Fault detection method for DFIG based on particle swarm optimized sliding mode observer

Abstract

The traditional sliding mode observer can achieve effective fault detection by reconstructing the doubly-fed induction generators (DFIG) model and comparing it with the measurable state quantity. However, unreasonable sliding mode observer parameters will greatly reduce the accuracy of fault detection and even cause false alarms. Aiming at the difficulty of selecting sliding mode parameters, this paper proposes to combine particle swarm optimization (PSO) algorithm with sliding mode observer for fault detection of DFIG. This method can obtain extremely high observation accuracy while minimizing chattering in the observer. First, this paper designs a sliding mode observer based on the mathematical model of the DFIG. Then, the PSO algorithm is used to find the optimal sliding mode observer gain. Finally, the normal operating conditions, the voltage drop fault of the grid terminal and the rotor current sensor fault are set, and on this basis, Simulink simulation models under different fault conditions are established. After comparing the actual rotor current value and the residual error of the observed value, the fault detection is realized. It is proven by simulation that this sliding mode observer can realize fault detection well, and it can be seen that the sliding mode observer has the characteristics of fast response speed and high accuracy