CN112819067A - Method, device, equipment and storage medium for processing bad data of power distribution network - Google Patents

Method, device, equipment and storage medium for processing bad data of power distribution network Download PDF

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CN112819067A
CN112819067A CN202110120172.2A CN202110120172A CN112819067A CN 112819067 A CN112819067 A CN 112819067A CN 202110120172 A CN202110120172 A CN 202110120172A CN 112819067 A CN112819067 A CN 112819067A
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曾顺奇
吴杰康
余方明
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The application discloses a method, a device, equipment and a storage medium for processing bad data of a power distribution network, which are used for acquiring measurement data in a preset time period in the power distribution network; inputting the measured data into a preset identification model for data classification, and outputting the measured data as normal data or bad data; determining the occurrence time t of bad data in the measured data based on the identification result, and inputting a preset number of normal data before the occurrence time t of the bad data into a preset data correction model to obtain predicted data at the time t; and replacing the bad data at the time t with the predicted data at the time t to obtain corrected measurement data. The method solves the technical problems that after the bad data are identified by adopting a convolutional neural network model, the rejected bad data are not supplemented or corrected, so that the data are incomplete, the observability of a power distribution network system is influenced, and the reliability of a power distribution network state estimation result is reduced.

Description

Method, device, equipment and storage medium for processing bad data of power distribution network
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing bad data of a power distribution network.
Background
In recent years, a power distribution network system realizes a leap development, and meanwhile, the requirements of residents on the power supply quality, reliability and the like of a power grid are increasingly strict. However, because the real-time measurement system is not perfect, the measured data such as node voltage, line circuit, branch power, etc. obtained from the data acquisition and monitoring System (SCADA) may have errors, repetitions, deletions, etc., that is, bad data may appear in the obtained data. The existence of bad data can generate adverse effect on the running state of the power distribution network system, and further the performance of the power distribution network system is affected.
The traditional bad data identification method is mainly based on a state estimation method, and comprises a residual error search method, a non-quadratic criterion method, a zero residual error method, an estimation identification method and the like. These methods usually take weighted residuals or standard residuals as feature values, assume that they obey a certain probability distribution, and then set a threshold value according to a certain confidence level to perform hypothesis testing. After the position of the suspicious data is determined, the suspicious data are removed or weakened from the measured data set, and finally a new state estimation is generated. However, the methods have the common defects that residual pollution and residual inundation are easy to occur, so that missed detection or false detection is caused, and the identification effect is greatly reduced.
In order to solve the problems of the state estimation method, the prior art adopts a convolutional neural network to identify bad data. After a neural network model is trained, a result of identifying most types of measurement errors can be rapidly generated, and a safe and reliable data warehouse is formed in a control center. However, in the prior art, after the bad data is identified, the rejected bad data is not supplemented or corrected, so that the data is incomplete, the observability of a power distribution network system is influenced, and the reliability of a power distribution network state estimation result is reduced.
Disclosure of Invention
The application provides a processing method, a processing device, processing equipment and a storage medium for bad data of a power distribution network, and the processing device, the processing equipment and the storage medium are used for solving the technical problems that after the bad data are identified by a convolutional neural network model, the bad data after being eliminated are not supplemented or corrected, so that the data are incomplete, the observability of the power distribution network system is influenced, and the reliability of a power distribution network state estimation result is reduced.
In view of this, the first aspect of the present application provides a method for processing bad data of a power distribution network, including:
acquiring measurement data in a preset time period in a power distribution network, wherein the measurement data comprises node voltage, line current and branch power;
inputting the measured data into a preset identification model for data classification, and outputting an identification result of the measured data, wherein the identification result of the measured data comprises normal data and bad data;
determining the occurrence time t of bad data in the measured data based on the identification result, and inputting a preset number of normal data before the occurrence time t of the bad data into a preset data correction model to obtain predicted data at the time t;
and replacing the bad data at the moment t with the predicted data at the moment t to obtain the corrected measurement data.
