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• W29299046 abstract "Abstract: In the recent years, an increasing number of voltage stability indicators has been proposed for the voltage collapse assessment. Almost all of them are determined by very complex analytical tools and are difficult to be interpreted by the system operators. In the present work, a preliminary study to implement an Artificial Intelligence (AI)-based tool for the analysis of voltage instabilities is performed. The goal is to identify the most important parameters to be computed to help the system operator in the evaluation of the stability margins of an electric transmission network. The AI system receives as an input a given set of nume rical indices, which are collected to represent a snapshot of the current electric system operating point. The set of numerical values is translated into a set of symbolic and linguistic quantities by m eans of the Fuzzy Logic (FL). Each index is manipulated by a set of logical connectives and of Inference Methods provided by the Mathematical Logic, in order to translate it into a very meaningful and easy to use measure of the MW distance from the stability limit. The Fuzzy Inference Engine has been built and optimized by utilizing as test system an appropriate model of the EHV Italian transmission network connected to an equivalent of the UCTE network (about 1500 busses)." @default.
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• W29299046 date "2004-01-01" @default.
• W29299046 modified "2023-09-26" @default.
• W29299046 title "A Neuro-Fuzzy Inference System for the Evaluation of Voltage Collapse Risk Indices" @default.
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