CN111308355A - Transformer substation storage battery state detection and analysis method based on deep learning - Google Patents
Transformer substation storage battery state detection and analysis method based on deep learning Download PDFInfo
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- CN111308355A CN111308355A CN202010178245.9A CN202010178245A CN111308355A CN 111308355 A CN111308355 A CN 111308355A CN 202010178245 A CN202010178245 A CN 202010178245A CN 111308355 A CN111308355 A CN 111308355A
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- 238000012549 training Methods 0.000 claims abstract description 24
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- 238000007405 data analysis Methods 0.000 claims abstract description 17
- 230000000007 visual effect Effects 0.000 claims abstract description 7
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/0084—Arrangements for measuring currents or voltages or for indicating presence or sign thereof measuring voltage only
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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Abstract
The invention relates to the technical field of transformer substation storage battery state monitoring, in particular to a transformer substation storage battery state detection and analysis method based on deep learning, which comprises the following steps of: a. firstly, acquiring parameters of a storage battery pack and a single battery of a transformer substation in real time by using a data acquisition module; b. the data processing module is used for preprocessing the uploaded original parameter data; c. training a storage battery state prediction model by using the divided training set data; d. deploying the trained model in a data analysis module; e. displaying the result of the data analysis in a visual interface; the invention realizes the real-time monitoring of the storage battery state of the transformer substation, provides an alarm when the detected storage battery parameters are abnormal, can judge the fault reason and point out the fault position through analysis, and simultaneously provides corresponding fault processing suggestions, thereby providing a basis for the maintenance of a direct current system of the transformer substation and providing powerful support and guarantee for the safe and reliable operation of the transformer substation.
Description
Technical Field
The invention relates to the technical field of transformer substation storage battery state monitoring, in particular to a transformer substation storage battery state detection and analysis method based on deep learning.
Background
In the power grid industry, a storage battery is an important component of a direct-current power supply of a transformer substation, and is widely applied to the transformer substation as a backup power supply, and the reliability of the storage battery directly influences the safe operation of the power grid. Once a problem occurs in the dc system, the battery pack can be used as a backup power source to supply power to the load, so the operation state of the battery pack is very important. In actual use, the phenomena of deformation of a battery shell, leakage of electrolyte, insufficient capacity, uneven terminal voltage of the battery and the like can occur in the storage battery of the transformer substation, and if the phenomena cannot be found in time and huge hidden dangers can be buried, the timing detection and the online monitoring of the storage battery of the transformer substation are very important and necessary. And the realization of the health condition and state evaluation of the storage battery pack is always a difficult problem.
In order to solve the problems, the invention provides a transformer substation storage battery state detection and analysis system based on deep learning by combining the prior art, so that the comprehensive judgment of the running state of the current storage battery pack is realized, the failure reason is analyzed and given when the storage battery is abnormal, and the technical support is provided for direct current operation and maintenance staff.
Disclosure of Invention
In order to solve the deficiencies in the above technical problems, the present invention aims to: the method for detecting and analyzing the state of the storage battery of the transformer substation in deep learning is provided, the current state of the storage battery is obtained through monitoring and analyzing various parameters of the storage battery, and technical support is provided for stable operation of a direct current system of the transformer substation.
The technical scheme adopted by the invention for solving the technical problem is as follows:
the transformer substation storage battery state detection and analysis method based on deep learning comprises the following steps:
a. firstly, acquiring voltage, internal resistance state, storage battery capacity and storage battery environment temperature parameters of a storage battery pack and a single battery of a transformer substation in real time by using a data acquisition module;
b. the data processing module is used for preprocessing the uploaded original parameter data, sorting and storing the parameter data of each time point, uploading the parameter data to the data analysis module, and supporting the derivation of data for classifying a data set and dividing a training set and a test set;
c. training the storage battery state prediction model by using the divided training set data, testing and adjusting model parameters;
d. deploying the trained model in a data analysis module, realizing the analysis of data transmitted by a data processing module, and predicting the state of the current storage battery pack;
e. and the result of the data analysis is displayed in a visual interface, the state information parameters of the storage battery are visually given, and the current condition of the storage battery is explained in detail.
