Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics

Abstract

A large amount of data is generated through monitoring, maintenance, repair and diagnostics of power transformer. However, all these data cannot preindicate the exact type and probability of failure. To overcome the problem this paper presents artificial intelligence based methodology for power transformers fault detection and classification. The possibility of presented monitoring methodology is to assist the operator’s engineers in decision making about urgency of intervention and type of maintenance of power transformer. The article analyzes the application of Mamdani-model and Sugeno-model in fuzzy expert system for fault diagnosis based on the current state of the power transformer. Paper presents two case studies with one unique and five separate controllers. In the first case inputs of controller are results of on-line and off-line transformer tests: age, the overheating temperature of the hot spot, frequency response analysis, temperature of insulation, dissolved gas-in-oil analysis, tgδ and polarization index. Second case study in addition to the existing inputs includes previous measurements. A fuzzy controller (FC) is designed to characterize the operating condition and to determine the urgency of intervention with possibility to indicate probability of specific type of failure. Cumulative probability of occurrence of the faults is also observed in second case study. FCs are tested based on real measurements from Serbian transmission system. The results show acceptable effectiveness in detecting different faults and might serve as a good orientation in the power transformer condition monitoring.