Optionally, the inputting the measured data into a preset identification model for data classification, and outputting the identification result of the measured data further includes:
preprocessing the measurement data through a preset formula, wherein the preset formula is as follows:
Figure BDA0002921728150000021
wherein,
Figure BDA0002921728150000022
for the j-th dimension data in the i-th measurement data,
Figure BDA0002921728150000023
the j-th dimension data in the ith type of measured data after preprocessing is obtained; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，NiThe total dimensionality of the data in the ith type of measured data.
Optionally, the configuration process of the preset identification model is as follows:
acquiring a measured data training set, wherein the label of each training sample in the measured data training set is the normal data or the bad data;
inputting the measured data training set into a preset convolutional neural network for training, and outputting a prediction result corresponding to each training sample;
calculating a loss value through a loss function based on the prediction result corresponding to each training sample and the label;
and updating the parameters of the preset convolutional neural network by a gradient descent method based on the loss value until the loss value is lower than a preset threshold value, so as to obtain the preset identification model.
Optionally, the preset data correction model is a Q learning-long and short term memory neural network model;
correspondingly, the inputting a preset number of normal data before the time t when the bad data appears into a preset data correction model to obtain the predicted data at the time t includes:
inputting n normal data before the time t when the bad data appears into the Q learning-long short-term memory neural network model, enabling the Q learning-long short-term memory neural network model to process the n normal data based on the hidden layer state and the unit state of the previous n times t-1, t-2, t.
Optionally, the determining, based on the identification result, a time t when bad data occurs in the measured data, inputting a preset number of normal data before the time t when the bad data occurs to a preset data correction model to obtain predicted data at the time t, and then further including:
inputting the prediction data at the moment t into the preset identification model for data classification, and outputting the identification result of the prediction data at the moment t;
correspondingly, the replacing the bad data at the time t with the predicted data at the time t to obtain the corrected measurement data includes:
and when the identification result of the predicted data at the moment t is the normal data, replacing the bad data at the moment t with the predicted data at the moment t to obtain the corrected measured data.
This application second aspect provides a processing apparatus of distribution network bad data, includes:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring measurement data in a preset time period in the power distribution network, and the measurement data comprises node voltage, line current and branch power;
the first classification unit is used for inputting the measured data into a preset identification model for data classification and outputting an identification result of the measured data, wherein the identification result of the measured data comprises normal data and bad data;
the input unit is used for determining the occurrence time t of bad data in the measured data based on the identification result, and inputting a preset number of normal data before the occurrence time t of the bad data into a preset data correction model to obtain predicted data at the time t;
and the replacing unit is used for replacing the bad data at the moment t with the predicted data at the moment t to obtain the corrected measurement data.
Optionally, the method further includes:
the preprocessing unit is used for preprocessing the measured data through a preset formula, wherein the preset formula is as follows:
Figure BDA0002921728150000041
wherein,
Figure BDA0002921728150000042
for the j-th dimension data in the i-th measurement data,
Figure BDA0002921728150000043
the j-th dimension data in the ith type of measured data after preprocessing is obtained; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，NiThe total dimensionality of the data in the ith type of measured data.
Optionally, the method further includes:
the second classification unit is used for inputting the prediction data at the moment t into the preset identification model for data classification and outputting the identification result of the prediction data at the moment t;
correspondingly, the replacement unit is specifically configured to:
and when the identification result of the predicted data at the moment t is the normal data, replacing the bad data at the moment t with the predicted data at the moment t to obtain the corrected measured data.
The third aspect of the application provides a processing device for bad data of a power distribution network, which comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is used for executing the processing method of the bad data of the power distribution network according to any one of the first aspect according to the instructions in the program code.
A fourth aspect of the present application provides a computer-readable storage medium, which is used for storing program codes, wherein the program codes are used for executing the processing method of bad data of the power distribution network according to any one of the first aspects.