Preferably, the data acquisition module in step a is to acquire parameter data of a storage battery pack of the substation by using a sensor: the storage battery pack of the transformer substation, the voltage and internal resistance state of the single battery, the capacity of the storage battery and the ambient temperature of the storage battery are transmitted to the data processing module in a group, and all parameters measured at the same time point are transmitted to the data processing module in a group.
Preferably, the data processing module in step b is configured to sort the acquired data, store the parameter data measured at the same time point as a group, derive the stored data to classify the data as a training set, classify the data set into a normal data set and an abnormal data set by professional personnel, classify the cause of the fault according to specific abnormal parameters in the abnormal data set, ensure high quality and reliability of the data set, and then randomly classify the processed data set into training data and test data according to a proportion. The storage of the collected data is stored in a json format file, and the storage structure is as follows:
{‘battery voltage’:**,’cell voltage’:**,’internal resistance’:**,’battery capacity’:**,’temperature’:**}
the battery voltage (substation battery pack voltage), the cell voltage (cell voltage), the internal resistance (battery internal resistance), the battery capacity, and the temperature (battery ambient temperature) are included.
The analyzable fault reasons comprise common fault reasons such as unqualified storage battery capacity, open circuit of single batteries, short circuit of storage battery, water loss of storage battery, grid corrosion deformation, softening of active substances and the like.
Preferably, the data analysis module in step d is that a substation storage battery state analysis model is deployed in the module, the input data is storage battery state parameter data of each group acquired by the acquisition module, and the state information of the storage battery at this time, that is, the storage battery is in a healthy state or an abnormal state, and if the state information is the abnormal state, an abnormal reason is given.
Preferably, the visual interface in the step e means that an analysis result given by the data analysis module is displayed on the interface, and the specific display contents are detection parameters, whether the storage battery is abnormal or not and reasons of the abnormality of the storage battery, so that a worker can simply and clearly know the state information of the storage battery of the substation in real time and can timely and correctly process the state information of the storage battery when the state of the storage battery is abnormal.
Preferably, the substation storage battery state analysis model adopts a Wide & Deep model and consists of a linear model and a DNN part, the training method of the model is mini-batch stochastic optimization, wherein the Wide part is subjected to FTRL (Follow-the-regulated-leader) algorithm + L1 regularized learning, the memory capacity is efficiently realized by utilizing cross features, the Deep part is subjected to learning by using an AdaGrad optimization algorithm, the generalization capacity of the model is realized by the learned low-density latitude vectors, and the outputs of the Wide and Deep parts are combined together in a weighting mode and finally output through a logistic loss function. The analysis result has higher accuracy and expansibility, the classified training set data is used for training and learning the model to obtain the storage battery state analysis model, and the model can effectively realize the analysis of the storage battery state.
Compared with the prior art, the invention has the following beneficial effects:
the invention realizes the real-time monitoring of the storage battery state of the transformer substation, provides an alarm when the detected storage battery parameters are abnormal, can judge the fault reason and point out the fault position through analysis, and simultaneously provides corresponding fault processing suggestions, thereby providing a basis for the maintenance of a direct current system of the transformer substation and providing powerful support and guarantee for the safe and reliable operation of the transformer substation.
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FIG. 1 is a block diagram illustrating a process in the present invention.
Detailed Description
The following further describes embodiments of the present invention:
example 1
As shown in fig. 1, the method for detecting and analyzing the state of a storage battery of a transformer substation based on deep learning according to the present invention includes the following steps:
a. firstly, acquiring voltage, internal resistance state, storage battery capacity and storage battery environment temperature parameters of a storage battery pack and a single battery of a transformer substation in real time by using a data acquisition module; the data acquisition module in the step a is used for acquiring various parameter data of a storage battery pack of the transformer substation by using a sensor: the storage battery pack of the transformer substation, the voltage and internal resistance state of the single battery, the capacity of the storage battery and the ambient temperature of the storage battery are transmitted to the data processing module in a group, and all parameters measured at the same time point are transmitted to the data processing module in a group.
