Wind Power Generation Capacity Evaluation Based on Correlation Analysis of Adjacent Wind Farms

Accurate assessment of the power generation capacity of the wind farm is the key basis for incorporating it into power system scheduling and other optimizing operation activities. Traditional evaluation methods, such as benchmarking wind turbine method, have higher requirements for input data, but many wind farms in operation for many years are difficult to meet such data conditions. A new power generation capacity evaluation method based on correlation analysis of adjacent wind farms is proposed in this paper. Meteorological data and power of adjacent wind farms are used as replaced input data based on correlation analysis between the target farm with its adjacent farms. First, anticipate wind speed data and then construct the wind power curve with three different neural networks to evaluate power generation capacity. Afterwards, adjacent wind farms are used as data sources to fit the power data of the target wind farm with the application of influence radius method, inverse square distance method and bilinear interpolation method. Finally, combined with data in the actual case, investigate the advantages and disadvantages of different methods in the evaluation of power generation capacity.