A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Cui, Fengxin; Tu, Yanzhao; Gao, Wei

doi:10.3390/en15113961

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…Especially, due to the advantages of mobility, flexibility, programmability and large-area coverage, the unmanned aerial vehicles (UAVs) have been widely employed in the PV plant to capture moving images of various PV modules in the distributed power plants. Through intelligent image analysis, we can inspect specific PV faults in an efficient way, which is key to improve the operation and maintenance (O&M) level of the power plant ( Atsu et al, 2020 ; Chen et al, 2020 ; Abubakar et al, 2021 ; Navid et al, 2021 ; Cui et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning

Zhang,

Ge,

Wang

et al. 2024

Front. Neurorobot.

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With the fast development of large-scale Photovoltaic (PV) plants, the automatic PV fault identification and positioning have become an important task for the PV intelligent systems, aiming to guarantee the safety, reliability, and productivity of large-scale PV plants. In this paper, we propose a residual learning-based robotic (UAV) image analysis model for low-voltage distributed PV fault identification and positioning. In our target scenario, the unmanned aerial vehicles (UAVs) are deployed to acquire moving images of low-voltage distributed PV power plants. To get desired robustness and accuracy of PV image detection, we integrate residual learning with attention mechanism into the UAV image analysis model based on you only look once v4 (YOLOv4) network. Then, we design the sophisticated multi-scale spatial pyramid fusion and use it to optimize the YOLOv4 network for the nuanced task of fault localization within PV arrays, where the Complete-IOU loss is incorporated in the predictive modeling phase, significantly enhancing the accuracy and efficiency of fault detection. A series of experimental comparisons in terms of the accuracy of fault positioning are conducted, and the experimental results verify the feasibility and effectiveness of the proposed model in dealing with the safety and reliability maintenance of low-voltage distributed PV systems.

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Section: Introductionmentioning

confidence: 99%

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning

Zhang,

Ge,

Wang

et al. 2024

Front. Neurorobot.

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“…3 (2023) Ben Rahmoune M, Iratni A, Amari AS, Hafaifa A, Colak I: Fault detection and diagnosis of photovoltaic … 2 assessment of the last years has influenced the improvement of the monitoring approaches of these photovoltaic systems, for sustainable development of electric energy production. These modern approaches have been studied and applied in several works, such as; Abdellatif Mahammedi et al in [1], Ahmed Hafaifa et al in [4], Aref Eskandari et al in [8], Balamurugan et al in [10], Barun Basnet et al in [12], Fengxin Cui et al in [14], Imed Kaid et al in [17], Jianbo Yu et al in [19], Kurukuru et al in [22], Ruby Beniwal et al in [28]. Other research works have been conducted in the direction of improving the quality of electrical power generation, and detecting faults over time with high sensitivity of the monitoring system, such as the works of Abdelmoumen Saci et al in [2], Ahmed Zohair Djeddi et al in [5], Ali Kidar et al in [6], Azghandi Ali et al in [9], Fan Jia et al in [13], Ghadir Badran et al in [16], José Miguel et al in [20], Joshuva Arockia et al in [21], Mohamed Ben Rahmoune et al in [24], Mohammed Amine Deriche et al in [25][26], Sally Abdulaziz et al in [29] and Vincenzo Carletti et al in [33].…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Rahmoune¹,

Iratni²,

Amari³

et al. 2023

Diagnostyka

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Solar energy has become one of the most important renewable energies in the world. With the increasing installation of power plants in the world, the supervision and diagnosis of photovoltaic systems have become an important challenge with the increased occurrence of various internal and external faults. Indeed, this work proposes a new solar power plant diagnosis based on the artificial neural network approach. The developed model was to improve the performance and reliability of the power plant located in Tamanrasset, Algeria, which is subjected to varying weather conditions in terms of radiation and ambient temperature. By using the real data collected from the studied system, this approach allow to increase electricity production and address any issues that may arise quickly, ensuring uninterrupted power supply for the region. Neural networks have shown interesting results with high accuracy. This fault diagnosis approach allows to determine the time of occurrence of a fault affecting the examined PV system. Also, allow an early detection of failures and degradation of the system, which contributes to improving the productivity of this photovoltaic installation. With a significant reduction in the time needed to repair the damage caused by these faults and improve the reliability and continuity of the electrical energy production service.

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“…Cui et al proposed a PV-fault identification method based on improved deep residual shrinkage networks (DRSN). The method was able to identify short-circuit faults, partial-shading, abnormal aging and hybrid faults in a 6.48 kWp experimental PV field [19]. Voutsinas et al proposed a multi-output ANN for fault detection on the DC side of a photovoltaic system.…”

Section: Introductionmentioning

confidence: 99%

Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks

Ρουμπακιάς

Stamatelos

2022

Applied Sciences

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The operation and maintenance of a photovoltaic system is a challenging task that requires scientific soundness, and has significant economic impact. Faults in photovoltaic systems are a common phenomenon that demands fast diagnosis and repair. The effective and accurate diagnosis and categorization of faults is based on information received from the photovoltaic plant monitoring and energy management system. This paper presents the application of machine learning techniques in the processing of monitoring datasets of grid connected systems in order to diagnose faults. In particular, monitoring data from four photovoltaic parks located in Central Greece are analyzed. The existing data are divided for training and validation procedures. Different scenarios are examined first, in order to observe and quantify the behavior of artificial neural networks in already known faults. In this process, the faults are divided in three main categories. The system’s performance deviation against the prediction of the trained artificial neural network in each fault category is processed by health monitoring methodology in order to specify it quantitatively.

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A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Cited by 10 publications

References 34 publications

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks

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