Abstract
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 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 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.
Highlights
Electric load modeling is essential for power system stability analysis and control [1]
A recursive Bayesian-based approach for the online automatic identification of generalized load models has been presented in this paper
2) The multi-model framework with a certain number of sub-models is suitable for describing the diversity of load in various conditions
Summary
Electric load modeling is essential for power system stability analysis and control [1]. In [23] and [24], the Gibbs sampling method combined with Bayesian estimation is proposed for estimating the distribution of the parameters of load model. All those SA-based load modeling methods are only implemented offline in a single model framework. The aim of this paper is to introduce a new recursive Bayesian-based approach for online automatic identification of generalized electric load models in a multi-model framework. Compared to the aforementioned works, the main contributions of this paper are as follows: 1) The generalized load characteristics are described in a multi-model framework and identified using a recursive Bayesian approach.
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