NDLI: A rule-based fuzzy logic approach for the voltage collapse risk classification

Content Provider	IEEE Xplore Digital Library
Author	Marannino, P. Berizzi, A. Merlo, M. Demartini, G.
Copyright Year	2002
Description	Author affiliation: Dipt. di Ingegneria Elettrica, Pavia Univ., Italy (Marannino, P.)
Abstract	In recent years, an increasing number of voltage stability indicators have been proposed for voltage collapse assessment. A lot of them are determined by very complex analytical tools and are difficult to interpret by system operators. In the present work, a different direction has been followed: Artificial intelligence (AI) approaches have been exploited, based on fuzzy logic (FL) and artificial neural network (ANN) support. A decision model built on FL has been developed. It receives as input a given set of numerical variables, which are collected to represent a snapshot of the actual operating point for the power system. The set of numerical values is translated into a set of symbolic and linguistic quantities. These variables are manipulated by a set of logical connectives and inference methods provided by mathematical logic. As a final result, the FL approach gives a measure in a percent rate of the security level degradation with respect to the voltage collapse risk. The settled fuzzy inference engine has been built and optimised by utilizing, as a test system, an appropriate equivalent of the EHV Italian transmission network. The results obtained with the FL approach are compared with the ones given by a conventional analytical tool.
Starting Page	876
Ending Page	881
File Size	324956
Page Count	6
File Format	PDF
ISBN	0780373227
DOI	10.1109/PESW.2002.985132
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-01-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Voltage Artificial intelligence Artificial neural networks Power system modeling Power system security Stability Power system measurements Degradation Fuzzy neural networks
Content Type	Text
Resource Type	Article

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