Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multi-resolution Signal Decomposition

Abstract

Fault detection in Photovoltaic (PV) arrays becomes difficult as the number of PV panels increases. Particularly, under low irradiance conditions with an active maximum power point tracking (MPPT) algorithm, line-to-line (L-L) faults may remain undetected because of low fault currents, resulting in loss of energy and potential fire hazards. This paper proposes a fault detection algorithm based on multi-resolution signal decomposition (MSD) for feature extraction, and two-stage support vector machine (SVM) classifiers for decision making. This detection method only requires data of the total voltage and current from a PV array and a limited amount of labeled data for training the SVM.

Existing System

Mitigation Techniques, Maximum Power Point Tracking.

Proposed System

A fault detection algorithm for PV systems based on pattern recognition and machine learning techniques is proposed to improve the detection accuracy for challenging L-L fault scenarios that occur under low irradiance, through a high impedance, or in interaction with the MPPT scheme. The method takes advantage of the multi-resolution signal decomposition (MSD) technique to extract the feature space of L-L faults. A two-stage support vector machine (SVM) classifier is proposed for decision making. The proposed method is economical as it only requires measurements of the overall voltage and current of a PV array instead of numerous costly sensors. Trained by a minimum portion of data, this algorithm presents satisfactory accuracy in detecting L-L faults under different operating conditions.

Architecture

Training and testing process of the proposed fault detection method