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Integrated neural network-based MPPT and ant colony optimization-tuned PI bidirectional charger-controller for PV-powered motor-pump system |
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| รหัสดีโอไอ | |
| Creator | Rati Wongsathan |
| Title | Integrated neural network-based MPPT and ant colony optimization-tuned PI bidirectional charger-controller for PV-powered motor-pump system |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2567 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 51 |
| Journal No. | 5 |
| Page no. | 605-617 |
| Keyword | ACO, Bidirectional charger, MPPT, Neural networks, Photovoltaic pumping system |
| URL Website | https://ph01.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| Abstract | This study presents the design and implementation of an efficient off-grid photovoltaic (PV)-powered motor-pump system utilizing a two-stage power converter. The system integrates a neural network-based maximum power point tracking (MPPT-NN) algorithm with a proportional integral (PI) controller and an additional bidirectional PI charger. Controller gains are optimized using ant colony optimization (ACO) to achieve optimal performance. The proposed MPPT-NN-PI/ACO controller enhances control responses and improves energy utilization efficiency by 17% compared to traditional PI controller. Performance comparisons of MPPT techniques demonstrates that the proposed controller outperforms several existing methods, including commercial on-off controllers, the modified Perturb & Observe algorithm, and neural network-based controllers, by approximately 4%–20%. It shows a slightly different performance of about 1%–6% compared to advanced adaptive controllers, including fuzzy logic and neuro-fuzzy controllers. For bidirectional charger performance, the DC bus voltage connecting the boost converter and bidirectional converter remains stable with small ripples and is well-aligned with the reference voltage, ensuring uninterrupted operation under varying weather conditions. The bidirectional charge management effectively maintains battery state-of-charge (SOC), showing a decline during periods of insufficient PV energy and achieving full charging during periods of excess PV energy. System performance is validated through both simulation and laboratory-scale prototyping, ensuring robust operation. |
