.png){kind=icon}
返回
Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors
.png){kind=icon}
PRE
AI
DOI：10.1016/j.enconman.2012.12.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents maximum-power-point-tracking (MPPT) based control algorithms for optimal wind energy capture using radial basis function network (RBFN) and a proposed torque observer MPPT algorithm. The design of a high-performance on-line training RBFN using back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller for the sensorless control of a permanent magnet synchronous generator (PMSG). The MPSO is adopted in this study to adapt the learning rates in the back-propagation process of the RBFN to improve the learning capability. The PMSG is controlled by the loss-minimization control with MPPT below the base speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. Then the observed disturbance torque is feed-forward to increase the robustness of the PMSG system. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Radial basis function network (RBFN)
Modified particle swarm optimization (MPSO)
Wind turbine generator (WTG)
Permanent magnet synchronous generator (PMSG)
Maximum power point tracking (MPPT)
期刊
IF:
10.9
论文数:
2.0W
被引数:
11.3W
机构
引用论文
.png){kind=icon}
分享收藏Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization用于功率捕获优化的DFIG变速风力发电机的非线性风估计控制
PREAI
分享收藏Mobilization of mercury and arsenic in humans by sodium 2,3-dimercapto-1-propane sulfonate (DMPS).
OAAI
OAAI分享收藏
查看更多内容