  Load modeling is an important issue in modeling a power system. 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
load modeling problem is explored for the first time, and more complicated
static load model structures can be applied. In the upper stage, the
identification of dynamic load parameters is regarded as an optimization
problem. In the lower stage, the optimal static load parameters are obtained
through linear regression for a given group of dynamic load parameters.
Afterwards, the regression residuals are regarded as the objective function
(OF) of the upper stage optimization problem. The proposed method is validated
by the case study results in Guangdong Power Grid. The results have shown that
the OF is mostly quasi-convex after the transformation of induction motor
model, which provides the basis for the application of gradient-based
optimization algorithm. The case study results also validate that the proposed
approach has better computation efficiency and model structure complexity
compared with the previous ASLM approach.
