Collaborative strategy for model-free control of arrays of wave energy converters: A genetic algorithm approach

In the field of renewable energy, one of the most promising branches is wave energy conversion. Systems used to extract wave energy are called WECs (Wave Energy Converters). In order to extensively exploit the untapped potential wave energy has, several devices should be adopted together, constituting in this way an array configuration. The development of suitable control strategies for WECs is among the open challenges in wave energy field. Classical approaches rely on models of the considered WECs to compute the optimal control action. In the proposed work, however, a different model-free approach is pursued. Here, the control action is computed only on the basis of past applied control parameters and of absorbed power measurements among the elements of the array. The strategy developed in this work is based on the analogy between devices of the array and the concept of generation in genetic optimization algorithm. Given a sea-state condition, each WEC in the array constitutes an individual of the generation. On each one, a combination of control parameters is applied and the average absorbed power thus obtained is measured. With the considered analogy, this set of control parameters constitutes the chromosome, while the power is the measure of the fitness function to be, in this case, maximized. In this way, merging the data coming from different WECs of the array helps the control strategy to converge in a collaborative way to optimal parameters faster. As a case study, an array of point absorbers deployed in the Mediterranean Sea has been considered, together with its typical values of significant height, energetic period and annual occurrences.