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SVM-based PQ disturbance recognition system


Huang, J and Jiang, Z and Rylands, L and Negnevitsky, M, SVM-based PQ disturbance recognition system, IET Generation Transmission and Distribution, 12, (2) pp. 328-334. ISSN 1751-8687 (2017) [Refereed Article]

Copyright Statement

The Institution of Engineering and Technology 2017

DOI: doi:10.1049/iet-gtd.2017.0637


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.

Item Details

Item Type:Refereed Article
Keywords:support vector machine (SVM)-based techniques, discrete wavelet transform (DWT)
Research Division:Engineering
Research Group:Control engineering, mechatronics and robotics
Research Field:Field robotics
Objective Division:Manufacturing
Objective Group:Machinery and equipment
Objective Field:Machinery and equipment not elsewhere classified
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:124531
Year Published:2017
Web of Science® Times Cited:9
Deposited By:Engineering
Deposited On:2018-02-23
Last Modified:2018-12-14

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