Performance Evaluation of Grid-Connected Photovoltaic System Using EHO-Tuned VPTIDF and DQC-Based SPWM

• Published in: Iranian journal of science and technology. Transactions of electrical engineering 2023-03, Vol.47 (1), p.35-60
• Main Authors: Patra, Akshaya Kumar, Rath, Debswarup
• Format: Article
• Language: English
• Subjects: Algorithms; Derivatives; Design optimization; Electrical Engineering; Engineering; Harmonic distortion; Herding; Integrals; Machine learning; Parameters; Performance enhancement; Performance evaluation; Photovoltaics; Pulse duration modulation; Quadratures; Research Paper
• ISSN: 2228-6179; 2364-1827
• DOI: 10.1007/s40998-022-00541-1

This manuscript presents the design of elephant herding optimization algorithm-tuned variable parameter tilt integral derivative with filter (EHO-tuned VPTIDF) controller to reduce the harmonic distortion and improve performance of grid-connected solar micro-inverter. Classical controllers with optimization techniques are seen to improve performance, but it failed to make the system robust. Robustness can be improved by considering control parameter as a function of error. This is utilized in the proposed novel control technique. The nonlinearities present in photovoltaic system framework cause power quality issues and occasional faults. The use of Levenberg–Marquardt algorithm (LMA)-based machine learning technique efficiently detects a fault condition from standard operating conditions efficiently. EHO-tuned VPTIDF along with direct and quadrature control (DQC)-based sinusoidal pulse width modulation (SPWM) technique is implemented in this manuscript. This proposed model is analyzed for reduction in harmonic, improved system performance and fault classification.