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Wave energy is one of the most promising technologies for the future of renewable energy. Its steady, even if not dashing, development, both in theoretical and applied solutions, will bring the field, in the mid-term, to the point of being a viable, economical and sustainable technology, competitive when compared to more mature ones. This paper deals with the ISWEC (Inertial Sea Wave Energy Converter) power take off (PTO) technology and control strategies. In particular it focuses on the control strategy used for harnessing energy during its first deployment, and presents the development of a new controller designed using optimal control theory. The approach to the problem of this work is not to seek for the theoretical optimal control strategy. In fact, during the test period of the ISWEC into the sea, limitations of a model-optimized control strategy came out, due to well known shortcomings of hydrodynamics modeling theory for wave energy devices, and the idea of an easy-to-tune in the field controller arose. This work points out in the direction of developing a practical and effective strategy for industrial application, with the main objective of increasing the power production while simplifying the control law by which the device harnesses energy. [ABSTRACT FROM AUTHOR]

Oscillating water columns (OWCs) are widely regarded as the simplest and most reliable type of wave energy converter. An economically viable OWC wave power plant requires maximizing the performance of the power take-off system, composed of an air turbine coupled to an electrical generator. Besides, the OWC power plant's inherent nonlinearities and modeling uncertainties must be dealt with. One solution is the use of fuzzy controllers since they capture both nonlinearities and uncertainties of the system, which can largely contribute to improving the overall efficiency. This paper shows how an inverse fuzzy model optimized by a genetic algorithm (GA) can improve the control of an OWC and, consequently, its performance. As a case study, the Mutriku power plant with a biradial turbine was addressed, considering 11 different sea states of the characteristic wave climate. Two optimization objectives were considered: maximizing turbine and generator output average powers. Compared with a well-known baseline control strategy, the GA-fuzzy control did not significantly contribute to increasing average turbine power. However, when maximizing generator yearly-accumulated power, there were improvements of over 9%. Ultimately, these research findings may play an essential role in control strategies for wave power plants. • An inverse model controller was proposed for wave energy control. • The inverse model was obtained with fuzzy modeling and optimized by genetic algorithms. • Focus on a real case study: An oscillating water column at the Mutriku power plant. • Comparison of the proposed algorithm with a well-known baseline strategy. • The proposed strategy showed yearly improvements of over 9% on average generator power. [ABSTRACT FROM AUTHOR]

Abstract: This work addresses an off-shore oscillating water column for producing electricity from sea waves. It describes the modelling of this device and the study of control techniques that could improve energy extraction. Optimisation techniques applied improved the device performance for a wide number of sea states. A control strategy was developed with the objective of improving the quality of the energy absorbed by the device. This proved to be effective. In a later stage of this work, some experiments considering a variable pitch Wells turbine were performed with the objective of applying phase and amplitude control: it was proved that it is possible to obtain a resonant response to a sinusoidal wave with a frequency different from the device’s natural frequency. [Copyright &y& Elsevier]

Abstract: The Archimedes Wave Swing (AWS) is a a fully-submerged Wave Energy Converter (WEC), that is to say, a device that converts the energy of sea waves into electricity. A first prototype of the AWS has already been built and tested. In this paper, neural network (NN) models for this AWS prototype are developed. NNs are then used together with proven control strategies (phase and amplitude control, internal model control and switching control) to maximise energy production. Simulations show an yearly average electricity production increase of 160% over the performance of the original AWS controller. [Copyright &y& Elsevier]

Abstract: This paper addresses the Archimedes Wave Swing (an offshore wave energy converter, which produces electricity from sea waves). It compares the performances of latching control (a discrete, highly non-linear, intrinsically sub-optimum control strategy), of reactive control, of phase and amplitude control (two optimum control strategies that involve non-causal transfer functions, which have to be implemented with approximations, thus rending the control sub-optimum), and of feedback linearisation control (a non-linear control strategy). From extensive simulations it is concluded that the latter performs clearly better irrespective of the sea state, and leads to a significant increase of absorbed wave power. [Copyright &y& Elsevier]

This paper aims to contribute to the improvement of the energy extracted from Wave Energy Converters, arrayed in a wave farm, through the implementation of several actions, namely wave extrapolation using a dynamic neural network, wave type identification resorting to a machine learning system, and coordination of the interoperability, communication, and control of all the devices belonging to the wave farm via a Multi-Agent System. The Multi-Agent System was initially developed and subsequently tested through simulations. Simulations used the Inertial Sea Wave Energy Converter, which extracts power from sea waves, using gyroscopic motion, to generate electricity. This device can be originally controlled by proportional-derivative controllers with parameters that change with the sea state. With this Multi-Agent System, ocean waves and corresponding sea states are identified using a machine learning wave classifier method. Information is passed on so that all devices can use optimal control parameters. Therefore, energy extraction by the wave farm is enhanced due to the combination of the Multi-Agent System with automatic learning methods, while its architecture is resilient to communication problems between devices. Simulation results for a case study showed an increase in the average power absorbed, proving the effectiveness of combining a Multi-Agent System with automatic learning methods. Conclusions demonstrated that the proposed system architecture is a viable alternative to the original wave forecasting model. Additionally, this Multi-Agent System can be adapted to wave farms with other Wave Energy Converters and other types of control. [ABSTRACT FROM AUTHOR]

Ocean waves hold a promising potential as a sustainable source of clean energy. However, in order to make wave energy converters financially viable, particularly in the case of the oscillating water column (OWC), it is crucial to optimize the power take-off system to maximize performance. Most control strategies focus solely on turbine performance, often at the expense of overlooking the overall wave-to-wire system performance. Therefore, a model predictive control (MPC) strategy using short-term air pressure prediction was proposed to enhance electrical energy delivered to the grid. The Mutriku wave power plant (Spain) was case-studied for evaluating the control approaches, which focused on maximizing average power (turbine or generator) from an OWC equipped with the innovative biradial turbine. In comparison to the reference baseline strategy, the implementation of MPC (with a 16-second forecast) resulted in a minor 2%-increase in the average turbine power. However, when it comes to the generator power accumulated over a year, the MPC strategy produced an improvement of more than 8%. Since generator power is the ultimate product, this improvement is significant. Nonetheless, the MPC strategy exhibited a non-smooth control response compared to other strategies. Improving control action quality should be a primary focus of future research. • A model predictive control was proposed for an oscillating-water-column power plant. • Short-term air pressure forecasting was performed using an autoregressive model. • The proposed algorithm was applied to the Mutriku wave power plant. • Improvements in the annual average generator power output of over 8% were observed. [ABSTRACT FROM AUTHOR]