EVALUATION OF INSULATION FAILURE IN POWER SYSTEM USING ARTIFICIAL INTELLIGENCE

A power system is dynamic because the voltage and the current are not in phase, due to a phase angle difference. These occurrences led to instability, transient, and sub-transient conditions when power is wheeled from the generating station to the load centers. This tends to create undue stress on power machines, which leads to mechanical and electrical shifts from the machine's designed specification standard. This occurrence led to the burning of power facilities, faults in the power systems, the death of system operators, and insulation failures. Insulation failures of electrical machines are more pronounced in the distribution system than in the transmission system. Enugu Electricity Distribution Company (EEDC) was chosen, and the data on insulation failure of the power machine was gathered. EEDC comprises of 18 districts with 64 Injection. Through the secondary data, release by the protection, Control and Metering, 40 percent of the Injection Sub are losing insulation, which was gathered through preventive maintenance. Atani Injection Sub was chosen for investigation. Atani Injection Sub is made of 6 feeders: Housing, Iweka, Water Works, Iyiowa, Wharf, and Premier feeder. An insulation test was run using a megger, and the test result showed that the Iyiowa breaker failed the insulation test. Housing breaker, Iweka breaker, Water Works breaker, Wharf breaker, and Premier breaker are confirmed okay. Artificial intelligence was introduced to detect the insulation level in real time and was infused into EEDC networks in real time. The system was simulated, and the result showed that the artificial intelligence updated the system operator, the insulation level of the power machine, which reduced the rate of failure of the power machine to 10 percent