A Novel TKEO With the Decision Tree–Based Method for Fault Analysis of the HVDC Transmission Link Fed by Offshore Wind and Solar Farms

Abstract

Detecting and classifying various faults on high voltage DC transmission (HVDC) lines and pinpointing their locations are crucial tasks for the power system’s efficient operation. This paper presents a Teager–Kaiser energy operator (TKEO) technique with a decision tree–based fault type classifier to monitor power system faults on the HVDC transmission line. The change identification filter technique is used to identify the fault location and record it as the change initiation point (CIP). There are only three samples of the average current (I_avrg) used at the CIP of the HVDC link. The eight indices for fault analysis are produced by the suggested TKEO approach by processing average current (I_avrg) signals not the differential current. Electricity networks may be restored as soon as practical while minimizing economic losses to the greatest extent possible, thanks to the new method’s speedy problem identification. This state-of-the-art technique improves fault localization, categorization, and identification efficiency. It also reduces the time and computational complexity needed to find faults. It is even more cost-effective because the suggested method is connected to a nearby microgrid, which supplies a small portion of the total electricity produced by the two wind and solar farms. With a fault-detecting efficiency of 97%, the suggested method shows a significant improvement in accuracy.