A Hierarchical Framework for Ambient Signals Based Load Modeling: Exploring the Hidden Quasi-Convexity

Abstract

The approach of ambient signals-based load modeling (ASLM) was recently proposed to better track the time-varying changes of load models. To improve computation efficiency and model structure complexity, a hierarchical framework for ASLM is proposed in this paper. Through this framework, the hidden quasi-convexity of the load modeling problem is explored for the first time. This allows more complicated static load model structures and gradient or Hessian-based optimization algorithms to be used. In the upper level, the identification of dynamic load parameters is regarded as an optimization problem. In the lower level, the optimal static load parameters are obtained through linear regression for a given group of dynamic load parameters. Afterward, the regression residuals are regarded as the objective function (OF) of the upper level optimization problem. The proposed method is validated by the case study results on the Guangdong Power Grid. The results show that the OF is mostly quasi-convex after the transformation of the induction motor model, which provides the basis for the application of gradient or Hessian-based optimization algorithms. The case study results also validate that the proposed approach has better computation efficiency and model structure complexity compared with the previous ASLM approaches.