According to the technical scheme, the method has the following advantages:
the application provides a processing method of bad data of a power distribution network, which comprises the following steps: acquiring measurement data in a preset time period in a power distribution network, wherein the measurement data comprises node voltage, line current and branch power; inputting the measured data into a preset identification model for data classification, and outputting an identification result of the measured data, wherein the identification result of the measured data comprises normal data and bad data; determining the occurrence time t of bad data in the measured data based on the identification result, and inputting a preset number of normal data before the occurrence time t of the bad data into a preset data correction model to obtain predicted data at the time t; and replacing the bad data at the time t with the predicted data at the time t to obtain corrected measurement data.
According to the method, after the preset identification model is adopted to identify the bad data in the measured data, the preset number of normal data before the time t when the bad data appears are input into the preset data correction model to predict the normal measured data at the time t, the predicted data at the time t are obtained, then the predicted data at the time t is used for replacing the bad data at the time t, the corrected measured data are obtained, the bad data are eliminated, and the integrity of the data is also ensured.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced 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 that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a method for processing bad data of a power distribution network according to an embodiment of the present application;
fig. 2 is another schematic flow chart of a method for processing bad data of a power distribution network according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a processing apparatus for bad data of a power distribution network according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a preset identification model according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a Q learning-long short term memory neural network model according to an embodiment of the present application.
Detailed Description
The application provides a processing method, a processing device, processing equipment and a storage medium for bad data of a power distribution network, and the processing device, the processing equipment and the storage medium are used for solving the technical problems that after the bad data are identified by a convolutional neural network model, the bad data after being eliminated are not supplemented or corrected, so that the data are incomplete, the observability of the power distribution network system is influenced, and the reliability of a power distribution network state estimation result is reduced.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For easy understanding, please refer to fig. 1, an embodiment of a method for processing bad data of a power distribution network provided in the present application includes:
step 101, measurement data in a preset time period in a power distribution network are obtained, wherein the measurement data comprise node voltage, line current and branch power.
Acquiring historical and real-time data of node voltage in the power distribution network for one year from a data acquisition and monitoring system, and constructing a node voltage data matrix:
Figure BDA0002921728150000061
in the formula, V (i, t) is a measured voltage value of the ith node in the distribution network at time t, i is 1, 2. T is 1,2, T is the total number of sampling times per day; the number of data samples N is 365 because 365 days a year.
Acquiring historical and real-time data of line current in the power distribution network for one year, and constructing a line current data matrix:
Figure BDA0002921728150000062
in the formula, I (L, t) is a measured current value of the first line in the power distribution network at time t, wherein L is 1, 2. T is 1,2, T is the total number of sampling times per day; the number of data samples N is 365.
Acquiring historical and real-time data of branch power in the power distribution network for one year, and constructing a branch power data matrix:
Figure BDA0002921728150000071
in the formula, P (K, t) is a measured power value of the kth branch in the power distribution network at time t, K is 1,2, and K is the total number of branches in the power distribution network; t is 1,2, T is the total number of sampling times per day; the number of data samples N is 365.
And 102, inputting the measured data into a preset identification model for data classification, and outputting an identification result of the measured data, wherein the identification result of the measured data comprises normal data and bad data.
The preset identification module is used for identifying whether the input measurement data is normal data or bad data, and includes a plurality of convolution layers, a plurality of pooling layers, a full-link layer and a Softmax classifier, please refer to fig. 4. The preset identification module processes the input measurement data as follows:
1) and (3) rolling layers: the convolution layer carries out convolution operation to each dimension data of input through a plurality of convolution kernel effect successive layers to obtain every layer of convolution layer's different characteristic data through nonlinear activation function, export through the last layer of convolution kernel of combination, and then can extract the contact successive layer of hiding in data inside, the concrete calculation process of convolution layer is:
Figure BDA0002921728150000072
in the formula,
Figure BDA0002921728150000073
the output of the mth layer convolution layer of the jth dimension data in the ith type measurement data; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，p＝1,2,…,Ni，NiThe total dimension of the data in the ith type measured data;
Figure BDA0002921728150000074
inputting the mth layer convolution layer of jth dimension data in the ith type measurement data;
Figure BDA0002921728150000075
the m-th layer convolution kernel is the p-th dimension data and the j-th dimension data in the ith type of measurement data;
Figure BDA0002921728150000076
the offset of the mth layer convolution layer of the jth dimension data in the ith class data.
