Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods
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Research Article Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods Math H. J. Bollen,1, 2 Irene Y. H. Gu,3 Peter G. V. Axelberg,3 and Emmanouil Styvaktakis4 1 STRI
AB, 771 80 Ludvika, Sweden Lule˚a University of Technology, 931 87 Skellefte˚a, Sweden 3 Department of Signals and Systems, Chalmers University of Technology, 412 96 Gothenburg, Sweden 4 The Hellenic Transmission System Operator, 17122 Athens, Greece 2 EMC-on-Site,
Received 30 April 2006; Revised 8 November 2006; Accepted 15 November 2006 Recommended by Mois´es Vidal Ribeiro This paper presents the two main types of classification methods for power quality disturbances based on underlying causes: deterministic classification, giving an expert system as an example, and statistical classification, with support vector machines (a novel method) as an example. An expert system is suitable when one has limited amount of data and sufficient power system expert knowledge; however, its application requires a set of threshold values. Statistical methods are suitable when large amount of data is available for training. Two important issues to guarantee the effectiveness of a classifier, data segmentation, and feature extraction are discussed. Segmentation of a sequence of data recording is preprocessing to partition the data into segments each representing a duration containing either an event or a transition between two events. Extraction of features is applied to each segment individually. Some useful features and their effectiveness are then discussed. Some experimental results are included for demonstrating the effectiveness of both systems. Finally, conclusions are given together with the discussion of some future research directions. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
With the increasing amount of measurement data from power quality monitors, it is desirable that analysis, characterization, classification, and compression can be performed automatically [1–3]. Further, it is desirable to find out the cause of each disturbance, for example, whether a voltage dip is caused by a fault or by some other system event such as motor starting or transformer energizing. Designing a robust classification for such an application requires interdisciplinary research, and requires efforts to bridge the gap between power engineering and signal processing. Motivated by the above, this paper describes two different types of automatic classification methods for power quality disturbances: expert systems and support vector machines. There already exists a significant amount of literature on automatic classification of power quality disturbances, among others [4–24]. Many techniques have further been developed for extracting features and characterization of power quality disturbances. Feature extraction may apply directly to the original measurements (e.g., RMS values), from some transformed domain (e.g., Fourier and wavelet transforms,
and subband filters) or from the param
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