Distributed Model Predictive Active Power Control of Wind Farms

Haoran Zhao, Qiuwei Wu

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

This chapter explores a distributed model predictive control (D‐MPC) approach for optising active power of a wind farm. The control scheme is based on the fast gradient method via dual decomposition. The developed D‐MPC approach is implemented using the clustering‐based piecewise affine (PWA) wind turbine model. Wind farm control can be implemented either by the utilization of a separate energy storage device or through derated operation of the wind turbines. Model predictive control (MPC) is an effective scheme for multi‐objective wind farm control. The chapter describes the key properties required to apply the fast dual gradient method. Due to their flexible charging and discharging characteristics, energy storage system (ESSs) are considered effective tools to enhance the flexibility and controllability of wind farms. The chapter presents a case study of a wind farm comprising ten 5‐MW wind turbines that is used as the test system.

Original languageEnglish
Title of host publicationModeling and Modern Control of Wind Power
PublisherWiley-IEEE press
Publication date2018
Pages151-173
Chapter8
ISBN (Print)9781119236382
DOIs
Publication statusPublished - 2018

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