A unified approach for determining the underlying causes of non-stationary disturbances
Praveen Pankajakshan · International Journal of Computer Applications in Technology · 2009
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.