Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model

Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model

With the widespread attention and research of distributed photovoltaic (PV) systems, the fault detection and diagnosis problems of distributed PV systems has become increasingly prominent. To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault...

Full description

Bibliographic Details
Main Authors: Weiguo He, Deyang Yin, Kaifeng Zhang, Xiangwen Zhang, Jianyong Zheng
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Energies
Subjects:
PV array
fault detection
fault diagnosis
fine-tuning Naive Bayesian model
Online Access:https://www.mdpi.com/1996-1073/14/14/4140
id doaj-8caa3b0c0a5848acb9d114bddcda3ba7
record_format Article
spelling doaj-8caa3b0c0a5848acb9d114bddcda3ba72021-07-23T13:38:41ZengMDPI AGEnergies1996-10732021-07-01144140414010.3390/en14144140Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian ModelWeiguo He0Deyang Yin1Kaifeng Zhang2Xiangwen Zhang3Jianyong Zheng4School of Automation, Southeast University, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaSchool of Automation, Southeast University, Nanjing 210096, ChinaChina Electric Power Research Institute, Nanjing 210003, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaWith the widespread attention and research of distributed photovoltaic (PV) systems, the fault detection and diagnosis problems of distributed PV systems has become increasingly prominent. To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault conditions of PV array such as open-circuit, short-circuit, shading, abnormal degradation, and abnormal bypass diode is proposed. First, in view of the problem of less distributed PV fault data, a fine-tuning Naive Bayes model (FTNB) is proposed to improve the diagnosis accuracy. Second, the failure sample set is used to train the model. Then, the maximum power point data of the PV inverter and the meteorological data are collected for fault diagnosis. Finally, the effectiveness and accuracy of the proposed method are verified by the analysis of simulation. In addition, this method requires only a small number of fault sample sets and no additional measurement equipment is required, which is suitable for real-time monitoring of distributed PV systems.https://www.mdpi.com/1996-1073/14/14/4140PV arrayfault detectionfault diagnosisfine-tuning Naive Bayesian model
collection DOAJ
language English
format Article
sources DOAJ
author Weiguo He
Deyang Yin
Kaifeng Zhang
Xiangwen Zhang
Jianyong Zheng
spellingShingle Weiguo He
Deyang Yin
Kaifeng Zhang
Xiangwen Zhang
Jianyong Zheng
Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
Energies
PV array
fault detection
fault diagnosis
fine-tuning Naive Bayesian model
author_facet Weiguo He
Deyang Yin
Kaifeng Zhang
Xiangwen Zhang
Jianyong Zheng
author_sort Weiguo He
title Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
title_short Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
title_full Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
title_fullStr Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
title_full_unstemmed Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
title_sort fault detection and diagnosis method of distributed photovoltaic array based on fine-tuning naive bayesian model
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2021-07-01
description With the widespread attention and research of distributed photovoltaic (PV) systems, the fault detection and diagnosis problems of distributed PV systems has become increasingly prominent. To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault conditions of PV array such as open-circuit, short-circuit, shading, abnormal degradation, and abnormal bypass diode is proposed. First, in view of the problem of less distributed PV fault data, a fine-tuning Naive Bayes model (FTNB) is proposed to improve the diagnosis accuracy. Second, the failure sample set is used to train the model. Then, the maximum power point data of the PV inverter and the meteorological data are collected for fault diagnosis. Finally, the effectiveness and accuracy of the proposed method are verified by the analysis of simulation. In addition, this method requires only a small number of fault sample sets and no additional measurement equipment is required, which is suitable for real-time monitoring of distributed PV systems.
topic PV array
fault detection
fault diagnosis
fine-tuning Naive Bayesian model
url https://www.mdpi.com/1996-1073/14/14/4140
work_keys_str_mv AT weiguohe faultdetectionanddiagnosismethodofdistributedphotovoltaicarraybasedonfinetuningnaivebayesianmodel
AT deyangyin faultdetectionanddiagnosismethodofdistributedphotovoltaicarraybasedonfinetuningnaivebayesianmodel
AT kaifengzhang faultdetectionanddiagnosismethodofdistributedphotovoltaicarraybasedonfinetuningnaivebayesianmodel
AT xiangwenzhang faultdetectionanddiagnosismethodofdistributedphotovoltaicarraybasedonfinetuningnaivebayesianmodel
AT jianyongzheng faultdetectionanddiagnosismethodofdistributedphotovoltaicarraybasedonfinetuningnaivebayesianmodel
_version_ 1721288585034858496

Similar Items