CN110632545A - Regression neural network-based error risk assessment method and device for electronic transformer - Google Patents

Regression neural network-based error risk assessment method and device for electronic transformer Download PDF

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CN110632545A
CN110632545A CN201910909225.1A CN201910909225A CN110632545A CN 110632545 A CN110632545 A CN 110632545A CN 201910909225 A CN201910909225 A CN 201910909225A CN 110632545 A CN110632545 A CN 110632545A
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陈刚
卢树峰
李志新
杨世海
徐敏锐
丁俊
陈文广
周赣
李志�
程思远
陆子刚
成国锋
吴桥
徐欣
程含渺
尤文正
陈飞
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State Grid Corp of China SGCC
Southeast University
State Grid Jiangsu Electric Power Co Ltd
Yangzhou Power Supply Co of Jiangsu Electric Power Co
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Southeast University
State Grid Jiangsu Electric Power Co Ltd
Yangzhou Power Supply Co of Jiangsu Electric Power Co
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses an error risk assessment method and device for an electronic transformer based on a recurrent neural network, which are characterized in that a risk assessment model of the electronic transformer based on the recurrent neural network is trained according to the current and the voltage of the electronic transformer and screened environmental characteristic data; and inputting the characteristic data of the electronic transformer to be evaluated into the model to obtain the predicted specific difference and angular difference of the electronic transformer, and calculating the operation risk index of the electronic transformer according to the predicted specific difference and angular difference. The electronic transformer is modeled by using the sampling current and voltage values of the electronic transformer and combining the environmental characteristic data, the specific difference and the angular difference are predicted, risk evaluation is carried out, and the accuracy of transformer evaluation is verified.

Description

Regression neural network-based error risk assessment method and device for electronic transformer
Technical Field
The invention belongs to the technical field of measurement, and particularly relates to an electronic transformer error risk assessment method and device based on a recurrent neural network.
Background
An electronic transformer is a power distribution device consisting of one or more voltage or current sensors connected to a transmission system and to a secondary converter, for transmitting quantities proportional to the quantities being measured, to measurement instruments, meters and relay protection or control devices. With the development of the intelligent power grid technology in China, the electronic transformer becomes a key device for realizing the digitization of the electrical parameter information in the intelligent power grid.
However, in order to evaluate the error performance most concerned by the electronic instrument transformer, a high-precision standard is required to be provided to calibrate the electronic instrument transformer to be tested, and the high-precision standard is difficult to be put into a high-voltage environment due to the restriction of the operating environment, and can be calibrated only in a short time even if put into an online environment.
Due to the lack of an evaluation means of the operation state of the electronic transformer, namely the measurement error, the potential hidden danger is difficult to find in time, the requirement on the safe and stable operation of the power system is high, if the hidden danger of the electronic transformer is not found in time, the power failure maintenance brings great economic loss, and the satisfaction degree of users is reduced.
At present, electronic transformers of equipment manufacturers have uneven production level and different design methods.
Disclosure of Invention
In order to solve the problems, the invention discloses an electronic transformer error risk assessment method and device based on a recurrent neural network, and solves the problem that the measurement error of an electronic transformer is difficult to assess.
The technical scheme of the invention is as follows: an error risk assessment method of an electronic transformer based on a recurrent neural network,
training a risk assessment model of the electronic transformer based on a recurrent neural network according to the current and the voltage of the electronic transformer and the screened environmental characteristic data;
and inputting the characteristic data of the electronic transformer to be evaluated into the model to obtain the predicted specific difference and angular difference of the electronic transformer, and calculating the operation risk index of the electronic transformer according to the predicted specific difference and angular difference.
Further, the environment feature data includes: ambient temperature, ambient humidity, intelligent substation space magnetic field, electronic transformer operational environment's vibration.
Further, the screening process of the environmental characteristic data of the electronic transformer comprises the following steps:
analyzing the maximum value and the minimum value of each environmental characteristic data, and determining a threshold Th of the difference between the maximum value and the minimum value;
for a certain environmental characteristic, the following conditions are satisfied:
Mmax-Mmin＜Th
Mmax，Mminrespectively, the maximum value and the minimum value of a certain environmental characteristic M.
