Neural Networks Based MPPT for Wind Power Generation

• Main Authors: Yi-ming Houng, 黃意明
• Other Authors: Hong-Tzer Yang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/71789319958659574097

碩士 === 中原大學 === 電機工程研究所 === 94 === 　　The wind-turbine generation system (WTGS) exhibits a nonlinear characteristic and thus its maximum power point varies with changing atmospheric conditions. In order to operate the WTGS at maximum power points under different wind speeds and to avoid using anemometer in practical applications, the thesis adopts neural network base maximum power points tracking (MPPT) control theory in the WTGS. 　　In the thesis, load characteristic models of the WTGS under different wind speeds are first built up for design of control rules and feasibility studies of the proposed MPPT methods. We realize and compare the traditional current-type perturbation & observation (P&O) algorithm, three-point-weighting comparison (TPWC) algorithm, variable-speed wind turbine power control method, as well as the proposeed neural networks based MPPT method. In the practical system implementations, the MPPT methods are integrated with the TMS320C240 digital signal processor to adjust the duty ratios of the buck converter to operate the WTGS at maximum power outputs. 　　To compare and verify the effectiveness of the four MPPT control methods mentioned above, a practical WTGS has been used. The WTGS includes a small wind turbine with three 1.17m-diameter blades and a three-phase, 12-pole, 100W, small permanent-magnet synchronous generator. The experimental results show that the neural networks based MPPT method can reach maximum power points in different wind-speed conditions without using anemometer, and it can solve the oscillation problems around the maximal power output point in traditional P&O and TPWC algorithms.