Renewable Energy Resources and Technologies
Debswarup Rath; Akshaya Kumar Patra; Sanjeeb Kumar Kar
Abstract
The primary objective of the proposed work is the design of a Hybrid Teaching Learning-based Horse Herd Optimization Algorithm regulated Fractional Order Tilt Derivative Acceleration with Filter (TLBO-HHOA regulated FOTDAF) controller for enhanced performance and enhanced devaluation of harmonic components ...
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The primary objective of the proposed work is the design of a Hybrid Teaching Learning-based Horse Herd Optimization Algorithm regulated Fractional Order Tilt Derivative Acceleration with Filter (TLBO-HHOA regulated FOTDAF) controller for enhanced performance and enhanced devaluation of harmonic components of the grid-connected photovoltaic system. The solar photovoltaic system incorporates constituents such as a photovoltaic array, interleaved fractional order boost converter (IFOBC), Reduced Switch Multilevel Inverter (RSMI), and TLBO-HHOA regulated FOTDAF controller. IFOBC is preferred over boost converter because of its low ripple voltage, faster transient response, high efficiency, low duty cycle, reduced EMC, and improved reliability and stability. In this control strategy, the control logic is formulated by using a Tilt Integral Derivative Controller (TIDC), whose control parameters are considered as a function of the error to improve the robustness. The validation, better performance, and superiority of TLBO-HHOA regulated FOTDAF are established by comparative result analysis using modern controllers. This study implements TLBO-HHOA-regulated FOTDAF and applies Support Vector Pulse Width Modulation (SVPWM) technique. The proposed model managed to achieve improvements in overall system response and reduced harmonic distortions as well as better accuracy, improved stability, improved robustness, and better capabilities to handle system uncertainties.