Further, the regression neural network-based electronic transformer risk assessment model comprises four full-connection layers, wherein the input of the four full-connection layers is the environmental characteristics, current and voltage of the electronic transformer, the real specific difference and angular difference of the electronic transformer are used as labels, and the predicted specific difference and angular difference of the electronic transformer are output.
Further, the electronic transformer operation risk index R is calculated by the following method:
R＝α(|p′|-0.2)/0.2+β(|q′|-10)/10
wherein p 'is a specific difference predicted by the electronic transformer to be evaluated through the model, and q' is an angular difference predicted by the electronic transformer to be evaluated through the model; alpha and beta are weight coefficients.
The utility model provides an electronic transformer error risk assessment device based on recurrent neural network which characterized in that includes:
the electronic transformer risk assessment model training module based on the recurrent neural network is used for training an electronic transformer risk assessment model based on the recurrent neural network according to the current and the voltage of the electronic transformer and the screened environmental characteristic data;
and the electronic transformer operation risk index calculation module is used for inputting the characteristic data of the electronic transformer to be evaluated into the model to obtain the predicted specific difference and angular difference of the electronic transformer and calculating the operation risk index of the electronic transformer according to the predicted specific difference and angular difference.
Further, the environment feature data includes: ambient temperature, ambient humidity, intelligent substation space magnetic field, electronic transformer operational environment's vibration.
Further, the screening process of the environmental characteristic data of the electronic transformer comprises the following steps:
analyzing the maximum value and the minimum value of each environmental characteristic data, and determining a threshold Th of the difference between the maximum value and the minimum value;
for a certain environmental characteristic, the following conditions are satisfied:
Mmax-Mmin＜Th
Mmax，Mminrespectively, the maximum value and the minimum value of a certain environmental characteristic M.
Further, the regression neural network-based electronic transformer risk assessment model comprises four full-connection layers, wherein the input of the four full-connection layers is the environmental characteristics, current and voltage of the electronic transformer, the real specific difference and angular difference of the electronic transformer are used as labels, and the predicted specific difference and angular difference of the electronic transformer are output.
Further, the electronic transformer operation risk index R is calculated by the following method:
R＝α(|p′|-0.2)/0.2+β(|q′|-10)/10
wherein p 'is a specific difference predicted by the electronic transformer to be evaluated through the model, and q' is an angular difference predicted by the electronic transformer to be evaluated through the model; alpha and beta are weight coefficients.
The invention has the beneficial effects that:
1) the electronic transformer is modeled by using the sampling current and voltage values of the electronic transformer and combining environmental characteristic data, the specific difference and the angular difference are predicted, risk evaluation is carried out, and the accuracy of transformer evaluation is verified;
2) according to the invention, by removing invalid data in the environmental characteristic data of the electronic transformer, the effects of improving the value density of the data and optimizing the data access speed are achieved.
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Fig. 1 is a flowchart of an error risk assessment method for an electronic transformer according to an embodiment of the present invention;
FIG. 2 is a predicted scatter plot of the ratio differences according to an embodiment of the present invention;
FIG. 3 is a predicted scatter plot of angular differences in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to specific examples so that those skilled in the art can better understand the present invention and can implement the present invention, but the examples are not intended to limit the present invention.
Example 1:
as shown in fig. 1, an error risk assessment method for an electronic transformer based on a recurrent neural network includes the steps of:
the method comprises the following steps of firstly, screening the effectiveness of environmental characteristic data where an electronic transformer is located to obtain screened effective environmental characteristic data, and specifically comprises the following steps:
1) the environmental characteristic data includes: analyzing the maximum value and the minimum value of each environmental characteristic data to determine a threshold Th of the difference between the maximum value and the minimum value; the value of Th is required to accurately reflect the critical state of unstable environment for subsequent data screening.
2) For a certain environmental characteristic M (temperature, humidity, magnetic field or vibration), it needs to satisfy:
Mmax-Mmin<Th (1)
Mmax，Mminrespectively taking the maximum value and the minimum value of a certain environmental characteristic M, removing all data groups with difference values larger than a threshold value, and eliminating the influence of environmental instability on the operation of the electronic transformer so as to better establish and train a model;
calculating the real specific difference and the angular difference of the electronic transformer according to the current and the voltage of the electronic transformer,
Figure BDA0002214233680000031
JC＝(J-J′)*60 (2)
wherein BC is a specific difference, B is a voltage (current) amplitude measured by the electronic transformer, and B' is a voltage (current) amplitude of the traditional electromagnetic standard transformer, and is dimensionless; JC is the angular difference, J is the voltage (current) phase measured by the electronic transformer, and J' is the voltage (current) phase of the conventional electromagnetic standard transformer, and the unit is minutes.