2) A pooling layer: the pooling layer is a dimension reduction operation, namely, the preset identification model can pay more attention to the features rather than specific positions of the features, so that the learning process comprises more degrees of freedom and can tolerate the micro displacement of some features; and dimension reduction can be carried out on the features, so that the model can extract wider features, the parameter quantity in the learning process of the preset identification model is reduced, and the calculation process of the pooling layer is as follows:
Figure BDA0002921728150000077
in the formula,
Figure BDA0002921728150000078
the output of the mth layer pooling layer of the jth dimension data in the ith type of measurement data; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，NiMeasuring the total dimension of the data in the data set of the ith type;
Figure BDA0002921728150000081
the weight of the mth layer pooling layer of the jth dimension data in the ith type of measurement data; down (#) is a down-sampling function;
Figure BDA0002921728150000082
inputting the mth layer pooling layer of the jth dimension data in the ith type of measurement data;
Figure BDA0002921728150000083
bias of mth layer pooling layer for jth dimension data of ith type of metrology data.
3) Full connection layer: the full-connection layer performs full-connection on the characteristics output by the last convolution layer, and the specific processing process is as follows:
Figure BDA0002921728150000084
in the formula,
Figure BDA0002921728150000085
the j-dimension characteristic vector in the i-type measurement data is obtained; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，NiThe total dimension of the data in the ith type measured data; b0Is an offset; omega0Is a weight; f (-) is the activation function.
4) Softmax classifier: the fully-connected output is processed to identify the type of the various measurement data.
Furthermore, considering that certain correlation and difference exist between input measurement data, and the measurement data are not acquired from the same equipment, and thus have different physical meanings and dimensions, if proper processing is not performed, the identification effect of the preset identification model is affected. Therefore, in order to eliminate dimensions, weaken the physical significance of data, weaken the individual difference of the data, and balance the data dimension, so that the data are in a similar scale, the embodiment of the present application preprocesses the measured data, and before inputting the measured data into a preset identification model for data classification and outputting the identification result of the measured data, the measured data can be preprocessed by a preset formula, wherein the preset formula is as follows:
Figure BDA0002921728150000086
wherein,
Figure BDA0002921728150000087
for the j-th dimension data in the i-th measurement data,
Figure BDA0002921728150000088
the j-th dimension data in the ith type of measured data after preprocessing is obtained; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，NiThe total dimensionality of the data in the ith type of measured data.
Further, the configuration process of the preset identification model comprises the following steps:
and A1, acquiring a measured data training set, wherein the label of each training sample in the measured data training set is normal data or poor data.
The data format of the training sample is the same as the format of the measured data to be tested, and is different in that the training sample is labeled, i.e., normal data or bad data is known for the training sample. Assume that a training set of metrology data is obtained as
Figure BDA0002921728150000091
Contains N types of training samples, wherein dj∈RL×1The training sample is labeled
Figure BDA0002921728150000092
Yj∈[1,N]。
And A2, inputting the measured data training set into a preset convolutional neural network for training, and outputting a prediction result corresponding to each training sample.
Before the measured data training set is input to the preset convolutional neural network for training, the measured data training set may be preprocessed, and the specific process of preprocessing may refer to the preprocessing process in the foregoing steps, which is not described herein again.
The training sample is subjected to feature extraction and processing through a convolutional layer, a pooling layer and a full-connection layer in a preset convolutional neural network, and a Softmax classifier predicts the probability P (Y) of the training sample in a certain classj＝N|dj) The specific calculation process is as follows:
Figure BDA0002921728150000093
wherein λ ═ λ1,λ2,…,λN]TAnd characterizing the parameter vector of the Softmax classifier, wherein U is a final prediction result, and the sum of all prediction results is 1.
