Fault Detection & Classification in 230 kV Transmission Networks Using Hybrid-filtering Approach

Abstract

Renewable energy integration in transmission networks is more common due to environmental and economical benefits. However, fault detection is a significant subject in such renewable energy-based transmission networks (REBTNs). Furthermore, power transmission lines account for 85 to 87% of all power network faults. The presented research paper proposes an efficient method for detecting and classifying different types of faults on 230-kV REBTNs. Initially, the Adaptive Kalman Filter (AKF) is implemented on the measured 3-Phase current signal for the state estimation of nonfundamental features. Then, the low pass filtering and square law approach is employed for examining the features of the current signal from considered buses. Secondly, the sum of squared current-based fault detection (SSCBFD) and squared current-based fault classification (SCBFC) indices are generated. Then, in case of any faulty condition, considerable variation will be experienced in the SSCBFD and SCBFC indices. A modified IEEE-9 bus test system with a renewable solar energy source is analysed using Matlab/Simulink to determine the efficiency of the suggested methodology. Moreover, the suggested method detects and classifies all kinds of solid and high impedance faults (HIF) successfully and timely.