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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.08416 |
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| _version_ | 1866911309592788992 |
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| author | Andriniriniaimalaza, Fanambinantsoa Philibert Murad, Nour Telesphore, Randriamaitso Habachi, Bilal Nirilalaina, Randriatefison Ruffin, Manasina Bernard, Andrianirina Charles Blaise, Ravelo |
| author_facet | Andriniriniaimalaza, Fanambinantsoa Philibert Murad, Nour Telesphore, Randriamaitso Habachi, Bilal Nirilalaina, Randriatefison Ruffin, Manasina Bernard, Andrianirina Charles Blaise, Ravelo |
| contents | This article investigates on the improvement and stabilization of alternating current (AC) and direct current (DC) output voltages in a Permanent Magnet Synchronous Generator (PMSG) driven by a vertical-axis tidal turbine using advanced control strategies. The research integrates artificial intelligence (AI)-based techniques to enhance voltage stability and efficiency. Initially, the Maximum Power Point Tracking (MPPT) approach based on Tip Speed Ratio (TSR) and Artificial Neural Network (ANN) Fuzzy logic controllers is explored. To further optimize the performance, Particle Swarm Optimization (PSO) and a hybrid ANN-PSO methodology are implemented. These strategies aim to refine the reference rotational speed of the turbine while minimizing deviations from optimal power extraction conditions. The simulation results of a tidal turbine operating at a water flow velocity of 1.5 m/s demonstrate that the PSO-based control approach significantly enhances the voltage stability compared to conventional MPPT-TSR and ANN-Fuzzy controllers. The hybrid ANN-PSO technique improves the voltage regulation by dynamically adapting to system variations and providing real-time reference speed adjustments. This research highlights the AI-based hybrid optimization benefit to stabilize the output voltage of tidal energy systems, thereby increasing reliability and efficiency in renewable energy applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08416 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improvement and Stabilization of Output Voltages in a Vertical Tidal Turbine Using Intelligent Control Strategies Andriniriniaimalaza, Fanambinantsoa Philibert Murad, Nour Telesphore, Randriamaitso Habachi, Bilal Nirilalaina, Randriatefison Ruffin, Manasina Bernard, Andrianirina Charles Blaise, Ravelo Networking and Internet Architecture Signal Processing This article investigates on the improvement and stabilization of alternating current (AC) and direct current (DC) output voltages in a Permanent Magnet Synchronous Generator (PMSG) driven by a vertical-axis tidal turbine using advanced control strategies. The research integrates artificial intelligence (AI)-based techniques to enhance voltage stability and efficiency. Initially, the Maximum Power Point Tracking (MPPT) approach based on Tip Speed Ratio (TSR) and Artificial Neural Network (ANN) Fuzzy logic controllers is explored. To further optimize the performance, Particle Swarm Optimization (PSO) and a hybrid ANN-PSO methodology are implemented. These strategies aim to refine the reference rotational speed of the turbine while minimizing deviations from optimal power extraction conditions. The simulation results of a tidal turbine operating at a water flow velocity of 1.5 m/s demonstrate that the PSO-based control approach significantly enhances the voltage stability compared to conventional MPPT-TSR and ANN-Fuzzy controllers. The hybrid ANN-PSO technique improves the voltage regulation by dynamically adapting to system variations and providing real-time reference speed adjustments. This research highlights the AI-based hybrid optimization benefit to stabilize the output voltage of tidal energy systems, thereby increasing reliability and efficiency in renewable energy applications. |
| title | Improvement and Stabilization of Output Voltages in a Vertical Tidal Turbine Using Intelligent Control Strategies |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2512.08416 |