A3, calculating a loss value by a loss function based on the prediction result and the label corresponding to each training sample.
The loss function is:
Figure BDA0002921728150000094
wherein j is 1,2, …, Ni，NiMeasuring the total dimensionality of the data in the data training set for the ith type; 1 {. is an indicative function, i.e. 1 when the class attribute is correct, and 0 otherwise; delta is an attenuation coefficient, and certain parameters of the classifier are set to be 0, so that the generalization capability is improved; m is 1,2, …, M is preset convolution spiritAnd outputting the total layer number through the network.
And A4, updating the parameters of the preset convolutional neural network by a gradient descent method based on the loss value until the loss value is lower than a preset threshold value, and obtaining a preset identification model.
And updating the parameters of the preset convolutional neural network by a gradient descent method based on the loss value until the loss value J (lambda) is lower than a preset threshold value, and taking the latest parameter-updated preset convolutional neural network as a preset identification model.
And 103, determining the occurrence time t of the bad data in the measured data based on the identification result, and inputting a preset number of normal data before the occurrence time t of the bad data into a preset data correction model to obtain predicted data at the time t.
The preset data modification model is a Q learning-long short term memory neural network model, please refer to fig. 5. Inputting n normal data before the time t when the bad data appears into a Q learning-long and short term memory neural network model, enabling the Q learning-long and short term memory neural network model to process the n normal data based on the hidden layer state and the unit state of the previous n times t-1, t-2, t. The specific processing process of the Q learning-long and short term memory neural network model on the input data is as follows:
1) determining the occurrence time t of bad data in the measured data based on the identification result, and acquiring n normal data before the occurrence time t of the bad data
Figure BDA0002921728150000101
And obtaining an input vector which corresponds to one input unit of the long and short term memory network in the Q learning-long and short term memory neural network model.
2)[ht-1,Ct-1]，[ht-2,Ct-2]，…，[ht-n,Ct-n]Respectively the current time input vector in the long-short term memory neural network
Figure BDA0002921728150000102
Last moment hidden layer state ht-1，ht-2，…，ht-nAnd cell state C at the previous timet-1，Ct-2，…，Ct-n. The hidden layer state and the unit state correspond to the other two input units of the long-term and short-term memory neural network, the interior of the hidden layer state and the unit state is composed of three gates, namely a forgetting gate FtInput door ItAnd an output gate OtTo control the discarding and inheriting of information, the calculation formulas of the three gates are respectively as follows:
Figure BDA0002921728150000103
Figure BDA0002921728150000104
Figure BDA0002921728150000105
wherein, sigma is a Sigmoid activation function; wF，WI，WORespectively are weight matrixes of a forgetting gate, an input gate and an output gate;
Figure BDA0002921728150000106
indicating that layer state h is to be hiddent-1And an input vector
Figure BDA0002921728150000107
Connecting to obtain a new vector; b isF、BI、BORespectively are offset vectors of the forgetting gate, the input gate and the output gate.
3) In the Q learning mechanism, Qk[(ht-1,Ct-1)k,ak]，Qk[(ht-2,Ct-2)k,ak]，…，Qk[(ht-n,Ct-n)k,ak]Are respectively a passing state [ ht-1,Ct-1]k，[ht-2,Ct-2]k，…，[ht-n,Ct-n]kAnd select action akThereafter, a cumulative prize expectation is obtained. Wherein, akRefers to the step length (control action) corresponding to the hidden layer state and the unit state during the state update in the kth iteration. The specific expressions of the cumulative prize expectation are as follows:
Figure BDA0002921728150000111
Figure BDA0002921728150000112
...
Figure BDA0002921728150000113
wherein, alpha is a learning factor; r ((h)t-i,Ct-i)k,(ht-i,Ct-i)k+1,ak) I is 1,2, …, n +1 is the immediate reward function value; a is an action set; gamma is a discount factor.