Secondly, training a risk assessment model of the electronic transformer based on a recurrent neural network according to current and voltage of the electronic transformer and environmental characteristic data;
the electronic transformer risk assessment model based on the recurrent neural network comprises four full-connection layers, four environmental characteristics (temperature, humidity, vibration and magnetic field) and current and voltage of the electronic transformer are input, the real specific difference and the angular difference of the electronic transformer are used as labels, the predicted specific difference and the predicted angular difference of the electronic transformer are output, and the neural network learns all the characteristics of the electronic transformer by adjusting the weight among neurons. Training an electronic transformer risk assessment model based on a recurrent neural network, and specifically comprises the following steps:
1) 70% of the screened valid data are classified as a training set, and the remaining 30% are classified as a testing set. The training set is used for learning the model, the temperature (DEG C), the humidity (RH), the vibration (g) and the magnetic field (Gs) of each group of electronic transformers when working, and current and voltage data are used as input samples of the model, and the labels of the neural network are real specific difference and angular difference.
For neural networks, it is assumed that the l-1 st layer has m neurons, the l-1 st layer has n neurons, k is 1 to m, j is 1 to n,
Figure BDA0002214233680000041
for the output before the activation of the jth neuron of the ith layer, the output after the activation of the jth neuron of the ith layer is
Figure BDA0002214233680000042
Comprises the following steps:
Figure BDA0002214233680000043
wherein the content of the first and second substances,
Figure BDA0002214233680000044
is the weight between the j and k neurons of the l layer,
Figure BDA0002214233680000045
bias for the jth neuron at level i;
the weighting coefficients of layer I form an n x m matrix W, and the bias of layer I forms an n x 1 vector blThe output of layer l-1 constitutes onem x 1 vector al-1The linear output z before the first layer is deactivated forms an n x 1 vector zlThe output a of the l-th layerlThe first step is:
al＝σ(zl)＝σ(Wlal-1+bl) (4)
it can be seen that the output of the l-th layer constitutes an n x 1 vector al。
2) By forward and backward propagation, i.e.
Continuously adjusting the weight of the connection between the neurons by using a gradient descent algorithm until the loss function is reduced to the minimum, and converging the model:
Figure BDA0002214233680000046
wherein, p and q are the real ratio difference and the angular difference calculated in the step (2) of the step one, namely the labels used in the model training,
Figure BDA0002214233680000047
and
Figure BDA0002214233680000048
as predicted ratio and angle differences; training the model to enable the predicted ratio difference and angle difference to be close to the real ratio difference and angle difference in the label;
3) the test set is used to verify the effect of the model. And inputting each group of data (temperature, humidity, vibration, magnetic field, current and voltage of the electronic transformer) of the test set into the model to obtain a predicted specific difference and angular difference, and comparing the predicted specific difference and angular difference with a real angular difference and specific difference calculated by the current and voltage in each group of data of the test set to obtain errors of the specific difference and the angular difference. Here we use the root mean square error in statistics to measure the effectiveness of the model, and if the model is more effective, the error is smaller, and the prediction accuracy of the specific difference and the angular difference is higher. If the model effect is not good, the network depth is deepened, and after the two to three times of operations, one model structure with the minimum error is selected as a well-trained model.
And thirdly, inputting the characteristic data of the electronic transformer to be evaluated into the trained electronic transformer risk evaluation model based on the recurrent neural network to obtain the predicted specific difference and angular difference of the electronic transformer, and calculating the operation risk index of the electronic transformer according to the specific difference and the angular difference.
