A Random Under-Sampling based Passive Approach for Fast and Accurate Detection of Islanding in Electrical Distribution System

Abstract

ABSTRACT In this article, a robust random under-sampling boosting (RUSB) islanding detection technique evolved from the machine learning (ML) approaches is proposed. Unlike the conventional passive islanding techniques, the ML algorithms are superior in performance owing to their better dynamic behaviour. However, a major challenge arises when non-islanding events are more as compared to islanding events. This leads to skewness in dataset resulting in improper classification, and poor accuracy are often observable. To address these issues, the proposed algorithm which relies on the under-sampling approach can easily identify the dominating cases in the available dataset such that overall detection accuracy improves with a better dynamic response. Also, the modal transformation employed at the point to measurement is a rescuer for reducing the dataset by decomposing the three-phase current signal into single-phase current signal (also known as modal current (MC) component). Therefore, the feature extraction is carried out from MC’s information by employing wavelet transformation to detect islanding conditions quickly. The extensive numerical simulations are carried out for a standard IEEE 15 bus distribution network to assess the improvement achieved in the accuracy of classification and the ability to accurately detect the islanding condition in the event of large number of non-islanding test cases with a fast dynamic response.