4)Q*[(ht-1,Ct-1)r,ar]，Q*[(ht-2,Ct-2)r,ar]，…，Q*[(ht-n,Ct-n)r,ar]All adopt an optimal strategy pi*The corresponding maximum accumulated reward expectation value is expressed as follows:
Q*[(ht-1,Ct-1)r,ar]＝maxQπ[(ht-1,Ct-1)k,ak]；
Q*[(ht-2,Ct-2)r,ar]＝maxQπ[(ht-2,Ct-2)k,ak]；
...
Q*[(ht-n,Ct-n)r,ar＝maxQπ[(ht-n,Ct-n)k,ak]；
in the formula, pi is all strategy sets, and r belongs to kmax，kmaxAnd representing the iteration times corresponding to the optimal strategy as the maximum iteration times. Policy refers to performing action arAnd then, a series of actions which maximize the accumulated reward expectation value are taken, and if the accumulated reward expectation value obtained by the actions is maximum, the actions are called an optimal strategy.
5)[ht-1,Ct-1]best，[ht-2,Ct-2]best，…，[ht-n,Ct-n]bestThe optimal hidden layer state and the optimal unit state before the time t in the long-short term memory neural network are respectively processed through the full connection layer, so that the optimal prediction result of the node voltage/line current/branch power at the time t can be calculated, and the prediction data at the time t can be obtained.
And 104, replacing the bad data at the time t with the predicted data at the time t to obtain corrected measurement data.
And replacing the bad data at the moment t with the predicted data at the moment t to obtain the complete corrected measurement data.
In the embodiment of the application, after the preset identification model is adopted to identify the bad data in the measured data, the preset number of normal data before the time t when the bad data appears is input into the preset data correction model to predict the normal measured data at the time t to obtain the predicted data at the time t, then the predicted data at the time t is used for replacing the bad data at the time t to obtain the corrected measured data, namely the bad data is eliminated, and the integrity of the data is also ensured.
The foregoing is an embodiment of a method for processing bad data of a power distribution network provided by the present application, and the following is another embodiment of a method for processing bad data of a power distribution network provided by the present application.
Referring to fig. 2, a method for processing bad data of a power distribution network according to an embodiment of the present application includes:
step 201, measurement data in a preset time period in the power distribution network are obtained, wherein the measurement data comprise node voltage, line current and branch power.
Step 202, inputting the measured data into a preset identification model for data classification, and outputting an identification result of the measured data, wherein the identification result of the measured data comprises normal data and bad data.
And 203, determining the occurrence time t of the bad data in the measured data based on the identification result, and inputting a preset number of normal data before the occurrence time t of the bad data into a preset data correction model to obtain the predicted data at the time t.
The specific contents of steps 201 to 203 are the same as those of steps 101 to 103, and the details of steps 201 to 203 are not repeated herein.
And 204, inputting the prediction data at the moment t into a preset identification model for data classification, and outputting an identification result of the prediction data at the moment t.
And step 205, when the identification result of the predicted data at the time t is normal data, replacing the bad data at the time t with the predicted data at the time t to obtain corrected measured data.
And inputting the predicted data at the time t into a preset identification model again for identification so as to determine whether the predicted data predicted by the preset data correction model is normal or not, so as to achieve the purpose of checking the predicted data, if the predicted data is normal data, replacing the bad data at the time t with the predicted data at the time t to obtain corrected measured data, if the predicted data is bad data, indicating that the predicted data still has problems, discarding the data correspondingly (namely n input data are changed into n +1 data), and then predicting again until the data are normal and then taking the data as the corrected data.
In the embodiment of the application, after identifying bad data in measured data by adopting a preset identification model, inputting a preset number of normal data before the time t when the bad data appears into a preset data correction model to predict normal measured data at the time t to obtain predicted data at the time t, and then replacing the bad data at the time t with the predicted data at the time t to obtain corrected measured data, namely the bad data are eliminated and the integrity of the data is ensured, so that the technical problems that after identifying the bad data by adopting a convolutional neural network model in the prior art, the bad data after elimination is not supplemented or corrected, the data is incomplete, the observability of a power distribution network system is influenced, and the reliability of a power distribution network state estimation result is reduced are solved;
furthermore, after the prediction data at the time t is obtained, the prediction data is input into a preset identification model to check the prediction data, whether the prediction data is normal or not is determined, if the prediction data is normal, the prediction data replaces the bad data at the time t, and the reliability of the corrected measurement data is further ensured.
