Online fuzzy voltage collapse risk quantification

Many voltage stability indicators have been proposed in the past for the voltage collapse assessment. Almost all of them are determined through quite complex analytical tools; therefore, it is difficult for system operators to give them a physical meaning. In order to perform a simple and reliable evaluation of the security margins, it is necessary to make a synthesis of the information given by the various indices. The present work proposes an Artificial Intelligence-based tool for the evaluation of the voltage security. In particular, a Fuzzy Inference Engine is developed and optimized by two different approaches (Neural Networks and Genetic Algorithms). Starting from the state estimation, a given set of mathematical indices is computed to represent a snapshot of the current electric system operating point. The numerical values are then translated into a set of symbolic and linguistic quantities that are manipulated through a set of logical connectives and Inference Methods provided by the mathematical logic. As a result, the Fuzzy Logic gives a MW measure of the distance from the collapse limit, a metric usually appreciated by system operators.The Fuzzy System has been built and optimized by using, as a test system, a detailed model of the EHV Italian transmission network connected to an equivalent of the UCTE network (about 1700 buses).