Synchronous machine parameter identification using particle swarm optimization

Synchronous machines are the most widely used machines in power generation. Identifying their parameters in a non invasive way is very challenging due to the inherent nonlinearity of machine performance. This presentation proposes a synchronous machine parameter identification method using particle swarm optimization (PSO) with a constriction factor. The PSO allows a synchronous machine model output to be used as the objective function to give a new, more efficient method of parameter identification. This presentation highlights the effectiveness of the proposed method for the identification of synchronous machine model parameters, using both simulation and manufacturers measured experimental data. The presentation also considers the effectiveness of the method as the number of parameters to be identified is increased.