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Wind power forecasting is presently one of the challenging tasks to deal with supply-demand balance in modern electric power systems. Accurate wind power predictions are needed to reduce the risk in uncertainty and enable for better dispatch, scheduling, and power system integration. This article deals with the challenge of wind power forecasting by proposing the application of the forecasting methodology using the wavelet packet decomposition principles, neuro-fuzzy systems, as well as the benefits of data preprocessing and forecast combination framework. The used data consist of the quarter-hourly observations of wind power generation in France, and the proposed method is used to ensure forecasts for a time horizon of an hour-ahead. The obtained results indicate the superior accuracy of the proposed model with an average mean absolute percentage error of 3.408%, which means there is possibility to construct a high-precision method using only the historical wind power data.

The brushless doubly-fed machine (BDFM) continues to attract increasing interest for applications in wind generation where, robustness and low servicing costs are its principles advantages. The construction aspect of the BDFM has been widely studied and currently this machine can be build with good performances. However, the control aspect remains difficult to achieve and some studies show that the BDFM is less stable than the doubly-fed induction machine. To explore the BDFM stability in all operating mode, this paper proposes a stability analysis of a grid-connected variable speed wind turbinebased BDFM. For this purpose, a linearized small signals mathematical model is proposed which takes into account both grid and control disturbances. Then, the effect of electrical parameters variation and operating speed change on the stability of the BDFM has been studied. The stability has been investigated through simulation implementation. The obtained results demonstrate the validity and the superiority of the proposed model.