CN115293663A - Bus unbalance rate abnormity detection method, system and device - Google Patents
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Abstract
The invention provides a method, a system and a device for detecting the unbalance rate abnormality of a bus, wherein the method comprises the following steps: extracting normal data of the unbalance rate of the bus; inputting normal data of the bus unbalance rate as a training sample into a stacked self-encoder model for model training; after the model training is finished, carrying out data coding on all training samples to generate coded data, and calculating average coded data; calculating an abnormal judgment threshold value according to the training samples and the average coded data; carrying out data coding on the bus imbalance rate data to be detected through a stacked self-coder model to generate coded data to be detected; calculating Euclidean distance between coded data to be detected and average coded data to serve as detection distance data; and comparing the detected distance data with an abnormality determination threshold value to determine the abnormality of the unbalance rate of the bus. According to the invention, the data analysis is automatically carried out by utilizing the stacked self-encoder model, the abnormity of the bus imbalance rate can be timely found, and the detection efficiency and accuracy are improved.
Description
Technical Field
The invention relates to the technical field of power data monitoring, in particular to a method, a system and a device for detecting the abnormal imbalance rate of a bus.
Background
The difference value between the input electric quantity and the output electric quantity of the substation bus is called unbalanced electric quantity, and the ratio of the unbalanced electric quantity to the input electric quantity is called a bus unbalance rate. The abnormal bus imbalance rate not only reflects the abnormal operation condition of the substation equipment, but also reflects the real-time performance and the accuracy of the metering device in the station. The statistics and analysis of the unbalance rate of the bus electricity quantity are carried out, and the statistics and analysis are important means for finding out equipment faults and metering abnormality in the station.
At the present stage, the abnormity of the bus unbalance rate is only counted and calculated by the electric energy acquisition system, and on-site investigation is carried out according to the calculation result, so that the investigation workload is large, and more manpower and material resources are consumed. Moreover, the field investigation is greatly influenced, and the situation that the reason cannot be found is easy to occur, so how to automatically realize the abnormal monitoring of the unbalance rate of the bus is an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide a method, a system and a device for detecting the unbalance rate abnormality of a bus, aiming at the problems in the prior art.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a bus unbalance rate abnormity detection method comprises the following steps:
extracting normal data of the bus unbalance rate of backup in the electric energy data acquisition system;
inputting normal data of the bus unbalance rate as a training sample into a stacked self-encoder model for model training;
after the model training is finished, carrying out data coding on all training samples to generate coded data, and calculating average coded data;
calculating an abnormal judgment threshold value according to the training samples and the average coded data;
reading the unbalance rate data of the bus to be detected in the electric energy data acquisition system;
carrying out data coding on bus imbalance rate data to be detected through a stacked self-coder model to generate coded data to be detected;
calculating Euclidean distances between coded data to be detected and average coded data to serve as detection distance data;
and comparing the detected distance data with an abnormality judgment threshold value, and if the detected distance data is greater than the abnormality judgment threshold value, judging that the imbalance rate of the bus is abnormal.
Further, the inputting the normal data of the bus imbalance rate as a training sample into the stacked self-encoder model for model training includes:
inputting normal data of the bus unbalance rate into an encoder of a stacked self-encoder model for precoding;
after pre-coding is finished, carrying out noise addition processing by using a BART noise function, and taking processed data as a training sample;
inputting training samples into a stacked self-encoder model, and performing model optimization by using an optimizer of the stacked self-encoder model through a gradient descent method;
when the loss function of the optimized stacked self-encoder model is stable, the decoder is removed.
Further, after the model training is completed, data encoding is performed on all training samples to generate encoded data, and average encoded data is calculated, including:
performing data coding on all training samples by using a stack type self-coder model, and extracting corresponding coded data;
the average value of all the encoded data is calculated as average encoded data.
Further, the calculating an anomaly determination threshold according to the training samples and the average encoded data includes:
respectively calculating Euclidean distances of the training samples and the average coded data to be used as reference distances;
the average and standard deviation of all the reference distances are calculated, and the average is summed with 5 times of the standard deviation to be calculated as an abnormality determination threshold.
