To improve the wind energy production and profit

it is imperative to optimize the power output of wind turbine generator(WTG). This paper extracts the analytical relation between WTG power

wind profile and control variables from historical operation data with a designed feed-forward neural network. Based on such a relation

point-to-point and cluster optimization strategies are developed and used for WTG power optimization

which optimize WTG control variables for maximum WTG power under the wind profile measured. The K-means clustering algorithm is used in the latter strategy to reduce optimization complexity

thus facilitating real-time WTG power optimization. Three new indices of mean power gain(MPG)

rate of power gain(RPG)and probability of power gain(PPG)are proposed to quantify the power gains by the optimization strategies proposed. Extensive comparisons are conducted between two proposed strategies and recorded operation data using H56-850 WTG. Results show that both strategies can optimize WTG power output. In addition

cluster strategy with five clustering centers could considerably reduce optimization complexity while achieving similar effectiveness as that of the point-to-point strategy. This work is supported by National Natural Science Foundation of China(No. 51307185)and State Grid Corporation of China(No. SGCQDK00DJJS1500056).