On-line Monitoring and Fault Diagnosis of PV Array Based on BP Neural Network Optimized by Genetic Algorithm

Lin, Hanwei; Chen, Zhicong; Wu, Lijun; Lin, Peijie; Cheng, Shuying

doi:10.1007/978-3-319-26181-2_10

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…(2) The program used was developed based on the available software and programming language familiar to the authors. In [125], a failure diagnostic algorithm based on an online distributed monitoring system of a PV array of Zigbee wireless sensors network and a genetic algorithm optimization based BP neural network was investigated. The Zigbee wireless network system monitors each module's output current, voltage, and irradiation, as well as the environment's temperature and irradiation.…”

Section: Other Methodsmentioning

confidence: 99%

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

2021

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In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.

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Section: Other Methodsmentioning

confidence: 99%

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

2021

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“…The selection of features depends on the target fault and PV plant scale, i.e., module or array level. For the output, some cases (like in [32,33]) use the combination binary output values of each neuron to determine the fault type. In some cases, authors export the feature values that correspond directly either to the fault type (like in [38,50]) or to the fault severity (like in [35,42]), while the others do not specify.…”

Section: ) Integration Of Snn In Pv Fddmentioning

confidence: 99%

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Delpha

Diallo

et al. 2021

Renewable and Sustainable Energy Reviews

184

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“…Other methods are presented in [58], [59], and [60]. All of the developed electrical FDD based techniques can be done through offline adaptation (test of the technique's effectiveness) [36] or online adaptation at a real-time [60][61][62][63].…”

Section: Fdd Electrical-based Techniquesmentioning

confidence: 99%

A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

et al. 2021

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Recently, many focuses have been done in the field of renewable energies, especially in solar photovoltaic energy. Photovoltaic generator, considered as the heart of any photovoltaic installation, exhibits sometimes malfunctions which involve degradations on the overall photovoltaic plant. Therefore, diagnosis techniques are required to ensure failures detection. They avoid dangerous risks, prevent damages, allow protection, and extend their healthy life. For these purposes, many recent studies have given focuses on this field. This paper summarizes a large number of such interesting works. It presents a survey of photovoltaic generator degradations kinds, several types of faults, and their major diagnosis techniques. Comparative studies and some critical analyses are given. Other trending diagnosis solutions are also discussed. A proposed neural networks-based technique is developed to clarify the main process of diagnosis techniques, using artificial intelligence. This method shows good results for modelling and diagnosing the healthy and faulty (shaded) photovoltaic array.

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On-line Monitoring and Fault Diagnosis of PV Array Based on BP Neural Network Optimized by Genetic Algorithm

Cited by 13 publications

References 9 publications

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

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