项目介绍

borgestassio/Wind-Turbine-Control 是一个托管在 GitHub 上的开源项目,主要使用 Fortran 编写,目前获得 57 个 Star、20 次 Fork。

仓库全名
borgestassio/Wind-Turbine-Control
Star 数
57
Fork 数
20
未关闭 Issue
1

README 摘要

Wind Turbine Controllers with NREL Fast and Matlab/Simulink This repository has a few controllers for the 5-MW Reference Wind Turbine for Offshore System Development. The controllers are implemented through Matlab / Simulink. We start with a simple 1-DOF controller, where we consider only the rotor. The complexity of the system is increased up to 11 DOF, where we use MBC techniques to extract important features,…

完整内容见 GitHub 原始 README 或本页 。

常见问题

Wind-Turbine-Control 用什么语言开发?

主要使用 Fortran 编写,目前有 57 个 Star。

如何获取 Wind-Turbine-Control 的源码?

可以直接克隆仓库:git clone https://github.com/borgestassio/Wind-Turbine-Control.git,默认分支为 master;安装与运行步骤见仓库 README。

Wind-Turbine-Control 最近还在维护吗?

仓库最近一次更新为 2022年12月2日,当前有 1 个未关闭 Issue;Star 数为 57,可作为社区活跃度的参考。

关于

GitHub 原始描述(英文)

Controllers designed to the 5MW NREL wind turbine using Simulink and Fast V8

描述取自 GitHub 官方 API,保持仓库作者书写的原文。

语言占比

Batchfile
F*
Fortran
LabVIEW
M
MATLAB
Makefile

贡献者

README

This repository has a few controllers for the 5-MW Reference Wind Turbine for Offshore System Development. The controllers are implemented through Matlab / Simulink.

We start with a simple 1-DOF controller, where we consider only the rotor. The complexity of the system is increased up to 11 DOF, where we use MBC techniques to extract important features, don’t worry, the details will be on the appropriate folder. On the control side, we start with a simple feedback controller until we reach an Artificial Neural Network based controller. In the process, we’ll see state observer (Kalman Filter), disturbance rejection and gain scheduling, all in due time.

Ok, let’s cut to the chase, you’re here to see some pretty graphs and poor explanation.

These are the topics covered in this tutorial:

  1. Set-up Environment
  2. Linearization
  3. State Space Feedback
  4. State Space Observer
  5. Artificial Neural Network based Gain Scheduling
  6. Comparing Controllers

This is the “final” result, where we compare a baseline PID controller with a Radial Basis Function Neural Network based controller:

Other info and disclaimers:

Controllers designed to the 5MW NREL wind turbine using the MATLAB Simulink and FAST V8. Note: OpenFAST is already available and it’s open-source, the reason I’m still using FAST V8 is that my MATLAB is an older version not supported on the most recent FAST distribution.

The FAST software is distributed by the National Renewable Energy Laboratory

Matlab and Simulink are property of Mathworks;

The Matlab version used in this project is the Matlab R20017b

The FAST version used was FAST V8(v8.16.00a-bjj)

For Linearization, I used FAST V7 mainly because the output file (.lin) is easier to extract the desired information.

Although this is an individual repository maintained by the only author, some of the files contained here are modified by the members of the Laboratoire de Mécanique de Normandie (LMN) from the INSA Rouen freely made available as example for anyone who is interested in this topic.

Useful links:

The parameters used for the turbine are given by NREL in here: Definition of a 5-MW Reference Wind Turbine for Offshore System Development

Some controllers are adapted from Advanced Control Design for Wind Turbines; Part I: Control Design, Implementation, and Initial Tests

Fell free to contact me anytime for help, suggestions, critics and insults as well :)

These files are provided without warranty. Use them at your own risk.

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