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A Developed Algorithm Inspired from the Classical KNN for Fault Detection and Diagnosis PV Systems

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Abstract

During the operation of photovoltaic systems, various faults can occur and result in serious problems, such as energy loss or system shutdown. Therefore, it is crucial to identify and diagnose these faults in order to improve system performance. The purpose of this work is to propose an efficient and simple procedure for the early detection and diagnosis of faults on the direct current side of photovoltaic systems. These faults include the short circuit of three modules, short circuit of ten modules, and string disconnection. Therefore, it is necessary to distinguish between four classes: the healthy class and three classes representing different types of faults. A dataset representing the four classes and comprising four measured attributes—cell temperature, solar irradiance, and current and voltage at the maximum power point—is utilized in the developed approach. The idea is to transform the multiclassification problem into a binary classification problem and utilize a modified version of the well-known K-nearest neighbors (KNN) classifier. In this proposed version, the training dataset is divided into two hyperspheres, each representing a distinct class. The Giza pyramid construction algorithm is then utilized to determine the optimal center coordinates of these hyperspheres. To classify a new data point using the proposed classifier, which combines the KNN classifier and the Giza pyramid construction algorithm, distances are computed only between the new data point and the center of each sphere. Unlike the classical version of the KNN classifier, which involves computing distances between the new data point and the entire dataset. To assess the efficiency of the proposed approach, a comparative study was conducted, including the classical version of the KNN, support vector machine, decision tree, and random forest algorithms. The evaluation criteria considered were accuracy, precision, recall, and execution time. The results of the carried-out study demonstrated the remarkable superiority of the proposed algorithm over these alternative methods.

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Authors and Affiliations

  1. Laboratoire des systèmes électriques et télécommande, Faculté de Technologie, Université Blida 1, BP 270, Route de Soumaa, 09000, Blida, Algeria

    Youssouf Mouleloued & Kamel Kara

  2. Electrical Engineering Laboratory (LGE), University Mohamed Boudiaf of M’sila, BP 166, 28000, M’sila, Algeria

    Aissa Chouder

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  1. Youssouf Mouleloued
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Mouleloued, Y., Kara, K. & Chouder, A. A Developed Algorithm Inspired from the Classical KNN for Fault Detection and Diagnosis PV Systems. J Control Autom Electr Syst 34, 1013–1027 (2023). https://doi.org/10.1007/s40313-023-01025-1

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