Fault diagnosis of photovoltaic strings by using machine learning‐based stacking classifier

Liu, Bo; Sun, Kai; Wang, Xiaoyu; Zhao, Jian; Hou, Xiaochao

doi:10.1049/rpg2.12755

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…Convolutional Neural Networks (CNNs) are frequently adopted due to their high accuracy (100%), with several studies applying them for detecting a range of PV panel faults [39][40][41]. Additionally, hybrid models such as ensemble learning and stacking classifiers also reported significant accuracy enhancements [42,43]. Regarding speed and efficiency, the CNN approach in one study was particularly emphasized for its rapid fault identification [41].…”

Section: Comparison Of Techniquesmentioning

confidence: 99%

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Islam,

Rashel,

Ahmed

et al. 2023

Energies

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Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

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