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NGOA Assisted Neural Network MPPT for Grid-Connected PV System with Soft-Clamp X-Gain Boost Converter | ||
| AUT Journal of Electrical Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 14 اردیبهشت 1405 اصل مقاله (2.6 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2026.25230.5872 | ||
| نویسندگان | ||
| Viswaprakash Babu* 1؛ T Sridevi2؛ M. J. Murali3؛ J. Gnanavel4؛ D. Dinesh5؛ M Balasubramanian6 | ||
| 1Assistant Professor, Department of Electrical and Electronics Engineering, Kaveri University, Gouraram, Siddipet, Telangana, India, Pin- 502279 | ||
| 2Assistant Professor, Department of Electrical and Electronics Engineering, Peri Institute of Technology, Chennai, 600048, India. | ||
| 3Assistant Professor, Department of Electrical and Electronics Engineering, Bharath Institute of Science and Technology, Bharath Institute of Higher Education and Research, Chennai 600 073, India. | ||
| 4Assistant Professor, Department of Electrical and Electronics Engineering, Achariya College of Engineering Technology, Pondicherry. | ||
| 5Assistant Professor, Department of Mechatronics Engineering, Chennai Institute of Technology, Chennai 600069 | ||
| 6Assistant Professor, Department of Electrical and Electronics Engineering, Government College of Engineering, Tirunelveli 627007, Tamilnadu, India. | ||
| چکیده | ||
| This work presents an intelligent Photovoltaic (PV) grid integration system employing a Northern Goshawk Optimization Algorithm (NGOA)-based Radial Basis Function Neural Network (RBFNN) for Maximum Power Point Tracking (MPPT) and Soft Clamp X-Gain Boost (SC-XGB) converter for enhanced voltage regulation. The proposed system aims to optimize energy harvesting from PV source and ensure stable power delivery to a three-phase grid. The RBFNN is trained offline using comprehensive PV datasets and directly predicts the optimal duty cycle from measurable PV inputs during real-time operation, eliminating the need for auxiliary MPP pre-estimation algorithms, while NGOA enhances RBFNN’s learning capability by fine-tuning its weights and biases for rapid and accurate MPPT performance even under varying irradiance and temperature conditions. PV output is connected to SC-XGB, which efficiently raises the Direct Current (DC) voltage and is thus controlled by Pulse Width Modulation (PWM) signals which are generated according to MPPT output. Regulated DC output is then transformed into a three-phase Alternating Current (AC) by a Voltage Source Inverter (VSI), the output of which is taken through an LC filter to reduce harmonics before the power is fed into grid. Simulation is done in MATLAB showing the capacity of NGOA-RBFNN to track Maximum Power Point (MPP) at very high speed and accuracy. The system achieves superior voltage regulation of 95.24% efficiency, reduced Total Harmonic Distortion (THD) and enhanced dynamic performance over traditional MPPT control methods. | ||
| کلیدواژهها | ||
| PV system؛ soft clamp x-gain boost converter؛ NGOA based RBFNN MPPT؛ three-phase VSI؛ grid system | ||
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آمار تعداد مشاهده مقاله: 19 تعداد دریافت فایل اصل مقاله: 3 |
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