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.
Renewable Energy Resources and Technologies
Satyaprasad Mohapatra; Akshaya Kumar Patra; Debswarup Rath
Abstract
The design of a Spotted Hyena Optimization Algorithm-Variable Parameter Tilt Integral Derivative with Filter (SHO-VPTIDF) controller for improved performance and enhanced devaluation of harmonic components of grid-connected photovoltaic systems is the main objective of the suggested manuscript. The SHO-VPTIDF ...
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The design of a Spotted Hyena Optimization Algorithm-Variable Parameter Tilt Integral Derivative with Filter (SHO-VPTIDF) controller for improved performance and enhanced devaluation of harmonic components of grid-connected photovoltaic systems is the main objective of the suggested manuscript. The SHO-VPTIDF controller is proposed by reformulating Tilt Integral Derivative Controller with Filter (TIDCF). The TIDCF is characterized by longer simulation time, lower robustness, longer settling time, attenuated ability for noise rejection, and limited use. This research gap is addressed by replacing the constant gains of TIDCF by variable parameter tilt integral derivative with filter. The VPTIDF replaces the constant gains of TIDCF with error varying control parameters to improve the robustness of the system. The photovoltaic system with nonlinearities causes power quality issues and occasional faults, which can be detected by using Levenberg-Marquardt Algorithm (LMA) based machine learning technique. The novelties of the proposed manuscript including improved stability, better robustness, upgraded accuracy, better harmonic mitigation ability, and improved ability to handle uncertainties are verified in a Matlab simulink environment. In this manuscript, the SHO-VPTIDF and the Direct and Quadrature Control based Sinusoidal Pulse Width Modulation (DQCSPWM) method are employed for fault classification, harmonic diminishing, stability enhancement, better system performance, better accuracy, improved robustness, and better capabilities to handle system uncertainties.
