ABSTRACT

This paper proposes a dynamic maximum power point tracking (MPPT) controller for a permanent magnet synchronous generator (PMSG)-based variable speed autonomous wind power conversion system (WPCS) with an energy storage system (ESS). The dynamic controller extracts the maximum power from the WPCS under variable speed conditions using a cascade neural network (CNN)-based MPPT algorithm. Also, the efficient power management between an ESS, load and WPCS is obtained using a conventional PI controller. The CNN is trained and tested using simulation and experimental data, respectively. The performance of CNN-based MPPT is also compared with feed-forward-based MPPT in terms of accuracy and complexity. This paper also discusses FPGA implementation aspects of CNN-based MPPT and hardware resources management for desired accuracy. The proposed method is first implemented on a simulation model 900-watt PMSG-based WECS and results are validated. Further, the enthusiastic results were finally evaluated in a hardware setup of 1 HP PMSG-based WECS. The proposed method is shown to extract maximum power with a simple structure and provide a better response to variable wind speed.

摘要

本文针对具有储能系统 (ESS) 的基于永磁同步发电机 (PMSG) 的变速自主风电转换系统 (WPCS) 提出了一种动态最大功率点跟踪 (MPPT) 控制器。动态控制器使用基于级联神经网络 (CNN) 的 MPPT 算法在变速条件下从 WPCS 提取最大功率。此外，ESS、负载和 WPCS 之间的高效电源管理是使用传统的 PI 控制器获得的。CNN 分别使用模拟和实验数据进行训练和测试。基于 CNN 的 MPPT 的性能也在准确性和复杂性方面与基于前馈的 MPPT 进行了比较。本文还讨论了基于 CNN 的 MPPT 和硬件资源管理的 FPGA 实现方面，以获得所需的精度。所提出的方法首先在基于 900 瓦 PMSG 的 WECS 仿真模型上实施，并验证了结果。此外，最终在基于 1 HP PMSG 的 WECS 的硬件设置中评估了热情的结果。所提出的方法被证明可以以简单的结构提取最大功率，并对可变风速提供更好的响应。