University of Tasmania
Browse

File(s) under permanent embargo

SVM-based PQ disturbance recognition system

journal contribution
posted on 2023-05-19, 16:21 authored by Huang, J, Jiang, Z, Rylands, L, Michael NegnevitskyMichael Negnevitsky
The quality of power delivered by modern electricity grids is of interest as disturbances to power quality (PQ) have the potential to cause malfunction of control systems, interfere with communication networks, increase power losses and reduce the life of electrical components. It is, therefore, necessary to determine if there are PQ disturbances in a grid, and if so what forms these disturbances take. On the basis of site measurements at power distribution systems, a waveform generator is designed to emulate 11 types of PQ disturbances as well as harmonics, and a prototype for recognising these undesirable disturbances is presented. The first step is to use the discrete wavelet transform (DWT) to extract the most representative transients at different time spans from the original waveform. The second step is to use the output of the DWT to construct two sets of classifiers, which can recognise the types of disturbances present. Non-linear support vector machine (SVM)-based techniques are exploited for this step. Case studies are carried out to verify the prototype system. Simulations show that the SVM classifiers developed can achieve superior performance in recognising PQ disturbances compared with conventional counterparts.

History

Publication title

IET Generation Transmission and Distribution

Volume

12

Pagination

328-334

ISSN

1751-8687

Department/School

School of Engineering

Publisher

The Institution of Engineering and Technology

Place of publication

United Kingdom

Rights statement

© The Institution of Engineering and Technology 2017

Repository Status

  • Restricted

Socio-economic Objectives

Machinery and equipment not elsewhere classified

Usage metrics

    University Of Tasmania

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC