PV array Fault Classification based on Machine Learning

Minh, Nguyen Quoc; Dieu, Do Thi; Nguyen, Hau Van

doi:10.1109/iccais56082.2022.9990272

Cited by 1 publication

(1 citation statement)

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“…[16][17][18] Liu et al 19 introduce a novel clustering method based on dilation and erosion theory, with enhanced fault diagnosis in PV arrays without the need for predetermining fault types. Research efforts have often been fragmented, focusing on specific fault types through methodologies, like, fuzzy logic, 20 neural networks, [21][22][23][24][25] and machine learning 26 leaving a void for a comprehensive fault detection and localization solution. 27 While macroscale fault detection within PV arrays has achieved success, microscale fault localization at the module level remains challenging, often requiring costly wireless sensor networks for detailed module insights, thereby increasing deployment expenses.…”

Section: Introductionmentioning

confidence: 99%

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Lamdihine,

Ouassaid

2024

Energy Science & Engineering

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This paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed ‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems.

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