Dependency Analysis and Improved Parameter Estimation for Dynamic Composite Load Modeling

TLDR: This paper presents a general framework that can effectively analyze and visualize the parameter dependence of complex dynamic load models with large numbers of parameters under FIDVR and proposes to improve the parameter estimation performance by regularizing the NLS error objective using a priori information about parameter values.

Abstract: Dynamic load modeling by fitting the input–output measurements during fault events is crucial for power system dynamic studies. The WECC composite load model (CMPLDW) has been developed recently to better represent fault-induced delayed-voltage-recovery (FIDVR) events, which are of increasing concern to electric utilities. However, the model nonlinearity and large number of parameters of the CMPLDW model pose severe identifiability issues and performance degradation for the measurement-based load modeling approach using the classical nonlinear least-squares (NLS) objective. This paper will first present a general framework that can effectively analyze and visualize the parameter dependence of complex dynamic load models with large numbers of parameters under FIDVR. Furthermore, we propose to improve the parameter estimation performance by regularizing the NLS error objective using a priori information about parameter values. Effectiveness of the proposed dependence analysis and parameter estimation scheme is validated using both synthetic and real measurement data during faults. Albeit focused on CMPLDW, the proposed approaches can be readily used for composite load modeling in general.