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Ocean wave energy is one of the most abundant energy sources in the world. There is a wide variety of wave energy conversion systems that have been designed and developed, resulting from the different ways of ocean wave energy absorption and also depending on the location characteristics. This paper reviews and analyses the concepts of hydraulic power take-off (PTO) system used in various types of wave energy conversion systems so that it can be a useful reference to researchers, engineers and inventors. This paper also reviews the control mechanisms of the hydraulic PTO system in order to optimise the energy harvested from the ocean waves. Finally, the benefits and challenges of the hydraulic PTO system are discussed in this paper.

This study is concerned with the application of two major kinds of optimisation algorithms on the hydraulic power take-off (HPTO) model for the wave energy converters (WECs). In general, the HPTO unit’s performance depends on the configuration of its parameters such as hydraulic cylinder size, hydraulic accumulator capacity and pre-charge pressure and hydraulic motor displacement. Conventionally, the optimal parameters of the HPTO unit need to be manually estimated by repeating setting the parameters’ values during the simulation process. However, such an estimation method can easily be exposed to human error and would subsequently result in an inaccurate selection of HPTO parameters for WECs. Therefore, an effective approach of using the non-evolutionary Non-Linear Programming by Quadratic Lagrangian (NLPQL) and evolutionary Genetic Algorithm (GA) algorithms for determining the optimal HPTO parameters was explored in the present study. A simulation–optimisation of the HPTO model was performed in the MATLAB/Simulink environment. A complete WECs model was built using Simscape Fluids toolbox in MATLAB/Simulink. The actual specifications of hydraulic components from the manufacturer were used during the simulation study. The simulation results showed that the performance of optimal HPTO units optimised by NLPQL and GA approaches have significantly improved up to 96% and 97%, respectively, in regular wave conditions. The results also showed that both optimal HPTO units were capable of generating electricity up to 62% and 77%, respectively, of their rated capacity in irregular wave circumstances.

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This paper presents an evaluation of an optimal DC bus voltage regulation strategy for grid-connected photovoltaic (PV) system with battery energy storage (BES). The BES is connected to the PV system DC bus using a DC/DC buck-boost converter. The converter facilitates the BES power charge/discharge to compensate for the DC bus voltage deviation during severe disturbance conditions. In this way, the regulation of DC bus voltage of the PV/BES system can be enhanced as compared to the conventional regulation that is solely based on the voltage-sourced converter (VSC). For the grid side VSC (G-VSC), two control methods, namely, the voltage-mode and current-mode controls, are applied. For control parameter optimization, the simplex optimization technique is applied for the G-VSC voltage- and current-mode controls, including the BES DC/DC buck-boost converter controllers. A new set of optimized parameters are obtained for each of the power converters for comparison purposes. The PSCAD/EMTDC-based simulation case studies are presented to evaluate the performance of the proposed optimized control scheme in comparison to the conventional methods.

This paper presents accurate control parameters estimation of the hydraulic Power Take-Off (PTO) model for the wave energy conversion system to maximise energy production. In general, the performance of the hydraulic PTO system depends on the parameters setting of hydraulic PTO system components such as hydraulic motor displacement setting, pre-charge of the hydraulic accumulator, and et cetera. Conventionally, it requires to manually obtain the optimal parameters of a hydraulic PTO system by repeating the simulation process. However, this estimation method exposed to human error and would easily be resulting in a non-optimal selection of hydraulic PTO parameters for the wave energy conversion system. Therefore, an easy and accurate approach of using the GA optimisation method for determining hydraulic PTO parameters was introduced in the present study. This approach is simple and more accurate compared to the conventional optimisation method. The hydraulic PTO model was developed in SIEMENS/Amesim environment using available components in the library. The specifications of the actual hydraulic PTO system components from the manufacturer were used during the simulation set-up. The complete hydraulic PTO system was optimised using a special genetic algorithm (GA) optimisation tools in the SIEMENS/Amesim software. The simulation results showed that GA was effective to determine the optimal configuration parameters of hydraulic PTO system. From the results, the optimal configuration parameters of hydraulic PTO system were successfully reduced about 38%. Consequently, the maximum force applied to the WEC devices was reduced up to 34%. This force reduction is important since it will enable the WECS to be operated during a smaller wave condition.