Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network
The environment of the PV array is harsh and severe, resulting in frequent faults. In order to improve the accuracy of PV array fault diagnosis, a deep belief networks (DBN) based fault diagnosis method is proposed for the common fault types of PV arrays. The experimental feature parameters was obta...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2019-12-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201901066 |
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| author | Caixia TAO Xu WANG Fengyang GAO |
| author_facet | Caixia TAO Xu WANG Fengyang GAO |
| author_sort | Caixia TAO |
| collection | DOAJ |
| description | The environment of the PV array is harsh and severe, resulting in frequent faults. In order to improve the accuracy of PV array fault diagnosis, a deep belief networks (DBN) based fault diagnosis method is proposed for the common fault types of PV arrays. The experimental feature parameters was obtained by Matlab simulation and the fault diagnosis model with the five operating states of the PV array is established. According to the characteristics of the DBN, the impacts of training sets, training periods and restricted boltzmann machine (RBM) layers on the model performance are analyzed through recognition experiments. Compared with the fuzzy C-means clustering (FCM), the support vector machine (SVM) and the back-propagation neural network (BPNN) method from the overall diagnostic accuracy and different types of fault diagnostic accuracy. The results show that the method is suitable for fault classification of photovoltaic arrays, and it improves the accuracy of fault identification effectively compared with other diagnostic models. |
| format | Article |
| id | doaj-art-10c87afea8434a0bba31df8df04fc87e |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2019-12-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-10c87afea8434a0bba31df8df04fc87e2025-08-20T02:52:31ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492019-12-01521210511210.11930/j.issn.1004-9649.201901066zgdl-52-12-taocaixiaFault Diagnosis of Photovoltaic Array Based on Deep Belief NetworkCaixia TAO0Xu WANG1Fengyang GAO2Department of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaDepartment of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaDepartment of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaThe environment of the PV array is harsh and severe, resulting in frequent faults. In order to improve the accuracy of PV array fault diagnosis, a deep belief networks (DBN) based fault diagnosis method is proposed for the common fault types of PV arrays. The experimental feature parameters was obtained by Matlab simulation and the fault diagnosis model with the five operating states of the PV array is established. According to the characteristics of the DBN, the impacts of training sets, training periods and restricted boltzmann machine (RBM) layers on the model performance are analyzed through recognition experiments. Compared with the fuzzy C-means clustering (FCM), the support vector machine (SVM) and the back-propagation neural network (BPNN) method from the overall diagnostic accuracy and different types of fault diagnostic accuracy. The results show that the method is suitable for fault classification of photovoltaic arrays, and it improves the accuracy of fault identification effectively compared with other diagnostic models.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201901066pv arrayfault diagnosisfeature parametersdeep belief networkrecognition accuracy |
| spellingShingle | Caixia TAO Xu WANG Fengyang GAO Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network Zhongguo dianli pv array fault diagnosis feature parameters deep belief network recognition accuracy |
| title | Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network |
| title_full | Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network |
| title_fullStr | Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network |
| title_full_unstemmed | Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network |
| title_short | Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network |
| title_sort | fault diagnosis of photovoltaic array based on deep belief network |
| topic | pv array fault diagnosis feature parameters deep belief network recognition accuracy |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201901066 |
| work_keys_str_mv | AT caixiatao faultdiagnosisofphotovoltaicarraybasedondeepbeliefnetwork AT xuwang faultdiagnosisofphotovoltaicarraybasedondeepbeliefnetwork AT fengyanggao faultdiagnosisofphotovoltaicarraybasedondeepbeliefnetwork |