Abstract
This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO)-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration.
Highlights
This paper presents the application of intelligent optimization techniques in wind turbine rotor speed setpoint control algorithms
Wind turbine control algorithms adjust the setpoint in order to capture as much mean power as possible while preventing from reaching the tower resonance speed
Constant, which maintains the rotor speed setpoint in a nominal value; Conventional, which increases the rotor speed setpoint with the pitch angle. This algorithm has been modelled with α = 1, according to Equations (20) and (21); The proposed Reinforcement Learning (RL), which takes into account the rotor speed and the acceleration to change the rotor speed setpoint, according to Equations (11), (12) and (13); The proposed Particle Swarm Optimization (PSO), which increases the rotor speed setpoint with the pitch angle with an exponential α value, according to Equations (20) and (21)
Summary
This paper presents the application of intelligent optimization techniques in wind turbine rotor speed setpoint control algorithms. The RL-MDP framework is applied to improve the rotor speed setpoint algorithm. In [6,7,8,9] for example, an RL scheme is proposed in a multi body linked system control In this kind of problems the states are quantified in many level or possible values. PSO is a bio-inspired computational technique based on the idea of natural swarm learning mechanisms, where living organisms remember successful positions of the swarm and use them to improve future rewards This algorithm has been applied in Wind Turbine design with success [10].
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