With the increasing penetration of wind power, it is recognized that wind power will have a greater and greater impact on the planning and operation of the original power system. And the detailed modeling of wind farm with doubly-fed induction wind generator (DFIG) will require large storage and computation resources, which poses technical challenges for equivalent modeling of wind farm. In this paper, a multi-machine dynamic equivalent modeling method for wind farms with DFIGs is proposed. First, the artificial bee colony with k-means (ABC-KM) algorithm is proposed to improve the effectiveness of wind farm clustering. Second, the operating data composed of wind speed, pitch angle, rotor angular velocity, rotor current, real-time active and reactive power are selected as clustering indicators. A wind farm with DFIGs is divided into several groups and DFIGs in the same group are clustered as one DFIG through equivalent parameter aggregation. The proposed wind farm modeling method consisting of clustering method and clustering indicators is verified by comparing the simulation results of equivalent and detailed models at steady-state and dynamic-state cases.

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Wind power forecasting is a crucial part for the safe and stable operation of wind power integration, which is under the influence of different factors such as wind speed, wind direction, atmospheric pressure. These factors bring randomness and volatility to wind power which makes it less predictable. While, there are very limited studies on describing the uncertainty of wind power. Therefore, to providing additional information on the uncertainty and volatility, a kernel-based on Gaussian Process Regression (GPR) incorporating the hyper-parameters intelligent optimization method is proposed in this paper. Firstly, the hyper-parameters solution of GPR is formulated as a nonlinear optimization with constraints. Then, an intelligent algorithm named Brain-storming optimization (BSO) is adopted to obtain the optimal hyper-parameters of GPR. Furthermore, the performance is examined on short-term wind power data. Most importantly, the GPR incorporating BSO can avoid the hyper-parameters at local optimum.