A Hybrid Method Based on Optimized Neuro-Fuzzy System and Effective Features for Fault Location in VSC-HVDC Systems

Reza Rohani,Amangaldi Koochaki

doi:10.1109/access.2020.2986919

Reza Rohani, Amangaldi Koochaki

Open Access

https://doi.org/10.1109/access.2020.2986919

Copy DOI

Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 16	License type: CC BY 4.0

Affiliation: Islamic Azad University, Aliabad Katoul

Abstract

One of the most important issues with HVDC systems are the occurrence of various faults that can lead to considerable electrical power losses, serious damage to expensive equipment and huge financial losses. Hence, it is highly required to design an accurate and automatic fault location method in HVDC systems for maintaining uninterrupted supply of energy and protecting sensitive equipment such as rectifiers and inverters. Accordingly, this paper proposes a new hybrid system based on adaptive neuro-fuzzy inference system (ANFIS) with optimal parameters and Hilbert-Huang (HH) transform for fault location in voltage sourced converter-HVDC (VSC-HVDC) systems. The proposed fault location method consists of three major sections. In the first section, HH transform is applied to extract new features from current signal. In the second part, ANFIS uses the extracted features to estimate the fault location in transmission lines. Learning algorithm determines the accuracy and efficiency of each machine-learning algorithm. In the third section of the developed system, enhanced version of particle swarm optimization (PSO) algorithm named chaotic dynamic weight PSO (CDWPSO) algorithm is implemented as learning algorithm to train the ANFIS. The developed fault detection and location system was tested on a VSC-HVDC system with 250 km length and the obtained results using MATLAB simulations have shown that combination of new features, and CDWPSO-based ANFIS has high accuracy in fault detection and location in VSC-HVDC systems. High fault location accuracy, robust performance of neuro-fuzzy system, optimal training of ANFIS, extraction of novel effective features from current signal and fault location only with six features are the main contribution of the developed system.

Highlights

High-voltage direct current transmission lines or HVDC systems are an effective option for transmission of huge amounts of electrical energy from power plants that are located in remote areas such as offshore wind farms [1], [2]
The main contribution of the proposed method is improving the performance of adaptive neuro-fuzzy inference system (ANFIS) by using new learning algorithm, improving the robustness of ANFIS, extracting new effective features from current signal and enhancing the fault location accuracy in voltage source converters (VSC)-HVDC systems
In this study, an intelligent and accurate method based on ANFIS proposed for fault location in VSC-HVDC systems

Summary

Introduction

High-voltage direct current transmission lines or HVDC systems are an effective option for transmission of huge amounts of electrical energy from power plants that are located in remote areas such as offshore wind farms [1], [2]. These systems have crucial function in modern power grids due to their potential of long distance transmission and large capacity [3]–[5]. Over the past few decades, the appearance of semiconductor-based power devices, especially insulatedgate bipolar transistors (IGBT), have led to the development of voltage source converters (VSC) for transmission of electrical power. Serious damage to expensive and sensitive equipment such as converters and rectifiers, and huge financial losses are some destructive consequences of an undetected fault within

Methods

Results

Conclusion