INTEGRATED INSTANCE-BASED AND KERNEL METHODS FOR POWER QUALITY KNOWLEDGE MODELING

In this paper, an integrated knowledge discovery strategy for high dimensional spatial power quality event data is proposed. Real time, distributed measuring of the electricity transmission system parameters provides huge number of time series power quality events. The proposed method aims to construct characteristic event distribution and interaction models for individual power quality sensors and the whole electricity transmission system by considering feasibility, time and accuracy concerns. In order to construct the knowledge and prediction model for the power quality domain; feature construction, feature selection, event clustering, and multi-class support vector machine supervised learning algorithms are employed.