Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework

Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework

Blog Article

Electric loads are essential for power system dynamic simulation.However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load.When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more difficult.In this paper, a new solution for determining the unknown generalized load model is proposed.The radial basis function (RBF) neural network-based sub-models of generalized ALOE JUICE INNER load are stored in the form of a sub-model bank.

A recursive Bayesian approach is used to identify the sub-models and then merge them into one generalized load model according to their probabilities.The proposed method can be implemented a Half online and adapt to describing the diversity and time-varying behavior of the generalized load.Numerical studies are carried out using both simulation data and actual measurements.The comparisons with other load modeling methods verify the advantages of the proposed method.