Data improvement for ambient signal based load modeling via wide-area state estimation

Considering the component based modeling approach's shortage of requiring the typical load model parameters and the traditional measurement based modeling approach's inadequacy of the dependence on fault-disturbance data, the recently proposed ambient signal based load modeling approach offers an important idea to track the time-varying characteristics of power loads. However, it suffers the impacts of phasor measurement unit (PMU) measurement errors. Consequently, data improvement for ambient signal based load modeling is both important and necessary. In this paper, state estimation is adopted to make the ambient signal based method usable with field PMU measurements. To be specific, a weighted least square method is applied for state estimation to improve the data quality. On this basis, the improved data are used for load model parameter identification to evaluate the performance of the state estimation method from the perspective of data application. Case studies with both simulation data from power system analysis toolbox and field PMU measurements from China southern power grid have verified the effectiveness of the proposed approach.