Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors
Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors
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DOI：
10.1016/j.enconman.2012.12.012
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发表时间：
2013-05-01
影响因子：
10.4
通讯作者：
Tu, Chia-Sheng
中科院分区：
文献类型：
--
作者：
Hong, Chih-Ming;Chen, Chiung-Hsing;Tu, Chia-Sheng
本文提出了基于最大功率点跟踪（MPPT）的控制算法的最佳风能捕获使用径向基函数网络（RBFN）和提出的转矩观测器MPPT算法。针对永磁同步发电机（PMSG）的无速度传感器控制问题，设计了一种基于改进粒子群优化（MPSO）的反向传播学习算法的高性能在线训练径向基函数网络（RBFN）。在本研究中，采用MPSO来调整RBFN的反向传播过程中的学习速率，以提高学习能力。PMSG由损耗最小化控制控制，MPPT低于基本速度，这对应于低风速和高风速，并且可以从风中捕获最大能量。然后对观测到的扰动转矩进行前馈，以提高永磁同步发电机系统的鲁棒性。(C)2012爱思唯尔有限公司保留所有权利。
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.