| Autor: |
QI Weiwen, ZHANG Jun, WU Yang, FAN Qiang, ZHAO Feng, CHEN Jianguo, WANG Jian |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Zhejiang dianli, Vol 43, Iss 3, Pp 65-74 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1007-1881 |
| DOI: |
10.19585/j.zjdl.202403008 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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