Authors: Praveen Pankajakshan
Addresses: INRIA Sophia Antipolis - Mediterranee, 2004 route des lucioles, B.P.93, 06902 Sophia-Antipolis Cedex, France
Abstract: In this paper, we propose a framework for the automatic detection and classification of power distribution feeder disturbances based on their underlying causes. The segmentation algorithm based on either a Kalman Filter (KF) or a Wavelet Filter divides the quasi-stationary Root-Mean-Square (RMS) of the captured signal into pre-disturbance, disturbance and post-disturbance regions. The pre- and post-disturbance segments are essentially stationary while the non-stationary nature is extracted as the disturbance segment. Each region is then represented as a sequence of predefined wave patterns or primitives. A syntactically correct combination of these primitives will define the morphology of the mother RMS signal. The grammar, the production rules and the model for each class is built from a set of positive examples (I+) by using a stochastic Error-Correcting Grammar Inference (ECGI) engine. When used in combination with a k-nearest neighbour algorithm (kNN) classifier, this framework can recognise any event and learn new patterns.
Keywords: power disturbance; power quality; TCUL; tap changing under load; wavelets; Kalman filter; ECGI; error correcting grammatical inference; kNN; k-nearest neighbour; power distribution feeder disturbances; disturbance detection; disturbance classification.
International Journal of Computer Applications in Technology, 2009 Vol.35 No.2/3/4, pp.241 - 261
Published online: 20 Jun 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article