A New Identification Strategy for Improving Convergence Stability of Load Model Parameters

For current practices on the measurement-based load modeling, the dispersity of the identification results is an obstacle in the application of the load model. Based on the improvement of the basic genetic algorithm and the sensitivity analysis of composite load model parameters, this paper proposed a new identification strategy, in which the majority of parameters are fixed and only the parameters with higher sensitivity are identified. Case study shows that the proposed strategy not only provides an effective way to overcome the dispersity of load model parameters, but it also improves the efficiency of identification.