Abstract
The accuracy of the model of each equipment component impacts whether the power system simulation results can exactly reflect the operating behavior of the actual system, and the complexity and uncertainty of the load models are the highest among the rest. Adequate modeling of loads which can capture essential dynamic behaviors of the power system can indeed improve the factuality of stability simulations and the reliability of electric network analysis. In the literature, two distinct categories of dynamical load modeling approaches which are commonly used: measurement-based and component based method. This thesis will focus on the measurement-based method. The extreme learning machine (ELM) is adopted for identifying and validating of various dynamical load models. The proposed approach will consist of the following two steps. First, by considering the superior capability of mapping the input-output relationship and generalization performance, the load power was predicted by ELM with measured data. Then, it is expected that the dynamic response of the load power can be accurately estimated without using pre-defined physical load models. In this thesis, ELM was also used for parameter identification of static exponential load model. The contingency events for load model parameter identification were generated with PSS/E user-defined model. The predicted dynamical load models were compared with the default values set in the user-defined model. The predicted values were also used for validating the model of load power. Simulation results of both IEEE 9 bus and IEEE 39 bus system have shown satisfactory results.