Study Of Small Wind Power Projects MCP Wind Resource Analysis Based On Short-term Data

At present,due to the energy crisis and environment pollution problem are Increasingly prominent,people pay more and more attention to renewable energy and green energy.According to national standards,wind resource assessment requires at least one year of continuous measurement of wind farms hourly wind speed and direction data.However,in the actual project,a wind farm to be developed is often not measured wind data,or short-term wind data only 2 to 3 months.Small-scale wind power projects,the investment cost is very low,continuous field measure ments a year is too expensive,as a constraint to the development of small-scale wind power project is a major problem.Therefore,the study of new methods to shorten the period of the wind farm site measurements,the promotion of China's wind power development will be great significance.The feasibility of predicting the long-term wind resource at 4 sites using a measure-correlate-predict(MCP)approach based on just three months onsite wind speed measurements has been investigated.Three regression based techniques were compared in terms of their ability to predict the wind resource at a target site based on measurements at a nearby reference site.The accuracy of the predicted parameters of mean wind speed,mean wind power density,standard deviation of wind speeds and the Weibull shape factor was assessed,and their associated error distributions were investigated,using long-term measurements recorded over a period of 1 year.For each site,12 wind resource predictions covering the entire data period were obtained using a sliding window approach to account for inter-annual and seasonal variations.Both the magnitude and sign of the prediction errors were found to be strongly dependent on the season used for onsite measurements.Averaged across 4 sites and all seasons,the best performing MCP approach resulted in mean absolute and percentage errors in the mean wind speed and 4.8 respectively,and in the mean wind power density 14.The average errors were reduced to 3.6%in the mean wind speed and 10%in the mean wind power density when using the optimum season for onsite wind measurements.These values were shown to be a large improvement on the predictions obtained using an established semi-empirical model based on boundary layer scaling.The results indicate that the MCP approaches applied to very short onsite measurement periods have the potential to be a valuable addition to the wind resource assessment tool fit for small-scale wind developers.