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With the increasing capacity of grid connected wind farms, the influence of wind power to stable operation of an electric power system is becoming more and more important. In order to analyze the active power output characteristics of wind farm, a multimachine representation dynamic equivalent method based on the fuzzy clustering algorithm is proposed. First, indicators which can characterize the active power output performance of a doubly fed induction wind generator (DFIG) are researched. Second, a fuzzy C-means (FCM) clustering algorithm is first applied to the modeling of wind farm. DFIGs are divided into groups by analyzing the indicator data with FCM. Finally, DFIGs of the same group are equivalent as one DFIG to realize the dynamic equivalent modeling of wind farm with DFIG. Simulation results demonstrated that the established dynamic equivalent model can reflect the active power dynamic response characteristics of wind farm with DFIG effectively; meanwhile, the model of wind farm is simplified and computation complexity is reduced. Index Terms-Active power characteristic analysis, dynamic equivalent model, fuzzy clustering algorithm, multimachine representation method, wind farm with double fed induction wind generator (DFIG). 0885-8969

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SUMMARYThe problem of H 1 deconvolution filter design for a class of singular Markovian jump systems with time-varying delays and parameter uncertainties is considered in this paper. By constructing a more comprehensive stochastic Lyapunov-Krasovskii functional, novel delay-dependent conditions are established to guarantee the filtering error system is not only stochastically admissible, but also satisfies a prescribed H 1 -norm level for all admissible uncertainties. The desired filter parameters can be obtained by solving a set of strict linear matrix inequalities. Two examples and an electrical RLC circuit example are employed to verify the effectiveness and usefulness of the proposed methods in the paper.