An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test

Majdi Mansouri, Mansour Hajji, Mohamed Trabelsi, Mohamed Faouzi Harkat, Ayman Al-khazraji, Andreas Livera, Hazem Nounou and Mohamed Nounou

Energy, 2018, vol. 159, issue C, 842-856

Abstract: This paper proposes an improved statistical failure detection technique for enhanced monitoring capabilities of PV systems. The proposed technique offers reduced false alarm and missed detection rates compared to the generalized likelihood ratio test (GLRT) by taking into consideration the nature variance of the GLRT statistics and applying a multiscale representation. The multiscale nature of the data provides better robustness to noises and better monitoring quality. The effectiveness of the proposed multiscale weighted GLRT (MS-WGLRT) method in detecting failures is evaluated using a set of synthetic and simulated PV data where the developed chart is used for detecting single and multiple failures (e.g., Bypass, Mix and Shading failures). Moreover, a set of real-data was used in order to prove the effectiveness of the proposed technique in detecting partial shading faults. All results show that the MS-WGLRT method offers better fault detection performances compared to the classical WGLRT and conventional GLRT charts.

Keywords: Photovoltaic (PV) systems; Failure detection (FD); Generalized likelihood ratio test (GLRT); Weighted GLRT (WGLRT); Multiscale (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:159:y:2018:i:c:p:842-856

DOI: 10.1016/j.energy.2018.06.194

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