The foregoing is another embodiment of the method for processing bad data of the power distribution network provided by the present application, and the following is an embodiment of a device for processing bad data of the power distribution network provided by the present application.
Referring to fig. 3, an apparatus for processing bad data of a power distribution network according to an embodiment of the present application includes:
an obtaining unit 301, configured to obtain measurement data in a preset time period in a power distribution network, where the measurement data includes node voltage, line current, and branch power;
a first classification unit 302, configured to input the measurement data into a preset identification model for data classification, and output an identification result of the measurement data, where the identification result of the measurement data includes normal data and bad data;
an input unit 303, configured to determine a time t when bad data occurs in the measured data based on the identification result, and input a preset number of normal data before the time t when the bad data occurs to the preset data correction model to obtain predicted data at the time t;
a replacing unit 304, configured to replace the bad data at the time t with the predicted data at the time t, and obtain corrected measurement data.
As a further improvement, the method further comprises the following steps:
the preprocessing unit 305 is configured to preprocess the measurement data according to a preset formula, where the preset formula is:
Figure BDA0002921728150000141
wherein,
Figure BDA0002921728150000142
for the j-th dimension data in the i-th measurement data,
Figure BDA0002921728150000143
the j-th dimension data in the ith type of measured data after preprocessing is obtained; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，NiThe total dimensionality of the data in the ith type of measured data.
As a further improvement, the method further comprises the following steps:
the second classification unit 306 is configured to input the prediction data at the time t to a preset identification model for data classification, and output an identification result of the prediction data at the time t;
accordingly, the substitution unit 304 is specifically configured to:
and when the identification result of the predicted data at the time t is normal data, replacing the bad data at the time t with the predicted data at the time t to obtain corrected measured data.
In the embodiment of the application, after the preset identification model is adopted to identify the bad data in the measured data, the preset number of normal data before the time t when the bad data appears is input into the preset data correction model to predict the normal measured data at the time t to obtain the predicted data at the time t, then the predicted data at the time t is used for replacing the bad data at the time t to obtain the corrected measured data, namely the bad data is eliminated, and the integrity of the data is also ensured.
The embodiment of the application also provides processing equipment for the bad data of the power distribution network, and the equipment comprises a processor and a memory;
the memory is used for storing the program codes and transmitting the program codes to the processor;
the processor is used for executing the processing method of the bad data of the power distribution network in the processing method embodiment of the bad data of the power distribution network according to the instructions in the program codes.
The embodiment of the application also provides a computer-readable storage medium, which is used for storing a program code, and the program code is used for executing the processing method of the bad data of the power distribution network in the processing method embodiment of the bad data of the power distribution network.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or 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 unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for executing all or part of the steps of the method described in the embodiments of the present application through a computer device (which may be a personal computer, a server, or a network device). And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above 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; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. A processing method for bad data of a power distribution network is characterized by comprising the following steps:
acquiring measurement data in a preset time period in a power distribution network, wherein the measurement data comprises node voltage, line current and branch power;
inputting the measured data into a preset identification model for data classification, and outputting an identification result of the measured data, wherein the identification result of the measured data comprises normal data and bad data;
determining the occurrence time t of bad data in the measured data based on the identification result, and inputting a preset number of normal data before the occurrence time t of the bad data into a preset data correction model to obtain predicted data at the time t;
and replacing the bad data at the moment t with the predicted data at the moment t to obtain the corrected measurement data.
