High-Speed Fault Detection And Classification Method For Transmission Networks Using Hybridized Low Pass And Kalman Filtering Approach /

Abstract

Renewable energy integration in transmission networks is more common due to environmental and economic benefits. However, Fault detection is a significant subject in such renewable energy-based transmission networks (REBTN). Furthermore, power transmission lines account for 85 to 87% of all power network faults. This research proposes a new method for detecting and classifying different types of faults on 230-kV REBTNs. Initially, the Kalman Filter is implemented on the measured 3-phase current signal for the state estimation of non-fundamental 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. A threshold is calculated by comparing the values of indices under healthy and worst conditions. If the values of the SSCBFD index become higher than this threshold, then the system will be regarded as in a faulty condition. Hence, in case of any abnormal or faulty condition, considerable variation will be experienced in the SSCBFD and SCBFC index. All types of faults are precisely and timely detected as well as classified in various scenarios by using these threshold values. By following a systematic three-step process, this technique enables efficient demodulation, filtering, and synchronization of current signals, resulting in accurate fault detection and classification indices. A modified IEEE-9 bus test system with a renewable solar energy source is implemented in Matlab/ Simulink for analyzing the efficiency of the proposed technique. Moreover, the suggested method detects and classifies all kinds of solid and high impedance faults (HIF) successfully and timely. The results show that the proposed scheme is highly robust in all testing scenarios.