Fuzzy Logic Model for Failure Analysis in Electric Power Distribution Systems

Abstract

The current Ecuadorian regulation in the electricity area establishes that for the evaluation of the quality indices of the electricity service of the distribution companies, all those electric service interruptions with a time greater than three minutes must be considered. Failure to comply with these regulations gives way to a sanctioning regime in which financial fines are imposed for each non compliant index. Endogenous and exogenous agents produce failures in the distribution companies’ infrastructure, causing disconnections in the electricity supply that require the execution of corrective actions, which affect the time of restoration of the service. In this research, a model is proposed for the evaluation of the electric service in the event of a failure, using fuzzy logic and based on the historical information registered by an Ecuadorian electric power distribution company. For the implementation of this model, a statistical analysis was carried out of the records of the time of affectation attributable to four input variables. The simulation was run with free software tools considering four scenarios to evaluate the management of the distribution company with respect to the service reconnection time. The results obtained show that the implemented model is capable of obtaining an output, which by means of adopted membership functions, allowed to express in percentage terms the efficient management of the electricity distribution company. For service reconnection times greater than 27,000 seconds, the electricity distribution company management adopted a very poor membership, while for service reconnection times less than 6,200 seconds, the model solution adopted a very efficient membership. The simulations for the validation of the fuzzy model determined that there is a correspondence between the statistical ranges established for the membership functions of the input variables and the output of the model used by the inference engine.