And inputting the temperature, humidity, vibration, magnetic field, current and voltage data of the electronic transformer to be evaluated into the trained risk evaluation model of the electronic transformer based on the recurrent neural network, so that the ratio difference and the angular difference of the electronic transformer during operation can be predicted, and risk evaluation is carried out through the predicted ratio difference and angular difference. According to the voltage error (specific difference) and phase error (angular difference) limits of the electronic voltage transformer for measurement specified in GB/T2.8407.7-2007, since the data source of the invention requires an accuracy level of 0.2, i.e. the data is not reliable when the specific difference is greater than 0.2% or the angular difference is greater than 10 degrees, the running risk index of the transformer is calculated by multiplying different weight coefficients alpha and beta according to the respective exceeding percentages by the following formula:
R＝α(|p′|-0.2)/0.2+β(|q′|-10)/10 (6)
and p 'is the specific difference predicted by the electronic transformer to be evaluated through the model, and q' is the angular difference predicted by the electronic transformer to be evaluated through the model. Similarly, if the ratio difference and the angle difference are smaller, the risk index of the electronic transformer is lower, and the operation effect is more reliable. On the contrary, if the ratio difference and the angle difference are larger, the risk index of the electronic transformer is higher.
The electronic transformer error risk assessment method solves the problem that the error state of the electronic transformer cannot be assessed without depending on a standard device, and meanwhile, the risk that the electronic transformer measurement data are used for engineering needs to be further assessed. The working environment data (current, voltage, temperature, humidity, vibration and magnetic field) of the electronic transformer is input, so that the ratio difference and the angle difference of the transformer during working can be obtained through prediction, the working reliability of the electronic transformer is indirectly obtained, and the smaller the ratio difference and the angle difference is, the higher the working reliability of the transformer is, and the lower the working reliability of the transformer is otherwise.
As shown in fig. 2 and 3, a scatter diagram is drawn by comparing the actual ratio difference and angular difference in the test set with the ratio difference and angular difference predicted by the neural network, and ideally, the scatter point should approach a straight line passing through the origin, so that the model can achieve a better prediction effect. Then, the risk index of the mutual inductor can be further estimated by using the ratio difference and the angle difference obtained by prediction, if 300 groups of data are sampled, the ratio of the number of each risk level under the real and predicted conditions is calculated, and if the R value is below 5%, no risk exists; if between 5% and 10%, it is a secondary risk; more than 10% is the first order risk. The results can be seen in the following table.
TABLE 1 true and predicted occupation of risk classes
Figure BDA0002214233680000051
Figure BDA0002214233680000061
The invention provides an error risk assessment method of an electronic transformer based on a recurrent neural network, so that the screened effective data is learned, and the operation ratio difference and the angle difference of the electronic transformer can be predicted under the condition of no standard device. On the basis, the operation reliability and the risk of the electronic transformer are further evaluated by analyzing the predicted ratio difference and the angle difference.
Example 2:
an electronic transformer error risk assessment device based on a recurrent neural network comprises:
the electronic transformer risk assessment model training module based on the recurrent neural network is used for training an electronic transformer risk assessment model based on the recurrent neural network according to the current and the voltage of the electronic transformer and the screened environmental characteristic data;
and the electronic transformer operation risk index calculation module is used for inputting the characteristic data of the electronic transformer to be evaluated into the model to obtain the predicted specific difference and angular difference of the electronic transformer and calculating the operation risk index of the electronic transformer according to the predicted specific difference and angular difference.
The environmental characteristic data includes: ambient temperature, ambient humidity, intelligent substation space magnetic field, electronic transformer operational environment's vibration.
The screening process of the environmental characteristic data of the electronic transformer comprises the following steps:
analyzing the maximum value and the minimum value of each environmental characteristic data, and determining a threshold Th of the difference between the maximum value and the minimum value;
for a certain environmental characteristic, the following conditions are satisfied:
Mmax-Mmin＜Th
Mmax，Mminrespectively, the maximum value and the minimum value of a certain environmental characteristic M.
The electronic transformer risk assessment model based on the recurrent neural network comprises four full-connection layers, wherein the input of the four full-connection layers is the environmental characteristics, current and voltage of the electronic transformer, the real specific difference and angular difference of the electronic transformer are used as labels, and the predicted specific difference and angular difference of the electronic transformer are output.
The electronic transformer operation risk index R is calculated by the following steps:
R＝α(|p′|-0.2)/0.2+β(|q′|-10)/10
wherein p 'is a specific difference predicted by the electronic transformer to be evaluated through the model, and q' is an angular difference predicted by the electronic transformer to be evaluated through the model; alpha and beta are weight coefficients.
