Date of Award

May 2019

Degree Type

Thesis

Degree Name

Master of Science

Department

Engineering

First Advisor

Lingfeng Wang

Committee Members

Yi Hu, Zeyun Yu

Keywords

Faults detection, Faults diagnosis, HVDC, wavelet transform

Abstract

ABSTRACT

WAVELET TRANSFORM BASED METHODS FOR FAULT DETECTION AND DIAGNOSIS OF HVDC TRANSMISSION SYSTEMS

by

Zhonxguan Li

The University of Wisconsin-Milwaukee, 2019

Under the Supervision of Professor Lingfeng Wang

High-voltage direct current (HVDC) is a key enabler in power system. HVDC offers a most efficient means of transmitting large amount of power. Applications of HVDC can improve the operation security, reliability performance and economy of power systems. Due to factors inside and outside the HVDC system, the system will experience various faults, which have infected HVDC system. VSC-HVDC is a HVDC transmission based on IGBT and PWM. VSC-HVDC direct current transmission has broad application prospects in new energy grid-connected and grid-connected transformation. In this research, aiming at the fault diagnosis of VSC-HVDC, the fault diagnosis and fault detection are studied.

In this research, a VSC-HVDC was simulated in MATLAB Simulink, and an adjusted VSC-HVDC model was built. The models were applied to simulate the basic operation of VSC-HVDC and main faults on AC and DC side in the VSC-HVDC system. Take line current on AC or DC side as input data, the result data after wavelet processing was applied in HVDC faults diagnosis. To verify the function of fault detection, DC faults at different locations were set in the adjusted model. Wavelet entropy was applied in fault diagnosis and detection to gather accurate results.

According to the simulation results, wavelet transform exhibits a good performance in HVDC fault diagnosis and detection.

Available for download on Friday, June 05, 2020

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