Abstract
In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system’s stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method.
Highlights
Owing to the increasing energy demand and global effects of the climate change, the use of clean energy sources, such as wind, solar, tidal, and microhydropower, has become important
The sliding mode control (SMC) theory based on the variable structure system has been a good choice especially for the wave energy conversion systems [7,8]
An effective method of DC link voltage control based on a Grey–recursive cerebellum model articulation controller (RCMAC) control system is proposed for wave period variations of the turbine or load changes, controlling the electromagnetic torque of a permanent magnet synchronous generator (PMSG) driven using the variable speed Wells turbine; the effects of different speed variation forms are considered
Summary
Owing to the increasing energy demand and global effects of the climate change, the use of clean energy sources, such as wind, solar, tidal, and microhydropower, has become important. The ocean provides a promising but challenging source for renewable energy development To simplify assumptions such as monochromatic wave environments and linear fluid dynamics, the optimal energy extraction control for the wave energy converter (WEC) has been defined [4]. The grey prediction model is a nonlinear extrapolation forecasting method, developed in the. A traditional CMAC is a perceptual associative memory network with incomplete connections and strong local generalization abilities that uses constant binary or triangular functions. The combination of grey theory and the CMAC algorithm can improve the learning ability, effectiveness, and robustness of predictions. An IPSO algorithm is introduced to determine optimal parameters of recurrent CMAC (RCMAC) controllers for back-to-back converters of the PMSG. The overall simulation model was built for such systems in various cases through the power systems computer aided design (PSCAD)/electromagnetic transient design and control (EMTDC) platform
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