The above is only a preferred embodiment of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (10)

1. An electronic transformer error risk assessment method based on a recurrent neural network is characterized in that,
training a risk assessment model of the electronic transformer based on a recurrent neural network according to the current and the voltage of the electronic transformer and the screened environmental characteristic data;
and inputting the characteristic data of the electronic transformer to be evaluated into the model to obtain the predicted specific difference and angular difference of the electronic transformer, and calculating the operation risk index of the electronic transformer according to the predicted specific difference and angular difference.
2. The method for evaluating the error risk of the electronic transformer based on the recurrent neural network as claimed in claim 1, wherein the environmental characteristic data comprises: ambient temperature, ambient humidity, intelligent substation space magnetic field, electronic transformer operational environment's vibration.
3. The method for evaluating the error risk of the electronic transformer based on the recurrent neural network as claimed in claim 1, wherein the screening process of the environmental characteristic data of the electronic transformer comprises:
analyzing the maximum value and the minimum value of each environmental characteristic data, and determining a threshold Th of the difference between the maximum value and the minimum value;
for a certain environmental characteristic, the following conditions are satisfied:
Mmax-Mmin<Th
Mmax，Mminrespectively, the maximum value and the minimum value of a certain environmental characteristic M.
4. The method for evaluating the error risk of the electronic transformer based on the recurrent neural network as claimed in claim 1, wherein the electronic transformer risk evaluation model based on the recurrent neural network comprises four fully connected layers, the input is the environmental characteristics and the current and the voltage of the electronic transformer, the real specific difference and the angular difference of the electronic transformer are used as labels, and the predicted specific difference and the angular difference of the electronic transformer are output.
5. The method for evaluating the error risk of the electronic transformer based on the recurrent neural network as claimed in claim 1, wherein the operation risk index R of the electronic transformer is calculated by:
R＝α(|p′|-0.2)/0.2+β(|q′|-10)/10
wherein p 'is a specific difference predicted by the electronic transformer to be evaluated through the model, and q' is an angular difference predicted by the electronic transformer to be evaluated through the model; alpha and beta are weight coefficients.
6. The utility model provides an electronic transformer error risk assessment device based on recurrent neural network which characterized in that includes:
the electronic transformer risk assessment model training module based on the recurrent neural network is used for training an electronic transformer risk assessment model based on the recurrent neural network according to the current and the voltage of the electronic transformer and the screened environmental characteristic data;
and the electronic transformer operation risk index calculation module is used for inputting the characteristic data of the electronic transformer to be evaluated into the model to obtain the predicted specific difference and angular difference of the electronic transformer and calculating the operation risk index of the electronic transformer according to the predicted specific difference and angular difference.
7. The regression neural network-based electronic transformer error risk assessment device according to claim 6, wherein the environmental characteristic data comprises: ambient temperature, ambient humidity, intelligent substation space magnetic field, electronic transformer operational environment's vibration.
8. The regression neural network-based electronic transformer error risk assessment device according to claim 6, wherein the screening process of the environmental characteristic data of the electronic transformer is as follows:
analyzing the maximum value and the minimum value of each environmental characteristic data, and determining a threshold Th of the difference between the maximum value and the minimum value;
for a certain environmental characteristic, the following conditions are satisfied:
Mmax-Mmin<Th
Mmax，Mminrespectively, the maximum value and the minimum value of a certain environmental characteristic M.
9. The regression neural network-based electronic transformer error risk assessment device according to claim 6, wherein the regression neural network-based electronic transformer risk assessment model comprises four fully-connected layers, the input of the four fully-connected layers is the environmental characteristics, the current and the voltage of the electronic transformer, the real specific difference and the angular difference of the electronic transformer are used as labels, and the predicted specific difference and the angular difference of the electronic transformer are output.
10. The regression neural network-based error risk assessment device for the electronic transformer according to claim 6, wherein the operation risk index R of the electronic transformer is calculated by the following method:
R＝α(|p′|-0.2)/0.2+β(|q′|-10)/10
wherein p 'is a specific difference predicted by the electronic transformer to be evaluated through the model, and q' is an angular difference predicted by the electronic transformer to be evaluated through the model; alpha and beta are weight coefficients.
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