Intelligent Expert System for Diagnosing Faults and Assessing Quality of Power Transformer Insulation Oil by DGA Method

Abstract

Accurate assessment of oil and fault diagnostics of electrical power transformer insulation for lifelong endurance are the key issues addressed in this research. The durability of a transformer is significantly determined by the quality of its insulation oil, which deteriorates over time due to temperature fluctuations and moisture content. Protecting transformers from potential failure through the early and precise diagnosis of faults and efficient assessment of oil quality during the actual conduct of the operation can avoid sizeable economic losses. The ANFIS Expert System that uses intelligent software plays an important role. The dissolved gas analysis (DGA) in oil is reliable for diagnosing faults and assessing insulation oil quality in transformers. Transformer power plant protection teams often suffer sudden breakdowns that lead to massive damage and huge financial losses. The oil in transformers must be appropriately treated to circumvent such failures. In this research, an ANFIS Expert System was used to diagnose faults and assess insulation oil status and quality in power transformers. by the Rogers ratio method depending on the DGA in oil, a suitable treatment was identified. The graphical user interface from the MATLAB environment was used and proven effective for fault diagnosis and oil quality evaluation. The training algorithm can assess oil quality according to the specifications of the IEEE standard C57-104 and the IEC standard 60599.