load in the power grid always presents the characteristic of a near-sine waveform and the content of higher harmonics is gradually reduced, the fingerprint matrix of the power terminal can be reasonably discarded, a decomposition value with higher frequency is ignored, and the operation speed of subsequent steps is increased.
And step S3, training a deep parallel flexible transmitting network by using the fingerprint data of the power terminal processed in the step S2, wherein the flexible transmitting network is improved on the basis of a Macarock-Pettes model, a new bionics principle is introduced, the research result of the neurons is improved according to biology, the parameters of feedforward channels are improved to 2, the 3 rd parameter is introduced to simulate the memory intensity of the neurons, and the improved model has higher target fitting speed and higher fitting precision. Before training, the depth and iteration parameters of the deep flexible transmitting network are reasonably set, the number of deep learning neurons is more than 3, and a deep flexible transmitting network model with different parameters or depths is trained by using parallel calculation.
The flexible transmitting network neuron framework comprises two feedforward channel parameters (w, v) and an iterative memory intensity parameter M, and each parallel deep flexible transmitting network training step specifically comprises the following steps:
(1) a feed-forward process: taking the t-th neuron as an example, the feedforward channel has two parameters (w, v), the memory strength of the neuron itself has an iterative parameter M, and after complex description of the feedforward function, the following feedforward process function is obtained as
Figure BDA0002620070540000041
Wherein
Figure BDA0002620070540000042
For the output of the current iteration,
Figure BDA0002620070540000043
for the memory strength of the next iteration, sigma is a sigmoid excitation function and comprises a real part function and an imaginary part function, and both alpha and beta are adjustable parameters of the parallel model;
(2) and (3) a back propagation process: after one iteration is finished, the flexible transmitting network parameters are corrected according to the iteration result, firstly, the model gradient is calculated,
Figure BDA0002620070540000044
wherein
Figure BDA0002620070540000045
In order to correct for the difference in the back-propagation,
Figure BDA0002620070540000046
in order to perform the dot-product operation,
Figure BDA0002620070540000047
point state derivative vector for activation
Figure BDA0002620070540000048
Figure BDA00026200705400000415
And
Figure BDA0002620070540000049
a method is computed for the back propagation kernel. Comprises two of
Figure BDA00026200705400000410
And
Figure BDA00026200705400000411
an associated counter-propagating conduit;
wherein:
Figure BDA00026200705400000412
Figure BDA00026200705400000413
wherein:
Figure BDA00026200705400000414
wherein:
Figure BDA0002620070540000051
after the calculation is finished, correcting W and V by a given step length eta
Figure BDA0002620070540000052
The loss function E should be calculated after the back propagation process is finished. The loss function E is an integral loss function E (W, V) of the flexible transmit network neuron model, which fits the loss by squaring the difference between the true and predicted values, specifically:
Figure BDA0002620070540000053
wherein Y istIn order to be the output signal of the final layer,
Figure BDA0002620070540000054
is a real signal;
Figure BDA0002620070540000055
is a vector consisting of 0 and 1, if the device number is k, then
Figure BDA0002620070540000056
Is 1, otherwise is 0.
And judging whether the continuous iteration or the training reaches the expected index according to the loss function calculation result. The last iteration is carried out on M after the expected index is reached,
Figure BDA0002620070540000057
and step S4, after the loss function of each parallel model meets the requirement, the fingerprint of the power terminal can be identified. Collecting voltage and current waveforms of the power terminal equipment, performing discrete Fourier transform on the voltage and current waveforms, inputting a transform result into a depth parallel flexible transmitting network model which completes learning, calculating in each parallel model, and adding each parallel result to obtain a maximum possible result, namely, a recognition result.
Fig. 2 is a schematic diagram of a flexible transmission network of the method of the present invention. According to the power terminal fingerprint identification method, the flexible transmission network model is introduced, compared with the Macarocke-Pettes model, the operation speed and the prediction accuracy are remarkably improved, and the requirements of high precision and low calculation power are met under the condition that a large number of power terminals are accessed in the actual engineering. Through the identification of the fingerprint of the power terminal, the smart grid can sense the information of the power terminal equipment accessed to the power grid, so that more references are provided for the operation of the smart grid.

