Experimental Optimization Environment for Developing an Intracycle Pitch Control in Cross Flow Turbines

Abstract

Cross-flow tidal turbines also known as vertical-axis turbines have not reached the efficiency as it is usual found for horizontal-axis turbines. This can be mainly accounted to the intensive development effort dedicated to the latter in the past 30 years in the area of wind energy. Less attention has been given to cross-flow turbines as they present mechanical cyclic loads that detriment their durability. Nevertheless, there is great potential for their employment in tidal energy exploitation as they are intrinsically independent from the flow direction and have a higher power output per area in farm installation. For this reason the research for cross-flow turbines have regain impulse.
 Among various approaches to improve their hydrodynamic characteristics found in the literature, the intracycle pitch control is one of the most promising one. It consists in actively pitching each blade as function of the azimuth angle.
 The present paper will introduce the overall experimental environment and methods for developing an optimal intracycle control for cross-flow turbines in tidal power generation. This is the main component of project OPTIDE at Otto-von-Guericke-University Magdeburg, Germany in cooperation with the LEGI at University Grenoble-Alpes, France and the University of Applied Sciences Magdeburg-Stendal.
 The experimental system consists of a turbine flume model equipped with a speed controlled generator. The three bladed turbine model has independent controlled pitch actuators embedded in each blade and a force sensing system in one of the blade holding arms. Local high-dynamic systems allow to control the speed of the generator and the pitch position as a function of the rotational angle of the runner i.e. pitch trajectory.
 A superimposed governing system which automates the experiment has been implemented. This allows to perform an optimization process with the experiment in the loop. Evolutionary algorithms are employed to solve the two objectives of the optimization: (1) Maximizing the efficiency by (2) minimizing the alternating structural loads on the runner. These kind of problems are usually solved with numerical simulations using finite element method (FEM) and computational fluid dynamics (CFD). However, this commonly comes with very high computational efforts and uncertainty. For this reason in our approach simulations are replaced by an experimental optimization technique.
 The optimization cycle implemented in the superimposed governing system chooses a set of individuals (pitch trajectories) for each generation on base of the genetic algorithm. Each pitch trajectory is performed sequentially while power output and loads are measured. This allows to evaluate each individual in about one minute. The best individuals are then the base for the offspring of the new generation. Cross-over from best performing parents and subsequent mutation ensure both, convergence and a thorough exploration of the entire design space.
 It is expected that this method will fast and reliably find the optimal pitch trajectories under various conditions. Selected cases will be analysed in detail by means of particle-image velocimetry (PIV) with synchronized force and torque measurements to research the underlying hydrodynamic mechanisms and the effects of the flow control performed by the pitching motion.