Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

TL;DR

This paper presents a novel ensemble learning approach using XGB to accurately locate faults in multi-terminal high voltage dc networks, addressing challenges posed by measurement noise and network parameters.

Contribution

It introduces a data-driven fault location method employing XGB, specifically designed for multi-terminal HVdc networks, improving accuracy over traditional techniques.

Findings

High accuracy fault location achieved in simulations

Robustness to measurement noise demonstrated

Effective across various network parameters

Abstract

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).