Two-staged simplified fuzzy inference for dynamic contingency screening in power systems

This paper proposes a two-staged simplified fuzzy inference method for dynamic contingency screening in power systems. It plays a key role in classifying power system conditions into stable and unstable states in dynamic security control. Simplified fuzzy inference is used to express the nonlinear relationship between input and output variables. It's advantage is that it is less time-consuming and is easier to understand due to the crisp output variable. To improve the solution accuracy, this paper tunes up the fuzzy membership functions of input variables with tabu search and introduces the supervised learning into the fuzzy rules. Two-staged simplified fuzzy inference is proposed to enhance the solution accuracy. At the second stage, additional fuzzy membership functions are assigned to areas that give erroneous results at the first stage. The proposed method is successfully applied to sample systems. A comparison is made between the proposed and ANN methods.