Title :
Fuzzy Power Quality Indicator
Author :
Hsiao, Ying-Tung ; Wu, Ying-Ming ; Lee, Yen-Hsing ; Ye, Fun
Author_Institution :
Nat. Taipei Univ. of Educ., Taipei
Abstract :
This paper proposes a novel fuzzy power quality indicator for representing the level of power quality status. In this work, we develop the fuzzy rules, degree membership function and inference rules for identifying the power quality level on six encountered types of power quality events. The traditional power quality indices are not performed well for assessing the status of the power quality because of their hard limits. Hence, this study develops the soft (human thinking like) indices for presenting the serious degree of power quality. Simulation results show the proposed fuzzy power quality indicator is suitable for representing the level of power quality events.
Keywords :
fuzzy reasoning; fuzzy set theory; power supply quality; fuzzy power quality indicator; inference rule; Cybernetics; Fuzzy sets; Fuzzy systems; Harmonic distortion; Humans; Machine learning; Power quality; Power supplies; Total harmonic distortion; Voltage; Fuzzy; Index; Power quality; Voltage quality;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370367