TY - JOUR
T1 - Intelligent wind power smoothing control with BESS
AU - Lin, Faa Jeng
AU - Chiang, Hsuang Chang
AU - Chang, Jin Kuan
AU - Chang, Yung Ruei
N1 - Publisher Copyright:
© 2016 The Institution of Engineering and Technology.
PY - 2017
Y1 - 2017
N2 - An intelligent wind power smoothing control using recurrent fuzzy neural network (RFNN) 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 RFNN 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 RFNN 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. The minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the RFNN power smoothing control. 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.
AB - An intelligent wind power smoothing control using recurrent fuzzy neural network (RFNN) 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 RFNN 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 RFNN 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. The minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the RFNN power smoothing control. 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.
UR - http://www.scopus.com/inward/record.url?scp=85017544284&partnerID=8YFLogxK
U2 - 10.1049/iet-rpg.2015.0427
DO - 10.1049/iet-rpg.2015.0427
M3 - 期刊論文
AN - SCOPUS:85017544284
SN - 1752-1416
VL - 11
SP - 398
EP - 407
JO - IET Renewable Power Generation
JF - IET Renewable Power Generation
IS - 2
ER -