b. The data processing module is used for preprocessing the uploaded original parameter data, sorting and storing the parameter data of each time point, uploading the parameter data to the data analysis module, and supporting the derivation of data for classifying a data set and dividing a training set and a test set; the data processing module in the step b is used for sorting the acquired data, storing the parameter data measured at the same time point into a group, simultaneously deriving the stored data and classifying the data as a training set for use, dividing the data set into a normal data set and an abnormal data set by professional workers, completing the classification of fault reasons according to specific abnormal parameters in the abnormal data set, ensuring the high quality and reliability of the data set, and then dividing the processed data set into the training data and the test data for use according to proportion at random. The storage of the collected data is stored in a json format file, and the storage structure is as follows:
{‘battery voltage’:**,’cell voltage’:**,’internal resistance’:**,’battery capacity’:**,’temperature’:**}
the battery voltage (substation battery pack voltage), the cell voltage (cell voltage), the internal resistance (battery internal resistance), the battery capacity, and the temperature (battery ambient temperature) are included.
The analyzable fault reasons comprise common fault reasons such as unqualified storage battery capacity, open circuit of single batteries, short circuit of storage battery, water loss of storage battery, grid corrosion deformation, softening of active substances and the like.
c. Training the storage battery state prediction model by using the divided training set data, testing and adjusting model parameters;
d. deploying the trained model in a data analysis module, realizing the analysis of data transmitted by a data processing module, and predicting the state of the current storage battery pack; the data analysis module in the step d is that a substation storage battery state analysis model is deployed in the module, input data are storage battery state parameter data of each group acquired by the acquisition module, and state information of the storage battery at the moment is output, namely the storage battery is in a healthy state or an abnormal state, and if the state information is the abnormal state, an abnormal reason is given.
e. And the result of the data analysis is displayed in a visual interface, the state information parameters of the storage battery are visually given, and the current condition of the storage battery is explained in detail. And e, the visual interface in the step e is used for displaying an analysis result given by the data analysis module on the interface, wherein the specific display contents are detection parameters, abnormality or non-abnormality reasons and abnormality reasons of the storage battery, so that a worker can simply and clearly know the state information of the storage battery of the transformer substation in real time and can timely and correctly process the state information of the storage battery when the state of the storage battery is abnormal. The transformer substation storage battery state analysis model adopts a Wide & Deep model and consists of a linear model and a DNN part, the training method of the model is mini-batch storage optimization, wherein the Wide part is regularized learning by an FTRL (Follow-the-regularized-leader) algorithm and L1, the memory capacity is efficiently realized by utilizing cross characteristics, the Deep part is learned by an AdaGrad optimization algorithm, the generalization capacity of the model is realized by the learned low-latitude dense vectors, and the outputs of the Wide and Deep parts are combined together in a weighting mode and finally output through a logistic loss function. The analysis result has higher accuracy and expansibility, the classified training set data is used for training and learning the model to obtain the storage battery state analysis model, and the model can effectively realize the analysis of the storage battery state.
Claims (6)
1. A transformer substation storage battery state detection and analysis method based on deep learning is characterized by comprising the following steps:
a. firstly, acquiring voltage, internal resistance state, storage battery capacity and storage battery environment temperature parameters of a storage battery pack and a single battery of a transformer substation in real time by using a data acquisition module;
b. the data processing module is used for preprocessing the uploaded original parameter data, sorting and storing the parameter data of each time point, uploading the parameter data to the data analysis module, and supporting the derivation of data for classifying a data set and dividing a training set and a test set;
c. training the storage battery state prediction model by using the divided training set data, testing and adjusting model parameters;
d. deploying the trained model in a data analysis module, realizing the analysis of data transmitted by a data processing module, and predicting the state of the current storage battery pack;
e. and the result of the data analysis is displayed in a visual interface, the state information parameters of the storage battery are visually given, and the current condition of the storage battery is explained in detail.
2. The transformer substation storage battery state detection and analysis method based on deep learning of claim 1, wherein the data acquisition module in step a is used for acquiring various parameter data of a transformer substation storage battery pack by using a sensor: the storage battery pack of the transformer substation, the voltage and internal resistance state of the single battery, the capacity of the storage battery and the ambient temperature of the storage battery are transmitted to the data processing module in a group, and all parameters measured at the same time point are transmitted to the data processing module in a group.
