Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems. (9th July 2018)

• Record Type: Journal Article
• Authors: Mansouri, Majdi; Hajji, Mansour; Trabelsi, Mohamed; Al‐khazraji, Ayman; Harkat, Mohamed Faouzi; Nounou, Hazem; Nounou, Mohamed
• Is Part Of: International transactions on electrical energy systems. Volume 28:Number 12(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-09
• Subjects: failure detection (FD) -- generalized likelihood ratio test (GLRT) -- multiscale representation -- PV system -- weighted GLRT (WGLRT); Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/etep.2640
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: In this paper, a new multiscale weighted generalized likelihood ratio test (MS‐WGLRT) chart is proposed for enhanced failure detection in photovoltaic systems. The main weakness of the classical generalized likelihood ratio test chart is in dealing with residual samples while ignoring their natural variances. By taking into consideration the nature variance of the detection residual and applying a multiscale representation, the proposed technique allows the reduction in false alarm and missed detection rates compared with the classical generalized likelihood ratio test chart. The multiscale representation of data is an efficient data analysis and feature extraction tool that has a great impact on the effectiveness of failure detection. The effectiveness of the proposed method is evaluated on a simulated photovoltaic data where the developed chart is used for detecting single and multiple failures (eg, bypass, mix, and shading failures). The simulation results show that the multiscale weighted generalized likelihood ratio test method offers better performance compared with the classical generalized likelihood ratio chart.