Design of Hybrid Artificial Bee Colony Algorithm and Semi-Supervised Extreme Learning Machine for PV Fault Diagnoses by Considering Dust Impact

Photovoltaic (PV) systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. Abnormal operations lead to massive power losses, and severe faults as short circuit may cause safety problems and fire hazards. Therefore, monitoring the operation status of PV...

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Published inIEEE transactions on power electronics Vol. 35; no. 7; pp. 7086 - 7099
Main Authors Huang, Jun-Ming, Wai, Rong-Jong, Yang, Geng-Jie
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Aging
Artificial bee colony (ABC) algorithm
Artificial neural networks
Circuit faults
Cleaning
Computer simulation
Diagnostic systems
Dust
dust impact
Electric power generation
Fault diagnosis
Fire hazards
Machine learning
Monitoring
Operational hazards
Optimization algorithms
photovoltaic (PV)
Photovoltaic cells
Power consumption
Power generation
Power loss
Search algorithms
semi-supervised extreme learning. machine
Semisupervised learning
Short circuits
Supervised learning
Swarm intelligence
System effectiveness
Troubleshooting
Online AccessGet full text
ISSN0885-8993
1941-0107
DOI10.1109/TPEL.2019.2956812

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Abstract Photovoltaic (PV) systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. Abnormal operations lead to massive power losses, and severe faults as short circuit may cause safety problems and fire hazards. Therefore, monitoring the operation status of PV systems for timely troubleshooting potential failure and effective cleaning scheme are the focus of current research works. In this study, I-V characteristics of PV strings under various fault states are analyzed, especially soiling condition. Because labeled data for PV systems with specific faults are challenging to record, especially in the large-scale ones, a novel algorithm combining artificial bee colony algorithm and semi-supervised extreme learning machine is proposed to handle this problem. The proposed algorithm can diagnose PV faults using a small amount of simulated labeled data and historical unlabeled data, which greatly reduces labor cost and time-consuming. Moreover, the monitoring of dust accumulation can warn power plant owners to clean PV modules in time and increase the power generation benefits. PV systems of 3.51 and 3.9 kWp are used to verify the proposed diagnosis method. Both numerical simulations and experimental results show the accuracy and reliability of the proposed PV diagnostic technology.
AbstractList Photovoltaic (PV) systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. Abnormal operations lead to massive power losses, and severe faults as short circuit may cause safety problems and fire hazards. Therefore, monitoring the operation status of PV systems for timely troubleshooting potential failure and effective cleaning scheme are the focus of current research works. In this study, I–V characteristics of PV strings under various fault states are analyzed, especially soiling condition. Because labeled data for PV systems with specific faults are challenging to record, especially in the large-scale ones, a novel algorithm combining artificial bee colony algorithm and semi-supervised extreme learning machine is proposed to handle this problem. The proposed algorithm can diagnose PV faults using a small amount of simulated labeled data and historical unlabeled data, which greatly reduces labor cost and time-consuming. Moreover, the monitoring of dust accumulation can warn power plant owners to clean PV modules in time and increase the power generation benefits. PV systems of 3.51 and 3.9 kWp are used to verify the proposed diagnosis method. Both numerical simulations and experimental results show the accuracy and reliability of the proposed PV diagnostic technology.
Photovoltaic (PV) systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. Abnormal operations lead to massive power losses, and severe faults as short circuit may cause safety problems and fire hazards. Therefore, monitoring the operation status of PV systems for timely troubleshooting potential failure and effective cleaning scheme are the focus of current research works. In this study, I-V characteristics of PV strings under various fault states are analyzed, especially soiling condition. Because labeled data for PV systems with specific faults are challenging to record, especially in the large-scale ones, a novel algorithm combining artificial bee colony algorithm and semi-supervised extreme learning machine is proposed to handle this problem. The proposed algorithm can diagnose PV faults using a small amount of simulated labeled data and historical unlabeled data, which greatly reduces labor cost and time-consuming. Moreover, the monitoring of dust accumulation can warn power plant owners to clean PV modules in time and increase the power generation benefits. PV systems of 3.51 and 3.9 kWp are used to verify the proposed diagnosis method. Both numerical simulations and experimental results show the accuracy and reliability of the proposed PV diagnostic technology.
Author Yang, Geng-Jie
Wai, Rong-Jong
Huang, Jun-Ming
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SubjectTerms Aging
Artificial bee colony (ABC) algorithm
Artificial neural networks
Circuit faults
Cleaning
Computer simulation
Diagnostic systems
Dust
dust impact
Electric power generation
Fault diagnosis
Fire hazards
Machine learning
Monitoring
Operational hazards
Optimization algorithms
photovoltaic (PV)
Photovoltaic cells
Power consumption
Power generation
Power loss
Search algorithms
semi-supervised extreme learning. machine
Semisupervised learning
Short circuits
Supervised learning
Swarm intelligence
System effectiveness
Troubleshooting
Title Design of Hybrid Artificial Bee Colony Algorithm and Semi-Supervised Extreme Learning Machine for PV Fault Diagnoses by Considering Dust Impact
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