Fault detection and localization in solar photovoltaic arrays using the current-voltage sensing framework

Alajmi, Masoud; Abdel‐Qader, Ikhlas

doi:10.1109/eit.2016.7535257

Cited by 18 publications

(4 citation statements)

References 5 publications

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“…[37] used. Tis method enables us to separate the background and the modules using the binary thresholding much more easily, and it can be controlled using software [28]. Te next stage is implementing module edge detection with canny edge detection which Xie et al used [29].…”

Section: Module Segmentationmentioning

confidence: 99%

“…Te detection is carried out after images are captured by a thermal camera or UAV and then are processed in computer software. Te reviews, listed in Table 1, cover several insights regarding several algorithms used such as digital image processing (DIP) [23][24][25][26][27][28][29][30], deep learning (DL) [31], and other machine learning (ML) techniques [26,32], which have comparable results to detect modules with or without faults and classify it into a category. Despite that, those approaches were able to detect the module's fault but do not address its correlation to the module itself.…”

Section: Introductionmentioning

confidence: 99%

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Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Setiawan,

Fathurrahman,

Pamungkas

et al. 2024

Journal of Electrical and Computer Engineering

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Maintaining the maximum performance of solar panels poses the foremost challenge for solar photovoltaic power plants in this era. One of the common PV faults which decreases PV power output is a hot spot which is caused by a prolonged local partial shading from objects, such as dust piles or animal waste. To prevent it, an enormous effort for PV inspection is needed especially for large solar power plants. Hence, automatic partial shading detection is critical in preventing PV hot spots to assist maintenance activities which are associated with a drop in energy output. This research developed fast partial shading detection application on PV modules using digital image processing to detect the hot spot and PV modules areas and afterwards calculate the PV systems power loss ratio. The proposed method demonstrated a hot spot detection rate of 94.74% and a module detection rate of 100%. The power loss ratio calculation is compared and validated using IV curve measurement and has 91.26% similarity value which is a feasible application for the real-world system.

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Section: Module Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Setiawan,

Fathurrahman,

Pamungkas

et al. 2024

Journal of Electrical and Computer Engineering

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show abstract

“…Khác với MBDM, trong phương pháp RDM, các thông số thu thập từ tấm pin quang điện được phân tích và đặt ngưỡng cảnh báo thông qua các quy luật và kiến thức được lập trình trước. Các quy luật về so sánh và đặt ngưỡng cảnh báo có thể vẫn được lấy từ kết quả phân tích mô hình tấm pin lý thuyết 7,8 hoặc từ các đặc trưng trong quá trình vận hành thực tế 9,10 . OSA là các phương pháp biển đổi các tín hiệu thông số tấm pin bằng các phép biến đổi toán học để trích xuất đặc trưng của tín hiệu và đưa ra đánh giá dựa trên những đặc trưng được phân tích.…”

Section: Phân Loại Sự Cố Mô-đun Quang đIện Dựa Trên đIểm Công Suất Cự...unclassified

Fault classification for photovoltaic module based on maximum power point and machine learning techniques

2022

Sci. Tech. Dev. J. - Nat. Sci.

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“…The SVM method shows high accuracy for detecting this type of fault. In [6], research by Alajmi and Abdel-Qader designed a new approach to detect faults in a PV array. They compared current-voltage values to maximum measured signal values generated under normal operation conditions.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning as an Efficient Diagnostic Tool for Fault Detection and Localization in Solar Photovoltaic Arrays

Alajmi¹,

Aljahdali

Alsaheel³

et al.

EPiC Series in Computing

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Solar energy, one of many types of renewable energy, is considered to be an excellent alternative to non-renewable energy sources. Its popularity is increasing rapidly, especially because fuel energy consumes and depletes finite natural resources, polluting the environment, whereas solar energy is low- cost and clean. To produce a reliable supply of energy, however, solar energy must also be consistent. The energy we derive from a photovoltaic (PV) array is dependent on changeable factors such as sunlight, positioning of the array, covered area, and status of the solar cell. Every change adds potential for the creation of error in the array. Therefore, thorough research and a protocol for fast, efficient location and correction of all kinds of errors must be an urgent priority for researchers.For this project we used machine learning (ML) with voltage and current sensors to detect, localize and classify common faults including open circuit, short circuit, and hot-spot. Using the proposed algorithm, we have improved the accuracy of fault detection, classification and localization to 100%. Further, the proposed method can execute all three tasks (detection, classification, and localization) simultaneously.

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Fault detection and localization in solar photovoltaic arrays using the current-voltage sensing framework

Cited by 18 publications

References 5 publications

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Fault classification for photovoltaic module based on maximum power point and machine learning techniques

Machine Learning as an Efficient Diagnostic Tool for Fault Detection and Localization in Solar Photovoltaic Arrays

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