Fault diagnosis for PV system using a deep learning optimized via PSO heuristic combination technique

Eldeghady, Ghada Shaban; Kamal, Hanan A.; Hassan, Mohamed

doi:10.1007/s00202-023-01806-6

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…Eldeghad et al proposed a deep learning technique optimized via a particle swarm optimization (PSO) heuristic combination algorithm for fault diagnosis in PV systems. This algorithm exhibited good results in fault detection and is promising for enhancing system efficiency, reliability, and safety [19]. Appiah et al leveraged long short-term memory (LSTM) to extract fault features, subsequently inputting them into the softmax regression classifier for fault detection and diagnosis [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

Amiri,

Kichou,

Oudira

et al. 2024

Sustainability

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The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.

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Section: Literature Reviewmentioning

confidence: 99%

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

Amiri,

Kichou,

Oudira

et al. 2024

Sustainability

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“…To detect short-circuit faults in PV systems, Rezgui et al [129] proposed a method combining -nearest neighbors ( -NN) and SVM. -NN is a classification algorithm used to classify a certain data sample from the data samples closest to the training set.…”

Section: 7%mentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Hu,

Lim,

Tian

et al. 2024

CAAI Artificial Intelligence Research

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Integrating artificial intelligence (AI) into photovoltaic (PV) systems has become a revolutionary approach to improving the efficiency, reliability, and predictability of solar power generation. In this paper, we explore the impact of AI technology on PV power generation systems and its applications from a global perspective. Central to the discussion are the pivotal applications of AI in maximum power point tracking (MPPT), power forecasting, and fault detection within the PV system. On the one hand, the integration with AI technology enables the optimization and improvement of the operational efficiency of PV systems. On the other hand, new challenges have been observed, mainly in the areas of data processing and model management. Moreover, advances in AI technology and hardware upgrades will lead to the rapid global popularization of new energy sources such as solar energy, which is expected to replace traditional energy sources. Finally, we describe forward-looking solutions including transfer learning, few-shot learning, and edge computing, as well as the state of the art.

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“…Advanced models like DeepLabV3+, U-Net, and the Feature Pyramid Network (FPN) are increasingly combined for enhanced fault detection results [49,50]. Specific techniques, such as Ghost convolution integrated with You Only Look Once (YOLOv5), have also been introduced for PV fault detection [51]. Moreover, the Feature-Induced Augmentation (FIA) method showed improved results in identifying micro-cracks on PV surfaces [52].…”

Section: Efficiency Of Deep Learning Modelsmentioning

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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Fault diagnosis for PV system using a deep learning optimized via PSO heuristic combination technique

Cited by 10 publications

References 39 publications

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

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

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