Abstract

Wind farm control design is a recently new area of research that has rapidly become a key enabler for the development of large wind farm projects and their safe and efficient connection to the power grid. A comprehensive review of the intense research conducted in this area over the last 10 years is presented. Part I reviews control system concepts and structures and classifies them depending on their main objective (i.e. to maximise power production or to provide grid services. The work and key findings in each paper are discussed in detail with particular emphasis on the turbine side. Additionally, the review contributes to the existing reviews on the area by providing an elegant classification between model testing and control approaches. Areas where significant work is still needed are also discussed. In Part II, a thorough review on aerodynamic wind farm models for control design purposes is provided.

Highlights

  • Wind energy installations continue to increase at an accelerated pace worldwide with larger wind farm projects consisting of hundreds of turbines being constructed both onshore and offshore

  • A good number of feed forward wind farm controllers proposed in the open literature incorporate dynamic axial induction control (DAI), for example [51, 55] where optimal DAI was combined with large Eddy simulation (LES) in a closed-loop approach; the SP-Wind framework is used in both works (Table 2)

  • Intense research has been conducted on the use of model predictive control (MPC) in wind farm controllers that have as an objective the provision of services to the power grid

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Summary

INTRODUCTION

Wind energy installations continue to increase at an accelerated pace worldwide with larger wind farm projects consisting of hundreds of turbines being constructed both onshore and offshore. Modern wind farms are fitted with advanced, state-of-the-art monitoring and control equipment that enable the safe and reliable implementation of all functionalities required to achieve the best possible performance. They are not sufficiently optimised to conciliate a number of conflicting objectives such as continuously maximizing the power production whilst reducing turbine loading and still adapting to the spot price for electricity. In order to achieve the right balance among these objectives, the philosophy behind wind farm control is evolving from the conventional approach of controlling turbines individually to a holistic control approach In this context, wind farm control has become an area of increased interest and large amounts of works have been presented in the open literature aiming to address these pressing challenges. A significant contribution is the provision of a clear classification between model testing and control approaches

Search strategy and structure of the review
UNDERSTANDING A WIND FARM CONTROLLER
Control inputs
Sensor systems
Wind turbine and wind farm flow modelling
WIND FARM CONTROL
Non-optimisation-based verification of models and control approaches
Evaluation model
Non-optimisation-based feedback control
Non-optimisation-based feed forward control
Method
Sequential quadratic programming
Steepest descent
Conjugate gradient
Genetic algorithm
Particle swarm optimisation
Artificial bee colony
Game theory optimisation
Dynamic programming optimisation
Field test of feed forward optimisation works
4.4.10 Works specific to floating wind turbines
Optimisation-based model-based closed-loop
Model predictive control optimisation
Data-driven model-based approaches
Optimisation-based Model-free Closed-loop
PID control with gain scheduling hierarchical structure
PID control with gain scheduling—distributed structure
Optimisation-based model-based open-loop control
Model predictive control
Linear quadratic regulator
Evaluation model SOWFA
Particle Swarm Optimisation
ESTIMATION
DISCUSSION
Full Text
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