| Summary: | 碩士 === 中原大學 === 電機工程研究所 === 95 === Abstract
The electric energy is one of the important energy in human life. It is also an important index for the country development. With the increase of the people income, the life quality is being also paid attention to. Due to new electric equipments, the demand for the electric energy increases gradually. If the load of the power system can be predicted effectively, it can avoid the equipment damage because of overload. Therefore, it can save the manpower and materials, and reduce the operating cost further effectively. This thesis used the feed-forward neural network to perform the short-term reactive power demand forecasting for future reactive power coordination.
This thesis used the multi-layer feed-forward neural network to predict the reactive power. The inputs of the neural network consider for the reactive power demands (3 hours, 4 hours and 5 hours ahead to the present time), date and time, and the output of the neural network is the concerned reactive power demand at the next time.
This thesis used the historical information that Taiwan Power Company provided. It starts February 20, 2006 till February 24, 2006, It includes 524 data sets. The data sets were trained and tested by neural networks. The commercial packages Matlab and Gene Hunter were used. The feed-forward neural network was adopted. The trained weighting factors in the neural network served as parameters for DSP Builder for constructing neural network for forecasting reactive power. The accuracy is verified by errors. Finally, the neural network is realized by Field Programmable Gate Array (FPGA).
|
|---|
