A novel method for winding faults diagnostic of power transformers based on parameter estimation

This paper presents a parameter estimation based method to diagnose winding deformation and turn-to-turn fault of power transformers. First, an estimation model of transformer parameters is built, in which five equations are taken in account including voltage loop equation, active loss equation, input impedance equation, no-load current equation and no-load loss equation. Then, particle swarm optimization (PSO) is used to solve the model, and the characteristics of estimation data under winding faults are analyzed. At last, based on the estimation data, random forest (RF) algorithm is employed to classify transformer states and realize fault diagnostic. The simulation results show that the proposed parameter estimation method has high precision and are not affected by the factors including load power factors and the type and degree of winding deformation, and the impact of load rates can be avoided; the fault diagnostic scheme based on RF is quite sensitive and effective. The proposed method eliminates the need of transformer outage, and compared with other methods based on parameter estimation, it can distinguish winding deformation and turn-to-turn fault. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.