Fast and reliable estimation of composite load model parameters using analytical similarity of parameter sensitivity

This paper proposes a computationally efficient technique for estimating the composite load model parameters based on analytical similarity of parameter sensitivity. When the model parameters are updated in the optimization procedure to best fit the actual load dynamics, i.e., measurements, parameters of similar sensitivity representation in the given mathematical model structure are updated in the same manner at every iterative step. This research allows for practically reducing the number of load model parameters to be identified in the estimation process and the overall computational cost while preserving the desired complexity and accuracy of the original model. This approach consequently facilitates the parameter estimation in the optimization process and helps manage increased number of parameters often criticized for adopting the dynamic composite load model via measurement-based approach. Case studies for the real power system demonstrate the computational efficiency and intact accuracy of the proposed method with reference to the existing methods of estimating all the parameters of the given composite load model independently.