A RSCMAC Based Forecasting for Wind Power from Local Weather Information

• Main Authors: Po-Chun Hsu, 許博鈞
• Other Authors: 江青瓚
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/8vuz65

碩士 === 健行科技大學 === 電機工程系碩士班 === 105 === In recent years, due to limited reserves of fossil fuels and greenhouse gases and other environmental changes, forcing the world have to focus on and try to find a solution. Taiwan as a part of the world community, certainly cannot stay out of the issue, one of the solutions is to develop renewable energy. In recent years, the government not only pass the renewable energy regulations, but also put more efforts to promote the renewable energy industry, proposed “Thousands of sea & land wind turbine project”. The wind power is intermittent energy, with the gradual increase in renewable energy penetration, if lack of forward-looking planning design and appropriate operational strategy, will definitely affect the power supply reliability and power quality of the power system, then reduce the use of renewable energy. If the output power of the wind turbine can be predicted, then its power is possible to be dispatched or have other strategy to stabilize the grid. The research selected Penghu Zhongtun wind turbine park as the test site, there are total 8 German ENERCON E-40 600kW wind turbines installed in the park. In order to collect wind speed information in the park, 3 meteorological monitoring stations were set up in the neighborhood. Based on RSCMAC, using Penghu Zhongtun wind turbine monitoring system data provided by Taipower and regional meteorological monitoring system, three different extremely-short(1 hour) wind turbine power generation prediction models were developed. These three model training and test results showed excellent performance, displayed the availability and stability of the models. In the future, these three high-accuracy wind turbine output power prediction models can provide the optimal dispatch of wind power in the regional grid to achieve the maximum utilized efficiency and also to increase the wind turbine installation capacity.