3. The transformer substation storage battery state detection and analysis method based on deep learning of claim 1, wherein the data processing module in the step b is used for sorting the acquired data, storing parameter data measured at the same time point into a group, meanwhile, deriving the stored data to classify the data into a training set for use, dividing the data set into a normal data set and an abnormal data set by professional personnel, completing classification of fault reasons according to specific abnormal parameters in the abnormal data set, ensuring high quality and reliability of the data set, and then dividing the processed data set into training data and test data for use at random according to a proportion.
4. The transformer substation storage battery state detection and analysis method based on deep learning of claim 1, wherein the data analysis module in the step d is that a transformer substation storage battery state analysis model is deployed in the module, the input data is storage battery state parameter data of each group acquired by the acquisition module, and the state information of the storage battery at the time is output, namely the storage battery is in a healthy state or an abnormal state, and if the state information is the abnormal state, an abnormal reason is given.
5. The transformer substation storage battery state detection and analysis method based on deep learning of claim 1, wherein the visual interface in the step e is that an analysis result given by the data analysis module is displayed on the interface, and the specific display contents are detection parameters, whether the storage battery is abnormal or not and reasons of the abnormality of the storage battery, so that a worker can know the state information of the transformer substation storage battery simply and clearly in real time and can perform timely and correct processing when the state of the storage battery is abnormal.
6. The transformer substation storage battery state detection and analysis method based on Deep learning of claim 4 is characterized in that the transformer substation storage battery state analysis model adopts a Wide & Deep model, and consists of a linear model and a DNN part, the training method of the model is mini-batch storage optimization, wherein the Wide part is FTRL (Follow-the-regulated-leader) algorithm + L1 regularization learning, the memory capacity is efficiently realized by utilizing cross features, the Deep part is learned by using an AdaGrad optimization algorithm, the generalization capacity of the model is realized by the learned low dense vectors, and the outputs of the Wide and Deep parts are combined together in a weighting mode and finally output through logistic function.
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| CN111856299A (en) * | 2020-07-29 | 2020-10-30 | 中国联合网络通信集团有限公司 | Method, device and equipment for determining power supply state |
| CN112256755A (en) * | 2020-10-20 | 2021-01-22 | 中电科新型智慧城市研究院有限公司福州分公司 | Student abnormal behavior analysis method based on deep learning |
| CN112946497A (en) * | 2020-12-04 | 2021-06-11 | 广东电网有限责任公司 | Storage battery fault diagnosis method and device based on fault injection deep learning |
| CN112993421A (en) * | 2021-03-12 | 2021-06-18 | 深圳市雷铭科技发展有限公司 | BMS management method and system based on BIM model and electronic equipment |
| CN113065416A (en) * | 2021-03-16 | 2021-07-02 | 深圳供电局有限公司 | Leakage monitoring device integrated with transformer substation video monitoring device, method and medium |
| CN113219341A (en) * | 2021-03-23 | 2021-08-06 | 陈九廷 | Model generation and battery degradation estimation device, method, medium, and apparatus |
| CN113740752A (en) * | 2021-08-27 | 2021-12-03 | 济南大学 | Lithium battery life prediction method based on battery model parameters |
| CN115249403A (en) * | 2022-07-27 | 2022-10-28 | 湖北清江水电开发有限责任公司 | Drainage basin step power plant water and rain condition early warning system and early warning method |
| CN115693916A (en) * | 2022-09-07 | 2023-02-03 | 国网安徽省电力有限公司宿州供电公司 | Intelligent online monitoring method and system for direct-current power supply of transformer substation |
| CN116338500A (en) * | 2023-05-26 | 2023-06-27 | 南京志卓电子科技有限公司 | Rail transit vehicle storage battery operation monitoring system |
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| CN116338500A (en) * | 2023-05-26 | 2023-06-27 | 南京志卓电子科技有限公司 | Rail transit vehicle storage battery operation monitoring system |
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Application publication date: 20200619 |