This study considers the design and testing of a laboratory scale test rig for wave energy converters (WECs). The main element of the test rig is a double‐sided permanent magnet linear generator (PMLG). In this study, the authors describe the detailed design of the PMLG. The objective of the design is to find the detailed parameters of the PMLG to meet the targeted electromotive force (EMF) voltage with respect to the designed physical constraints. The design procedure is easy to follow and emphasises the practical aspects to construct the PMLG. A spreadsheet table was generated using the procedure to find the design parameters. Therefore, a designer can easily modify the parameters based on the physical constraints and the targeted EMF voltage. In addition to that, the authors explain the procedure to find the rating temperature for the generator. Finally, this PMLG is integrated with other components to form the test rig. The experiment is conducted to show how close the performance of the constructed PMLG is, in term of its EMF voltage and rated thermal, to the designed values. Additional tests were also conducted to test the performance of the test rig using various scenarios similar to ocean wave profiles.

This article presents an intelligent fuzzy logic controller (FLC) for controlling single-body heaving wave energy converter (WEC) or what is widely known as “Point Absorber”. The controller aims at maximizing the energy captured from the sea waves. The power take-off (PTO) limitations are addressed implicitly in the fuzzy inference system (FIS) framework. In order to enhance the WEC power capturing bandwidth and make it less susceptible to wave environment irregularities and the system parametric uncertainties, the controller is built to have a self-configurable capability. This also eliminates the need to repeatedly run in-situ tuning procedure of the fuzzy controller or switch between several controllers based on the operating conditions. The fuzzy membership functions (MFs) are optimally tuned using particle swarm optimization (PSO) algorithm. To alleviate the computational burden associated with performing on-line optimization, the fuzzy controller is tuned at a rate significantly lower than the system sampling time. The suggested PSO-FLC has shown promising results compared with the fixed structure fuzzy logic controller (FS-FLC) and other passive control strategies. Several computer simulations were carried out to evaluate the controller effectiveness by applying different sea-states and analyzing the resultant WEC dynamics.