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Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods

Math H.J. Bollen ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Peter G.V. Axelberg (Institutionen för signaler och system, Signalbehandling) ; Emmanouil Styvaktakis
EURASIP Journal on Advances in Signal Processing (1687-6172). p. 17 pages (Article ID 79747). (2007)
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: power quality, classification, rule-based expert systems, support vector machines, statistical learning, segmentation, feature extraction, event classification



Denna post skapades 2007-01-19. Senast ändrad 2014-01-29.
CPL Pubid: 25864

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling (1900-2017)

Ämnesområden

Signalbehandling
Elkraftteknik

Chalmers infrastruktur