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- W2011665371 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." @default.
- W2011665371 created "2016-06-24" @default.
- W2011665371 creator A5075744779 @default.
- W2011665371 date "2009-01-01" @default.
- W2011665371 modified "2023-09-27" @default.
- W2011665371 title "A unified approach for determining the underlying causes of non-stationary disturbances" @default.
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- W2011665371 doi "https://doi.org/10.1504/ijcat.2009.026601" @default.
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