1 research outputs found
Ambient Signals based Load Modeling with Combined Gradient-based Optimization and Regression Method
Load modeling has been an important issue in modeling a power system. Ambient
signals based load modeling approach has recently been proposed to better track
the time-varying changes of load models caused by the increasing uncertain
factors in power loads. To improve the computation efficiency and the model
structure complexity of the previous approaches, a combined gradient-based
optimization and regression method is proposed in this paper to identify the
load model parameters from ambient signals. An open static load model structure
in which various static load models can be applied, together with the induction
motor as the dynamic load model, are selected as the composite load model
structure for parameter identification. Then, the static load model parameters
are identified through regression, after which the induction motor parameters
can be obtained through optimization with the regression residuals being the
objective function. After the transformation of the induction motor model, the
objective function is quasiconvex in most of the feasible region so that the
gradient-based optimization algorithm can be applied. The case study results in
Guangdong Power Grid have shown the effectiveness and the improvement in
computation efficiency of the proposed approach.Comment: 8 pages, 13 figure