A fault diagnosis method for photovoltaic power plants based on an enhanced BP-Bagging algorithm
In response to the challenge of sample data imbalance in fault diagnosis methods for photovoltaic power plants based on machine learning， the paper proposes a fault diagnosis method leveraging an enhanced BP-Bagging algorithm. Firstly， a mapping relationship between photovoltaic data and fault types...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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zhejiang electric power
2024-03-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=1567cb79-f88d-480b-909e-977282797c77 |
| _version_ | 1827300309661122560 |
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| author | QI Weiwen ZHANG Jun WU Yang FAN Qiang ZHAO Feng CHEN Jianguo WANG Jian |
| author_facet | QI Weiwen ZHANG Jun WU Yang FAN Qiang ZHAO Feng CHEN Jianguo WANG Jian |
| author_sort | QI Weiwen |
| collection | DOAJ |
| description | In response to the challenge of sample data imbalance in fault diagnosis methods for photovoltaic power plants based on machine learning， the paper proposes a fault diagnosis method leveraging an enhanced BP-Bagging algorithm. Firstly， a mapping relationship between photovoltaic data and fault types is established using a BP neural network to achieve fault diagnosis in photovoltaic systems. Subsequently， the Bagging algorithm is enhanced by utilizing random under-sampling （RUS） to address the issue of class imbalance in samples. Furthermore， to tackle the problem of overfitting in the BP network， the paper introduces a fault diagnosis model for photovoltaic power plants based on the enhanced BP-Bagging. This involves parallel training of multiple BP networks and determining fault diagnosis results through a voting method. Finally， the paper conducts comparative experiments with different algorithms， calculates evaluation metrics related to model accuracy， and validates that the proposed method demonstrates high overall performance. To a certain extent， it effectively mitigates the challenge of sample class imbalance in fault diagnosis of photovoltaic power plants， thereby improving the accuracy of fault diagnosis in such systems. |
| first_indexed | 2024-04-24T16:01:20Z |
| format | Article |
| id | doaj.art-2848afd732c34faebc3580968fb67563 |
| institution | Directory Open Access Journal |
| issn | 1007-1881 |
| language | zho |
| last_indexed | 2024-04-24T16:01:20Z |
| publishDate | 2024-03-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj.art-2848afd732c34faebc3580968fb675632024-04-01T07:39:02Zzhozhejiang electric powerZhejiang dianli1007-18812024-03-01433657410.19585/j.zjdl.2024030081007-1881（2024）03-0065-10A fault diagnosis method for photovoltaic power plants based on an enhanced BP-Bagging algorithmQI Weiwen0ZHANG Jun1WU Yang2FAN Qiang3ZHAO Feng4CHEN Jianguo5WANG Jian6State Grid Shaoxing Power Supply Company, Shaoxing, Zhejiang 312362, ChinaState Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310007, ChinaState Grid Shaoxing Power Supply Company, Shaoxing, Zhejiang 312362, ChinaState Grid Shaoxing Power Supply Company, Shaoxing, Zhejiang 312362, ChinaState Grid Shaoxing Power Supply Company, Shaoxing, Zhejiang 312362, ChinaState Grid Shaoxing Power Supply Company, Shaoxing, Zhejiang 312362, ChinaCollege of Energy and Electrical Engineering, Hohai University, Nanjing 211100, ChinaIn response to the challenge of sample data imbalance in fault diagnosis methods for photovoltaic power plants based on machine learning， the paper proposes a fault diagnosis method leveraging an enhanced BP-Bagging algorithm. Firstly， a mapping relationship between photovoltaic data and fault types is established using a BP neural network to achieve fault diagnosis in photovoltaic systems. Subsequently， the Bagging algorithm is enhanced by utilizing random under-sampling （RUS） to address the issue of class imbalance in samples. Furthermore， to tackle the problem of overfitting in the BP network， the paper introduces a fault diagnosis model for photovoltaic power plants based on the enhanced BP-Bagging. This involves parallel training of multiple BP networks and determining fault diagnosis results through a voting method. Finally， the paper conducts comparative experiments with different algorithms， calculates evaluation metrics related to model accuracy， and validates that the proposed method demonstrates high overall performance. To a certain extent， it effectively mitigates the challenge of sample class imbalance in fault diagnosis of photovoltaic power plants， thereby improving the accuracy of fault diagnosis in such systems.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=1567cb79-f88d-480b-909e-977282797c77photovoltaic power stationfault diagnosisrandom under-samplingensemble learning |
| spellingShingle | QI Weiwen ZHANG Jun WU Yang FAN Qiang ZHAO Feng CHEN Jianguo WANG Jian A fault diagnosis method for photovoltaic power plants based on an enhanced BP-Bagging algorithm Zhejiang dianli photovoltaic power station fault diagnosis random under-sampling ensemble learning |
| title | A fault diagnosis method for photovoltaic power plants based on an enhanced BP-Bagging algorithm |
| title_full | A fault diagnosis method for photovoltaic power plants based on an enhanced BP-Bagging algorithm |
| title_fullStr | A fault diagnosis method for photovoltaic power plants based on an enhanced BP-Bagging algorithm |
| title_full_unstemmed | A fault diagnosis method for photovoltaic power plants based on an enhanced BP-Bagging algorithm |
| title_short | A fault diagnosis method for photovoltaic power plants based on an enhanced BP-Bagging algorithm |
| title_sort | fault diagnosis method for photovoltaic power plants based on an enhanced bp bagging algorithm |
| topic | photovoltaic power station fault diagnosis random under-sampling ensemble learning |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=1567cb79-f88d-480b-909e-977282797c77 |
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