Robust Dynamic Equivalent Modeling of Active Distribution Network With Time-Varying Parameters

The uncertainties from distributed generators (DGs) and load behaviors pose more challenges to traditional dynamic equivalent modeling of the active distribution networks (ADNs). Parameter identification of ADN equivalent models is affected by various dynamic components with time-varying characteristics. Moreover, multi-solution issue, parameter mutation, and continuous outliers further complicate the problem. Therefore, a robust dynamic equivalent modeling approach for ADN with time-varying parameters is proposed in the paper. To effectively avoid the multi-solution issue, a parameter sensitivity and decoupling based dominant parameter discrimination scheme have been designed. Then, a time-varying parameter identification algorithm with the directional forgetting technique (DFT) is developed to continuously update model parameters, especially when only some parameters change. Furthermore, a continuous outlier detection and suppression approach is proposed to eliminate the impacts of low-quality measurements. Case studies and simulations on the actual and modified ADNs verified the effectiveness of the proposed approach.