A Robust-Improved Method for Dynamic Equivalent Modeling of Active Distribution Network

Accurate equivalent model can be efficiently to analyze the dynamic properties of Active Distribution Network (ADN) as well as assess their impacts on stabilities of interconnected power system. However, due to stochastic nature of renewable resources and time-varying configurations of loading conditions, the specific operation condition derived equivalent parameter may cause severer robustness issues for conventional ADN equivalent model. To overcome the limitations, this paper developed a robust-improved method for dynamic equivalent modeling of ADN. To sketch out the most representative operation conditions of ADN, with the establishment of system dynamic response based database, two-step clustering method and Fisher Discriminant Analysis (FDA) are used to group the operation conditions featured characteristic data sets. With the key parameter based identification technique applied, the multiple solution issue in parameter identification process could be avoided. Moreover, Elman Neural Network (ENN) is used to derive the robust sets of parameters are able to help the developed equivalent model adaptable for the wide range of distinct ADN operation conditions. To verify the effectiveness of the proposed method, dynamic responses of the proposed ADN equivalent model are compared against the responses of the conventional modeling method for various scenarios through an actual ADN testing case.