State-of-the-Art of the most commonly adopted wave energy conversion systems

Mahdy, Ahmed; Hasanien, Hany M.; Aleem, Shady H. E. Abdel; Al‐Dhaifallah, Mujahed; Zobaa, Ahmed F.; Ali, Ziad M.

doi:10.1016/j.asej.2023.102322

Cited by 5 publications

(3 citation statements)

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“…Each of these controllers has five control parameters that need to be tuned. This leads to a total of thirty parameters that can be represented by an array r = [r [1], r [2], m [3], r [4], r[5], . .…”

Section: The Back-to-back Converter Configurationmentioning

confidence: 99%

“…. ., r [30]], where r [1], r [2], r [3], r [4], and r [5] represent the five control gains of FOPID-1, where r [4] = K d , and r[5] = β. Similarly, the FOPID-2 gains are represented by r [6], r [7], r [8], r [9], and r [10].…”

Section: The Back-to-back Converter Configurationmentioning

confidence: 99%

“…The benefits of using wave energy include avoiding the need for expensive real estate, and a short distance between wave energy converters and consumption because a large portion of the world lives on the ocean's shores, resulting in lower energy losses; lower investment in transmission lines; and higher power density compared with other renewable resources, such as wind and solar energies. Therefore, a large interest has developed toward convert wave energy into electricity, which led to the invention of numerous wave energy converters, like the Oscillating Water Column, Archimedes Wave Swing (AWS), Wave Dragon, Pelamis Wave Power, Aquabouy, and the Oyster [2]. The AWS converter was chosen by the authors for use in this study because it is one of the most regularly used converters and is employed by a well-known company (AWS Ocean Energy) [3,4].…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

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Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

Ali,

Ahmed,

Hasanien

et al. 2023

Fractal Fract

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In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

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Section: The Back-to-back Converter Configurationmentioning

confidence: 99%