A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm
Photovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array’s electrical behavior characteristics under fault conditions is analyzed in...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-01-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/1/238 |
| _version_ | 1811304516897734656 |
|---|---|
| author | Qiang Zhao Shuai Shao Lingxing Lu Xin Liu Honglu Zhu |
| author_facet | Qiang Zhao Shuai Shao Lingxing Lu Xin Liu Honglu Zhu |
| author_sort | Qiang Zhao |
| collection | DOAJ |
| description | Photovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array’s electrical behavior characteristics under fault conditions is analyzed in this paper, and a novel PV array fault diagnosis method is proposed based on fuzzy C-mean (FCM) and fuzzy membership algorithms. Firstly, clustering analysis of PV array fault samples is conducted using the FCM algorithm, indicating that there is a fixed relationship between the distribution characteristics of cluster centers and the different fault, then the fault samples are classified effectively. The membership degrees of all fault data and cluster centers are then determined by the fuzzy membership algorithm for the final fault diagnosis. Simulation analysis indicated that the diagnostic accuracy of the proposed method was 96%. Field experiments further verified the correctness and effectiveness of the proposed method. In this paper, various types of fault distribution features are effectively identified by the FCM algorithm, whether the PV array operation parameters belong to the fault category is determined by fuzzy membership algorithm, and the advantage of the proposed method is it can classify the fault data from normal operating data without foreknowledge. |
| first_indexed | 2024-04-13T08:07:28Z |
| format | Article |
| id | doaj.art-eabece7d1e2440f1bdc5812eb6a04b70 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:07:28Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-eabece7d1e2440f1bdc5812eb6a04b702022-12-22T02:55:06ZengMDPI AGEnergies1996-10732018-01-0111123810.3390/en11010238en11010238A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership AlgorithmQiang Zhao0Shuai Shao1Lingxing Lu2Xin Liu3Honglu Zhu4School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Renewable Energy, North China Electric Power University, Beijing 102206, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Renewable Energy, North China Electric Power University, Beijing 102206, ChinaPhotovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array’s electrical behavior characteristics under fault conditions is analyzed in this paper, and a novel PV array fault diagnosis method is proposed based on fuzzy C-mean (FCM) and fuzzy membership algorithms. Firstly, clustering analysis of PV array fault samples is conducted using the FCM algorithm, indicating that there is a fixed relationship between the distribution characteristics of cluster centers and the different fault, then the fault samples are classified effectively. The membership degrees of all fault data and cluster centers are then determined by the fuzzy membership algorithm for the final fault diagnosis. Simulation analysis indicated that the diagnostic accuracy of the proposed method was 96%. Field experiments further verified the correctness and effectiveness of the proposed method. In this paper, various types of fault distribution features are effectively identified by the FCM algorithm, whether the PV array operation parameters belong to the fault category is determined by fuzzy membership algorithm, and the advantage of the proposed method is it can classify the fault data from normal operating data without foreknowledge.http://www.mdpi.com/1996-1073/11/1/238PV arrayFCM algorithmcluster analysisfault diagnosismembership algorithm |
| spellingShingle | Qiang Zhao Shuai Shao Lingxing Lu Xin Liu Honglu Zhu A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm Energies PV array FCM algorithm cluster analysis fault diagnosis membership algorithm |
| title | A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm |
| title_full | A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm |
| title_fullStr | A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm |
| title_full_unstemmed | A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm |
| title_short | A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm |
| title_sort | new pv array fault diagnosis method using fuzzy c mean clustering and fuzzy membership algorithm |
| topic | PV array FCM algorithm cluster analysis fault diagnosis membership algorithm |
| url | http://www.mdpi.com/1996-1073/11/1/238 |
| work_keys_str_mv | AT qiangzhao anewpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT shuaishao anewpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT lingxinglu anewpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT xinliu anewpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT hongluzhu anewpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT qiangzhao newpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT shuaishao newpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT lingxinglu newpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT xinliu newpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm AT hongluzhu newpvarrayfaultdiagnosismethodusingfuzzycmeanclusteringandfuzzymembershipalgorithm |