Correspondingly, the invention also discloses a system for detecting the imbalance rate abnormity of the bus, which comprises the following components:
the data extraction unit is used for extracting the normal data of the bus unbalance rate of the backup in the electric energy data acquisition system;
the model training unit is used for inputting normal data of the bus unbalance rate as training samples into the stacked self-encoder model for model training;
the computing unit is used for carrying out data coding on all the training samples to generate coded data and computing average coded data;
a threshold value generation unit for calculating an abnormality determination threshold value from the training samples and the average encoded data;
the data reading unit is used for reading the data of the unbalance rate of the bus to be detected in the electric energy data acquisition system;
the encoding unit is used for carrying out data encoding on the bus imbalance rate data to be detected through the stacked self-encoder model to generate encoded data to be detected;
the data processing unit is used for calculating Euclidean distances between coded data to be detected and average coded data to serve as detection distance data;
and the judging unit is used for comparing the detection distance data with the abnormity judging threshold value, and if the detection distance data is larger than the abnormity judging threshold value, the bus unbalance rate is abnormal.
Further, the model training unit includes:
the pre-coding module is used for inputting the normal data of the bus unbalance rate into a coder of the stacked self-coder model for pre-coding;
the noise adding module is used for performing noise adding processing by utilizing a BART noise function and taking the processed data as a training sample;
the model optimization module is used for inputting the training samples into the stacked self-encoder model and optimizing the model by using an optimizer of the stacked self-encoder model through a gradient descent method;
and the removing module is used for removing the decoder when the loss function of the optimized stacked self-encoder model is stable.
Further, the computing unit is specifically configured to:
performing data coding on all training samples by using a stack type self-coder model, and extracting corresponding coded data;
the average value of all encoded data is calculated as average encoded data.
Further, the threshold generating unit is specifically configured to:
respectively calculating Euclidean distances of the training samples and the average coded data to serve as reference distances;
the average and standard deviation of all the reference distances are calculated, and the average is summed with 5 times of the standard deviation to be calculated as an abnormality determination threshold.
Correspondingly, the invention also discloses a device for detecting the abnormal imbalance rate of the bus, which comprises the following components:
a memory for storing a computer program;
a processor for implementing the steps of the bus imbalance abnormality detection method according to any one of the above when executing the computer program.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a method, a system and a device for detecting the abnormal unbalance rate of a bus. And then, converting the data of the bus imbalance rate to be detected by using the trained model, and judging whether the bus imbalance rate is abnormal or not by comparing the data with an abnormal judgment threshold value. According to the invention, the data analysis is automatically carried out by utilizing the stacked self-encoder model, the abnormity of the unbalance rate of the bus can be found in time, and the detection efficiency and accuracy are effectively improved.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a process flow diagram of an embodiment of the present invention.
Fig. 2 is a system block diagram of an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.
The method for detecting the abnormal imbalance rate of the bus as shown in fig. 1 comprises the following steps:
s1: and extracting the normal data of the bus unbalance rate of the backup in the electric energy data acquisition system.
S2: and inputting normal data of the bus unbalance rate as a training sample into the stacked self-encoder model for model training.
Firstly, normal data of the bus imbalance rate is input into an encoder of a stacked self-encoder model for precoding. And after the pre-coding is finished, carrying out noise addition processing by using a BART noise function, and taking the processed data as training samples. Then, the training samples are input into a stacked self-encoder model, and the model optimization is carried out by a gradient descent method by using an optimizer of the stacked self-encoder model. And when the loss function of the optimized stacked self-encoder model is stable, removing the decoder.
S3: and after the model training is finished, performing data coding on all training samples to generate coded data, and calculating average coded data.
Specifically, after model training is completed, data coding is performed on all training samples by using the trained stacked self-encoder model, and corresponding coded data is extracted. Then, the average value of all the encoded data is calculated as average encoded data.
S4: and calculating an anomaly judgment threshold value according to the training samples and the average coded data.
The specific calculation process is as follows:
first, euclidean distances of the training samples and the average encoded data are calculated as reference distances. At this time, the average and standard deviation of all the reference distances are calculated, and the average is summed with 5 times of the standard deviation to be calculated as the abnormality determination threshold.
