Authors:
Mennan Güder
;
Özgül Salor
and
Işık Çadırcı
Affiliation:
The Scientific and Technological Research Council of Turkey, Turkey
Keyword(s):
Machine Learning, Knowledge Discovery, Power Quality Mining, Feature Construction, Feature Extraction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Foundations of Knowledge Discovery in Databases
;
Information Extraction
;
Integration of Data Warehousing and Data Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Pre-Processing and Post-Processing for Data Mining
;
Soft Computing
;
Symbolic Systems
Abstract:
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.