Artificial intelligence based thermographic approach for high voltage substations risk assessment

This study deals with the condition monitoring of high voltage equipment (HVE) and risk assessment in transmission power systems. Artificial intelligence is applied to use the currently available information to calculate operational risk, and take appropriate operational measures to deal with the upcoming system states. The study presents a method based on the thermographic approach for determining urgency of intervention of HVE. Age of the HVE, voltage level, overheating temperature of the hot spot, temperature of the previous overheating, dissolved gas analysis, frequency response analysis, temperature of oil insulation, the number and time of operations and gas leaking have been used as the reference inputs for the designed fuzzy controller. The results of such methods have been combined with economic factors and applied in risk maps. The method of minimal paths and method of minimal cross-section are modified to use fuzzy numbers. These methods are used to analyse the performance of high voltage substations. System performance index is calculated to make a proper decision about reconfiguration and maintenance planning. The results might serve as a good orientation in the HVE condition monitoring and they are implemented in the asset management of transmission power systems using risk map.