S5: and reading the unbalance rate data of the bus to be detected in the electric energy data acquisition system.
S6: and carrying out data coding on the bus imbalance rate data to be detected through the stacked self-coder model to generate coded data to be detected.
S7: and calculating Euclidean distances between the coded data to be detected and the average coded data to be used as detection distance data.
S8: and comparing the detected distance data with an abnormality judgment threshold value, and if the detected distance data is larger than the abnormality judgment threshold value, judging that the unbalance rate of the bus is abnormal.
Correspondingly, as shown in fig. 2, the present invention also discloses a system for detecting an abnormal imbalance rate of a bus, comprising: the device comprises a data extraction unit 1, a model training unit 2, a calculation unit 3, a threshold generation unit 4, a data reading unit 5, an encoding unit 6, a data processing unit 7 and a judgment unit 8.
And the data extraction unit 1 is used for extracting the backup normal data of the bus unbalance rate in the electric energy data acquisition system.
And the model training unit 2 is used for inputting the normal data of the bus unbalance rate as a training sample into the stacked self-encoder model for model training.
And the calculating unit 3 is used for carrying out data coding on all the training samples to generate coded data and calculating average coded data.
Specifically, the computing unit 3 first performs data encoding on all training samples by using the stacked self-encoder model, and extracts corresponding encoded data. Then, the average value of all the encoded data is calculated as average encoded data.
A threshold value generating unit 4 for calculating an abnormality determination threshold value from the training samples and the average encoded data. The threshold generation unit 4 is specifically configured to: respectively calculating Euclidean distances of the training samples and the average coded data to serve as reference distances; the average and standard deviation of all the reference distances are calculated, and the average is summed with 5 times of the standard deviation to be calculated as an abnormality determination threshold.
And the data reading unit 5 is used for reading the data of the imbalance rate of the bus to be detected in the electric energy data acquisition system.
And the coding unit 6 is used for performing data coding on the bus imbalance rate data to be detected through the stacked self-coder model to generate coded data to be detected.
And a data processing unit 7 for calculating euclidean distances of the encoded data to be detected and the average encoded data as detected distance data.
And the judging unit 8 is used for comparing the detection distance data with an abnormality judgment threshold value, and if the detection distance data is larger than the abnormality judgment threshold value, the bus imbalance rate is abnormal.
As an embodiment of the present invention, the model training unit 2 includes: a pre-coding module 21, a noise adding module 22, a model optimizing module 23 and a removing module 24.
And the pre-coding module 21 is configured to input the normal data of the bus imbalance rate into a coder of the stacked self-coder model for pre-coding.
And a noise adding module 22, configured to perform noise adding processing by using a BART noise function, and use the processed data as a training sample.
And the model optimization module 23 is configured to input the training samples into the stacked self-encoder model, and perform model optimization by using an optimizer of the stacked self-encoder model through a gradient descent method.
And a removing module 24, configured to remove the decoder when the loss function of the optimized stacked self-encoder model is stable.
Correspondingly, the invention also discloses a device for detecting the abnormal imbalance rate of the bus, which comprises the following components:
a memory for storing a computer program;
a processor for implementing the steps of the bus imbalance rate abnormality detection method according to any one of the above items when the computer program is executed.
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention. The same and similar parts among the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
In the embodiments provided by the present invention, it should be understood that the disclosed system, system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional 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, systems 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 position, or may be distributed on multiple 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 modules in the embodiments of the present invention may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit.
Similarly, each processing unit in the embodiments of the present invention may be integrated into one functional module, or each processing unit may exist physically, or two or more processing units are integrated into one functional module.
The invention is further described with reference to the accompanying drawings and specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and these equivalents also fall within the scope of the present application.
Claims (9)
1. A bus unbalance rate abnormity detection method is characterized by comprising the following steps:
extracting normal data of the bus unbalance rate of backup in the electric energy data acquisition system;
inputting normal data of the bus unbalance rate as training samples into a stacked self-encoder model for model training;
after the model training is finished, carrying out data coding on all training samples to generate coded data, and calculating average coded data;
calculating an abnormal judgment threshold value according to the training samples and the average coded data;
reading the data of the unbalance rate of the bus to be detected in the electric energy data acquisition system;
carrying out data coding on bus imbalance rate data to be detected through a stacked self-coder model to generate coded data to be detected;
calculating Euclidean distances between coded data to be detected and average coded data to serve as detection distance data;
and comparing the detected distance data with an abnormality judgment threshold value, and if the detected distance data is larger than the abnormality judgment threshold value, judging that the unbalance rate of the bus is abnormal.
