Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

The environmentally clean nature of solar photovoltaic (PV) technology causes PV power generation to be embraced by all countries across the globe. Consequently, installation and utilization of PV power systems have seen much growth in recent years. Although PV arrays of such systems are robust, they are not immune to faults. To guarantee reliable power supply, economic returns, and safety of both humans and equipment, highly accurate fault detection, diagnosis, and interruption devices are required. In this paper, an overview of four major PV array faults and their causes are presented. Specifically, ground fault, line-line fault, arc fault, and hot spot fault have been covered. Next, conventional and advanced fault detection and diagnosis (FDD) techniques for managing these faults are reviewed. Moreover, a single evaluation metric has been proposed and utilized to evaluate the performances of the advanced FDD techniques. Finally, based on the papers reviewed, PV array fault management future trends and possible recommendations have been outlined.

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Fault Detection and Diagnosis for Photovoltaic Array Under Grid Connected Using Support Vector Machine

2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE) ◽

10.1109/cpere45374.2019.8980103 ◽

2019 ◽

Author(s):

Mohamed M. Badr ◽

Mostafa S. Hamad ◽

Ayman S. Abdel-Khalik ◽

Ragi A. Hamdy

Keyword(s):

Support Vector Machine ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Support Vector ◽

Photovoltaic Array ◽

Detection And Diagnosis

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Fault detection and diagnosis of power systems using artificial neural networks

Proceedings of the First International Forum on Applications of Neural Networks to Power Systems ◽

10.1109/ann.1991.213505 ◽

2002 ◽

Cited By ~ 14

Author(s):

K.S. Swarup ◽

H.S. Chandrasekharaiah

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fault Detection ◽

Power Systems ◽

Fault Detection And Diagnosis ◽

Artificial Neural ◽

Detection And Diagnosis

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Methodologies in power systems fault detection and diagnosis

Energy Systems ◽

10.1007/s12667-014-0129-1 ◽

2014 ◽

Vol 6 (1) ◽

pp. 85-108 ◽

Cited By ~ 7

Author(s):

Saad Abdul Aleem ◽

Nauman Shahid ◽

Ijaz Haider Naqvi

Keyword(s):

Fault Detection ◽

Power Systems ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

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Fault detection and diagnosis of photovoltaic system using fuzzy logic control

E3S Web of Conferences ◽

10.1051/e3sconf/201910702001 ◽

2019 ◽

Vol 107 ◽

pp. 02001 ◽

Cited By ~ 2

Author(s):

Sayed A. Zaki ◽

Honglu Zhu ◽

Jianxi Yao

Keyword(s):

Fuzzy Logic ◽

Fault Detection ◽

Fuzzy Logic Control ◽

Fault Detection And Diagnosis ◽

Rapid Expansion ◽

Photovoltaic System ◽

Maintenance Cost ◽

Pv Array ◽

Logic Control ◽

Detection And Diagnosis

Among several renewable energy resources, Solar has great potential to solve the world’s energy problems. With the rapid expansion and installation of PV system worldwide, fault detection and diagnosis has become the most significant issue in order to raise the system efficiency and reduce the maintenance cost as well as repair time. This paper presented a method for monitoring, identifying, and detecting different faults in PV array. This method is built based on comparing the measured electrical parameters with its theoretical parameters in case of normal and faulty conditions of PV array. For this purpose, three ratios of open circuit voltage, current, and voltage are obtained with their associated limits in order to detect eight different faults. Moreover, the fuzzy logic control FLC method is performed for studying the failure configuration and categorizing correctly the different faults occurred. The outcomes obtained by performing the different faults representing permanent and temporary faults demonstrated that the FLC was equipped to precisely identify the faults upon their occurring. Different simulated and experimental tests are conducted to demonstrate the performance of the proposed method.

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

Energies ◽

10.3390/en14144140 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4140

Author(s):

Weiguo He ◽

Deyang Yin ◽

Kaifeng Zhang ◽

Xiangwen Zhang ◽

Jianyong Zheng

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Naive Bayes ◽

Fault Detection And Diagnosis ◽

Fine Tuning ◽

Pv Systems ◽

Bayes Model ◽

Pv Array ◽

Diagnosis Method ◽

Detection And Diagnosis

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.

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