2. The method for processing bad data of the power distribution network according to claim 1, wherein the step of inputting the measured data into a preset identification model for data classification and outputting the identification result of the measured data further comprises:
preprocessing the measurement data through a preset formula, wherein the preset formula is as follows:
Figure FDA0002921728140000011
wherein,
Figure FDA0002921728140000012
for the j-th dimension data in the i-th measurement data,
Figure FDA0002921728140000013
is the i-th class quantity after the pretreatmentMeasuring j-th dimension data in the data; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，NiThe total dimensionality of the data in the ith type of measured data.
3. The method for processing the bad data of the power distribution network according to claim 1, wherein the configuration process of the preset identification model is as follows:
acquiring a measured data training set, wherein the label of each training sample in the measured data training set is the normal data or the bad data;
inputting the measured data training set into a preset convolutional neural network for training, and outputting a prediction result corresponding to each training sample;
calculating a loss value through a loss function based on the prediction result corresponding to each training sample and the label;
and updating the parameters of the preset convolutional neural network by a gradient descent method based on the loss value until the loss value is lower than a preset threshold value, so as to obtain the preset identification model.
4. The method for processing the bad data of the power distribution network according to claim 1, wherein the preset data modification model is a Q learning-long short term memory neural network model;
correspondingly, the inputting a preset number of normal data before the time t when the bad data appears into a preset data correction model to obtain the predicted data at the time t includes:
inputting n normal data before the time t when the bad data appears into the Q learning-long short-term memory neural network model, enabling the Q learning-long short-term memory neural network model to process the n normal data based on the hidden layer state and the unit state of the previous n times t-1, t-2, t.
5. The method for processing the bad data of the power distribution network according to claim 1, wherein the determining a time t of occurrence of the bad data in the measured data based on the identification result inputs a preset number of normal data before the time t of occurrence of the bad data into a preset data correction model to obtain predicted data of the time t, and then further comprises:
inputting the prediction data at the moment t into the preset identification model for data classification, and outputting the identification result of the prediction data at the moment t;
correspondingly, the replacing the bad data at the time t with the predicted data at the time t to obtain the corrected measurement data includes:
and when the identification result of the predicted data at the moment t is the normal data, replacing the bad data at the moment t with the predicted data at the moment t to obtain the corrected measured data.
6. A processing apparatus of distribution network bad data, characterized by comprising:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring measurement data in a preset time period in the power distribution network, and the measurement data comprises node voltage, line current and branch power;
the first classification unit is used for inputting the measured data into a preset identification model for data classification and outputting an identification result of the measured data, wherein the identification result of the measured data comprises normal data and bad data;
the input unit is used for determining the occurrence time t of bad data in the measured data based on the identification result, and inputting a preset number of normal data before the occurrence time t of the bad data into a preset data correction model to obtain predicted data at the time t;
and the replacing unit is used for replacing the bad data at the moment t with the predicted data at the moment t to obtain the corrected measurement data.
7. The device for processing the bad data of the power distribution network according to claim 6, further comprising:
the preprocessing unit is used for preprocessing the measured data through a preset formula, wherein the preset formula is as follows:
Figure FDA0002921728140000031
wherein,
Figure FDA0002921728140000032
for the j-th dimension data in the i-th measurement data,
Figure FDA0002921728140000033
the j-th dimension data in the ith type of measured data after preprocessing is obtained; i is 1,2 and 3, which respectively represent node voltage, line current and branch power; j is 1,2, …, Ni，NiThe total dimensionality of the data in the ith type of measured data.
8. The device for processing the bad data of the power distribution network according to claim 6, further comprising:
the second classification unit is used for inputting the prediction data at the moment t into the preset identification model for data classification and outputting the identification result of the prediction data at the moment t;
correspondingly, the replacement unit is specifically configured to:
and when the identification result of the predicted data at the moment t is the normal data, replacing the bad data at the moment t with the predicted data at the moment t to obtain the corrected measured data.
9. The device for processing the bad data of the power distribution network is characterized by comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is used for executing the processing method of the bad data of the power distribution network according to any one of claims 1 to 5 according to the instructions in the program code.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium is used for storing program codes, and the program codes are used for executing the processing method of bad data of the power distribution network according to any one of claims 1-5.
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