2. The method for detecting the abnormal bus imbalance rate according to claim 1, wherein the step of inputting the normal data of the bus imbalance rate as a training sample into a stacked self-encoder model for model training comprises:
inputting normal data of the bus unbalance rate into an encoder of a stacked self-encoder model for precoding;
after precoding is finished, carrying out noise addition processing by using a BART noise function, and taking processed data as training samples;
inputting training samples into a stacked self-encoder model, and performing model optimization by using an optimizer of the stacked self-encoder model through a gradient descent method;
and when the loss function of the optimized stacked self-encoder model is stable, removing the decoder.
3. The bus bar imbalance rate abnormality detection method according to claim 2, wherein after the model training is completed, data encoding is performed on all training samples to generate encoded data, and average encoded data is calculated, and the method includes:
performing data coding on all training samples by using a stack type self-coder model, and extracting corresponding coded data;
the average value of all encoded data is calculated as average encoded data.
4. The method for detecting bus imbalance rate abnormality according to claim 3, wherein the calculating an abnormality determination threshold value from the training samples and the average encoded data includes:
respectively calculating Euclidean distances of the training samples and the average coded data to serve as reference distances;
the average and standard deviation of all the reference distances are calculated, and the average and 5 times of the standard deviation are summed to calculate as an abnormality determination threshold.
5. A system for detecting bus imbalance abnormality, comprising:
the data extraction unit is used for extracting the normal data of the bus unbalance rate of the backup in the electric energy data acquisition system;
the model training unit is used for inputting normal data of the bus unbalance rate into the stacked self-encoder model as training samples to perform model training;
the computing unit is used for carrying out data coding on all the training samples to generate coded data and computing average coded data;
a threshold value generation unit for calculating an abnormality determination threshold value from the training samples and the average encoded data;
the data reading unit is used for reading the data of the unbalance rate of the bus to be detected in the electric energy data acquisition system;
the encoding unit is used for carrying out data encoding on the bus imbalance rate data to be detected through the stacked self-encoder model to generate encoded data to be detected;
the data processing unit is used for calculating Euclidean distances between coded data to be detected and average coded data to serve as detection distance data;
and the judging unit is used for comparing the detection distance data with the abnormity judging threshold value, and if the detection distance data is larger than the abnormity judging threshold value, the bus unbalance rate is abnormal.
6. The bus bar imbalance rate anomaly detection system of claim 5, wherein the model training unit comprises:
the pre-coding module is used for inputting the normal data of the bus unbalance rate into a coder of the stacked self-coder model for pre-coding;
the noise adding module is used for performing noise adding processing by utilizing a BART noise function and taking the processed data as a training sample;
the model optimization module is used for inputting the training samples into the stacked self-encoder model and optimizing the model by using an optimizer of the stacked self-encoder model through a gradient descent method;
and the removing module is used for removing the decoder when the loss function of the optimized stacked self-encoder model is stable.
7. The system for detecting bus imbalance rate anomaly according to claim 6, wherein the computing unit is specifically configured to:
performing data coding on all training samples by using a stack type self-coder model, and extracting corresponding coded data;
the average value of all encoded data is calculated as average encoded data.
8. The system for detecting bus imbalance rate abnormality according to claim 7, wherein the threshold generation unit is specifically configured to:
respectively calculating Euclidean distances of the training samples and the average coded data to be used as reference distances;
the average and standard deviation of all the reference distances are calculated, and the average is summed with 5 times of the standard deviation to be calculated as an abnormality determination threshold.
9. A bus unbalance rate abnormality detection device is characterized by comprising:
a memory for storing a computer program;
a processor for implementing the steps of the bus imbalance ratio anomaly detection method according to any one of claims 1 to 4 when executing said computer program.
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