Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation, risk arising from power transformer faults is of much uncertainty and complicacy. To timely and objectively control the risks, a transformer risk assessment method based on fuzzy analytic hierarchy process (FAHP) and artificial neural network (ANN) from the perspective of accuracy and quickness is proposed. An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix; with utilization of adaptive ability and nonlinear mapping ability of the ANN, the risk factors with large weights are used as input of neutral network, and thus intelligent quantitative assessment of transformer risk is realized. The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.