Claims (7)

1. A power terminal fingerprint identification method of a deep parallel flexible transmission network is characterized in that the power terminal fingerprint can be learned by using the deep parallel flexible transmission network, and the power terminal is identified when being accessed into a power system; the method mainly comprises the following steps in the using process:
(1) acquiring power utilization data: collecting power consumption data of voltage and current of each power terminal in different working states when the power terminal operates independently;
(2) a data preprocessing step: performing discrete Fourier transform on the voltage and current waveforms obtained in the step (1) to form a 4n matrix as a fingerprint feature of the power terminal;
(3) training: establishing a deep parallel flexible transmitting network model, and establishing a voltage and current Fourier transform matrix model and a data set corresponding to the name of the power terminal to train the model in parallel;
(4) an identification step: and inputting a discrete Fourier transform matrix of the voltage and current waveforms of the power terminal in the deep parallel flexible transmitting network classifier model after the training step is completed, and outputting a fingerprint matching result of the power terminal after the judgment of a parallel system.
2. The power terminal fingerprint identification method of the deep parallel flexible transmission network according to claim 1, wherein the power terminal fingerprint characteristics in the step (2) are voltage and current waveforms of the power terminal in different operating states, and a 4n matrix obtained by performing discrete fourier transform on the power terminal fingerprint characteristics is a mathematical model of the characteristics.
3. The power terminal fingerprint identification method of the deep parallel flexible transmission network according to claim 1, wherein a deep flexible transmission network model based on a combination of the flexible transmission network and deep learning is used when the power terminal fingerprint data is trained in the step (3); and in training, different deep flexible transmitting network parameters are used for parallel learning.
4. The power terminal fingerprint identification method of the deep parallel flexible transmission network according to claim 1, wherein when the power terminal fingerprint data is identified in the step (4), the deep flexible transmission network according to claim 3 is used for identification, a plurality of predicted values are output through a parallel model, and the judgment is performed in an accumulation sum mode to determine the identification result.
5. The power terminal fingerprint identification method of the deep parallel flexible transmission network according to claim 1, wherein the number of the used flexible transmission network neurons is not less than three, and the number of the used flexible transmission network neurons is not less than three, wherein the number of the parallel models is not less than three, and the flexible transmission network neuron framework comprises two feedforward channel parameters (w, v), an iterative memory strength parameter M and a loss function E; the loss function E is an integral loss function E (W, V) of the flexible transmit network neuron model, which fits the loss by the square of the difference of the real and predicted values, specifically:
Figure FDA0002620070530000011
wherein Y istIs the output signal of the final layer;
Figure FDA0002620070530000012
is a real signal;
Figure FDA0002620070530000013
is a vector consisting of 0 and 1, if the device number is k, then
Figure FDA0002620070530000014
Is 1, otherwise is 0.
6. The power terminal fingerprint identification method of the deep parallel flexible transmission network according to claim 1, wherein each parallel deep flexible transmission network training step specifically comprises the following steps,
(1) a feed-forward process: taking the t-th neuron iteration for the first time as an example, the feedforward channel has two parameters (w, v), the memory strength of the neuron itself has an iteration parameter M, and after the feedforward function is described in a complex number, the following feedforward process function is obtained as
Figure FDA0002620070530000021
Wherein
Figure FDA0002620070530000022
For the output of the current iteration,
Figure FDA0002620070530000023
for the memory strength of the next iteration, sigma is a sigmoid excitation function and comprises a real part function and an imaginary part function, and both alpha and beta are adjustable parameters of the parallel model;
(2) and (3) a back propagation process: after one iteration is finished, the flexible transmitting network parameters are corrected according to the iteration result, firstly, the model gradient is calculated,
Figure FDA0002620070530000024
wherein
Figure FDA0002620070530000025
In order to correct for the difference in the back-propagation,
Figure FDA0002620070530000026
in order to perform the dot-product operation,
Figure FDA0002620070530000027
for the activated point-state derivative vector,
Figure FDA0002620070530000028
and
Figure FDA0002620070530000029
the method for calculating the core for back propagation comprises two methods respectively connected with
Figure FDA00026200705300000210
And
Figure FDA00026200705300000211
an associated counter-propagating conduit;
correcting W and V with a given step length eta,
Figure FDA00026200705300000212
after the back propagation process is finished, judging whether the iteration is continued or the training reaches the expected index according to the calculation result of the loss function, carrying out the last iteration on the M after the expected index is reached,
Figure FDA00026200705300000213
7. the power terminal fingerprint identification method of the deep parallel flexible transmission network according to claim 1, characterized in that, the real-time voltage and current waveforms of the power terminal operation are subjected to discrete fourier transform to form a 4n matrix, and then sent into the deep parallel flexible transmission network which has been trained according to claim 6 for calculation, and the obtained parallel calculation result is judged and calculated according to the accumulation sum mode, so as to obtain the maximum possible power terminal number, which is regarded as the identification result.
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