Multi-criteria analysis of fuzzy symptoms of electrical faults in power systems

Abstract

The paper considers a possible method of technical diagnostics of electrical equipment of power supply systems and electrical substations based on the fuzzy sets and fuzzy logic. It is shown that, based on the matrix of fuzzy relationships, one can make a plausible enough prediction about possible malfunctions and causes of failures. The prerequisites for this analysis are the current condition (state) of the electrical equipment and expert assessments of diagnostic signs. The paper shows the comparison made using the features scale of Saaty, in accordance with nine degrees of preference. At the core of fuzzy expert assessments is an attempt to formalize linguistic information, namely linguistic variables whose meanings can be words or phrases. The paper presents a complete range of preconditioned defects consisting of m factors and their corresponding space conclusions as to the causes of these malfunctions (defects) of n symptoms. Fuzzy causal relations in the space of underlying factors are established between the assumptions and conclusions of the experts. The resulting system of equations is solved by the method based on the composition of fuzzy conclusions. Possible failures are ranked according to the experts' preference, which reveals the most significant symptoms of malfunctioning and allows arriving at the conclusion as to the future operation of the facility. The validity of the provisions of the method presented is confirmed by appropriate calculations, which demonstrates the correct behavior of the model concerning the transformer equipment. It is shown that in case of the fuzzy symptoms occurrence and evaluation of these features by a scale of preferences, it is possible to conclude about the further operation of electrical equipment or its withdrawal for repair. Thus, the mathematical model based on the fuzzy relations of symptoms selected using the experts' estimations contains elements of predicting the possible failures of power systems electrical equipment. © 2018 WIT Press.