Hybrid General Regression NN Model for Efficient Operation of Centralized TEG System under Non-Uniform Thermal Gradients

Electronics, Год журнала: 2023, Номер 12(7), С. 1688 - 1688

Опубликована: Апрель 3, 2023

The global energy demand, along with the proportionate share of renewable energy, is increasing rapidly. Renewables such as thermoelectric generators (TEG) systems have lower power ratings but a highly durable and cost-effective technology that can deal waste heat energy. main issues associated TEG are related to their vigorous operating conditions. dynamic temperature gradient across surfaces induces non-uniform distribution (NUTD) significantly impacts available output electrical mismatching current impact may yield by up 70% extractable thermal As solution, hybrid general regression neural network (GRNN) orca predation algorithm (OPA) proposed; backpropagation limitations minimized utilizing stochastic optimization named OPA. conclusions evaluated contrasted improved versions conventional particle swarm (PSO), grey wolf optimizer (GWO), Harris hawk (HHO). A detailed analytical statistical analysis carried out through five distinct case studies, including field data study, NUTD, varying temperature, load studies. Along matrix errors MAE, RMSE, RE, results assessed in terms efficiency, tracking, settling time. show superior performance achieved proposed GRNN-OPA based MPPT 35% faster 90–110% quicker time which, turn, enables 4–8% higher accumulation over longer period operation. low-cost experimental setup devised further validate practicality techniques. From comprehensive analysis, it be safely concluded successfully undertakes drawbacks existing classical methods efficiency.

Язык: Английский

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The Development of a Reduced-Scale Laboratory for the Study of Solutions for Microgrids

Energies, Год журнала: 2024, Номер 17(3), С. 609 - 609

Опубликована: Янв. 26, 2024

The integration of renewable energy sources is crucial for achieving sustainability and environmental preservation. However, their intermittent nature poses challenges to electrical system stability, requiring robust strategies. Microgrids emerge as a flexible solution, but successful deployment requires meticulous planning intelligent operation overcome these challenges. This paper presents the development reduced-scale laboratory dedicated researching both hardware software solutions microgrid management. was designed incorporate key components that are becoming increasingly important in present context, including generation, storage systems, electrolyzers hydrogen production, combined heat power sources. While some equipment consists commercial models, battery bank, converter, supervisory systems were custom-designed meet specific requirements laboratory. has proven itself tool conducting studies on microgrids, effectively incorporating essential components, addressing pertinent issues, allowing several tests converting control algorithms.

Язык: Английский

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Optimal Tuning of PID Controller for Boost Converter using Meta-Heuristic Algorithm for Renewable Energy Applications

Опубликована: Янв. 1, 2023

The Dynamic Levy Flight Chimp optimisation (DLFC) method is used in this study to optimise the Proportional- Integral-Derivative (PID) Controller for Boost converter. As a possible application, tuned PID controller utilised adjust voltages use of renewable power sources. maximum point tracking control approach based on machine learning (ML) anticipate reference solar system irradiance and ambient temperature. uses signal regulate (MPP) voltages. To finetune controller, comparisons are done with grey wolf optimiser (GWO), Harris hawk algorithms (HHO), particle swarm (PSO) algorithms. has fewer oscillations requires little time adapt changing load environment conditions. Additionally, statistical analysis, such as Mean Absolute Error (MAE) Root Square (RMSE) between voltage output voltage, presented. Since DLFC performs better than HHO, GWO, PSO terms RMSE MAE, it may be promising way optimising tuning boost converters photovoltaic (PV) applications.

Язык: Английский

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Comparative Study Of Five Mppt Controls In Two-Stage Three-Phase Grid-Connected Photovoltaic Systems Under Partial Shading Conditions

˜The œScientific Bulletin of Electrical Engineering Faculty, Год журнала: 2023, Номер 23(1), С. 21 - 30

Опубликована: Сен. 1, 2023

Abstract One of the maximum popular renewable electricity assets is photovoltaics. Grid-connected photovoltaic structures are designed to generate as a lot strength possible. Photovoltaic systems have nonlinear traits imposed with aid environmental factors consisting radiation and temperature, making it hard operate at most power factor. It may additionally be tough extract amount due formation nearby maxima because other which include shading or degradation. There several MPPT algorithms approaches that used for this. Publications comparative analyses also been released. However, in these works, comparisons based on simulations literature review. From simplest complex methods, empirical validation remains important. The two widely techniques perturbation, open-loop, incremental conductance algorithms. three challenging ones sliding mode control, backstepping controller, particle swarm optimization. Therefore, five empirically studied this paper. Under test settings, Matlab/Simulink was conduct experimental experiments. findings demonstrate under ordinary operating circumstances, algorithm only one capable finding global MPP influence local shadowing.

Язык: Английский

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Study and Simulation of an Efficient Smart MPPT Algorithm Based on Fuzzy Logic Theory

Опубликована: Май 27, 2024

Язык: Английский

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