A new Approach for Parameter Estimation of Power System Equipment Models

Modeling and simulation are of great importance in power systems studies. In order to ensure the accuracy of the results, model validation is performed. This paper presents a new approach using the Particle Swarm Optimization (PSO) algorithm for governor parameter estimation and identification. To estimate unknown parameters, the system frequency and turbine power signals following several events are used. In the proposed method, to recognize the most effective parameters in each event, a sensitivity analysis technique is applied. Next, the most influential intervals that parameters have greater impact on system response are selected. The unknown parameters are then estimated using the selected time intervals. The proposed technique is tested on IEEE 9-bus test system using DIgSILENT power factory software. Studies' results demonstrate the efficiency of the proposed technique especially in reducing the computation burden and obtaining faster convergence.