Intelligent Wind Power Smoothing Control using Fuzzy Neural Network

Faa Jeng Lin, Shih Gang Chen, Jin Kuan Chang

研究成果: 雜誌貢獻會議論文同行評審

1 引文 斯高帕斯(Scopus)


An intelligent wind power smoothing control using fuzzy neural network (FNN) is proposed in this study. First, the modeling of wind power generator and the designed battery energy storage system (BESS) are introduced. The BESS is consisted of a bidirectional interleaved DC/DC converter and a 3-arm 3-level inverter. Then, the network structure of the FNN and its online learning algorithms are described in detail. Moreover, actual wind data is adopted as the input to the designed wind power generator model. Furthermore, the three-phase output currents of the wind power generator are converted to dq-axis current components. The resulted q-axis current is the input of the FNN power smoothing control and the output is a gentle wind power curve to achieve the effect of wind power smoothing. The difference of the actual wind power and smoothed power is supplied by the BESS. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the FNN power smoothing control. In the experimentation, a digital signal processor (DSP) based BESS is built using two TMS320F28335. From the experimental results of various wind variation sceneries, the effectiveness of the proposed intelligent wind power smoothing control is verified.

期刊E3S Web of Conferences
出版狀態已出版 - 27 11月 2018
事件2018 International Conference Green Energy and Smart Grids, GESG 2018 - Irkutsk, Cape Hadartha, Russian Federation
持續時間: 27 7月 20182 8月 2018


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