Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods
Journal article, 2007

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 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 pre-processing to partition the data into segments each representing a duration containing either an event or 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.


feature extraction

support vector machines


statistical learning

rule-based expert systems

event classification

power quality


Math H.J. Bollen

Luleå University of Technology

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Peter G.V. Axelberg

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Emmanouil Styvaktakis

Hellenic Transmission System Operator

Eurasip Journal on Applied Signal Processing

1110-8657 (ISSN) 1687-0433 (eISSN)

17 pages (Article ID 79747)-

Subject Categories

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering



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