A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition

Sustainable Energy Technologies and Assessments, Год журнала: 2021, Номер 47, С. 101367 - 101367

Опубликована: Июнь 11, 2021

Язык: Английский

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A Novel Improved Grey Wolf Algorithm Based Global Maximum Power Point Tracker Method Considering Partial Shading

IEEE Access, Год журнала: 2024, Номер 12, С. 6148 - 6159

Опубликована: Янв. 1, 2024

Considering photovoltaic systems' sustainability and environmental friendliness, they have been widely used due to ease of installation as their cost reduces efficiency is improved. Analytical maximum power point tracking methods for system work effectively under uniform weather conditions. However, may fall into local points partial shading Although numerous meta-heuristic can overcome these challenges, still be improved regarding the convergence time global point. This paper suggests an grey wolf optimization method track points, enhancing process various The proposed has verified experimentally dynamic real conditions, consisting non-uniform provides better speed up 82% 1.4% compared basic optimization. According daily performance evaluation, IGWO runtime by 76% improves energy harvesting 2.3% obtained results validate superiority conditions in terms accuracy.

Язык: Английский

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Intelligent adaptive PSO and linear active disturbance rejection control: A novel reinitialization strategy for partially shaded photovoltaic-powered battery charging

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110037 - 110037

Опубликована: Янв. 4, 2025

Язык: Английский

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Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Energy Conversion and Management, Год журнала: 2022, Номер 258, С. 115564 - 115564

Опубликована: Апрель 1, 2022

Large scale integration of renewable energy system with classical electrical power generation requires a precise balance to maintain and optimize the supply–demand limitations in grids operations. For this purpose, accurate forecasting is needed from wind conversion systems (WECS) solar plants (SPPs). This daunting task has limits long-short term due highly random nature environmental conditions. paper offers hybrid variational decomposition model (HVDM) as revolutionary composite deep learning-based evolutionary technique for production microgrid farms. The objective obtain short-term five steps development. An improvised dynamic group-based cooperative search (IDGC) mechanism IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) proposed enhanced forecasting. meteorological data time series utilized. SCADA provide values system. improvisation been made metaheuristic algorithm an training designed short (STWF) problem. results are compared two different topologies three heuristic algorithms: particle swarm intelligence (PSO), IDGC, group cooperation optimization (DGCO). 24 h ahead studied experimental simulations. analysis using seasonal behavior year-round performance analysis. prediction accuracy achieved by shows greater results. comparison statistically existing works literature showing effective at lower computational burden. Three graphically statistically.

Язык: Английский

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Marine Predator Algorithm (MPA)-Based MPPT Technique for Solar PV Systems under Partial Shading Conditions

Energies, Год журнала: 2022, Номер 15(17), С. 6172 - 6172

Опубликована: Авг. 25, 2022

To satisfy global electrical energy requirements, photovoltaic (PV) is a promising source that can be obtained from the available alternative sources, but partial shading conditions (PSCs), which trap local maxima power point instead of peak (GMPP), are major problem needs to addressed in PV systems achieve uninterruptable continuous supply desired by consumers. avoid these difficulties, marine predator algorithm (MPA), bio-inspired meta-heuristic algorithm, applied this work. The work validated and executed using MATLAB/Simulink software along with hardware experimentation. superiority proposed MPA method four different PSCs on system, their characteristics compared those existing algorithms. PSC outcomes terms GMPP case 1 at 0.07 s 995.0 Watts; 2 0.06 674.5 3 0.04 654.1 4 364.2 Watts. software- hardware-validated results show its supremacy convergence time, efficiency, accuracy, extracted power.

Язык: Английский

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Towards green energy for sustainable development: Machine learning based MPPT approach for thermoelectric generator

Journal of Cleaner Production, Год журнала: 2022, Номер 351, С. 131591 - 131591

Опубликована: Март 30, 2022

Язык: Английский

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Data-driven green energy extraction: Machine learning-based MPPT control with efficient fault detection method for the hybrid PV-TEG system

Energy Reports, Год журнала: 2023, Номер 9, С. 3604 - 3623

Опубликована: Фев. 27, 2023

The hybrid photovoltaic-thermoelectric generation system (PVTEG) gives two-fold benefits; firstly, it efficiently utilizes the available solar energy as converts both irradiance and thermal into electricity, secondly, enhances PV efficiency by reducing module surface temperature. However, of PVTEG is usually low because TEG are not highly efficient devices. Under changing environmental conditions, a well-designed maximum power point tracking (MPPT) controller can enhance 10%–15%. Therefore, this research explores evolutionary Neural Network based MPPT control technique for systems. snake optimizer optimally tuned weight biases Multilayer perceptron neural network (MLPNN), which provides fast real-time global maxima (GM) tracking. Furthermore, to robustness control, PID gains using SO algorithm. Use (SOPID) with (SOANN) results in stable, accurate under varying conditions. effectiveness proposed SOANN validated comparing PSOANN, RSANN GWOANN. very negligible oscillations. extract optimal an average 99.928% time less than 5ms. intelligent data driven fault detection algorithm proposed, do require any temperature or sensors cost system. Comparative, simulation, quantitative, statistical second superior performance terms efficiency, time, stability capability various practical condition.

Язык: Английский

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Novel initialization strategy: Optimizing conventional algorithms for global maximum power point tracking

Results in Engineering, Год журнала: 2024, Номер 22, С. 102067 - 102067

Опубликована: Март 31, 2024

In this research, a novel initialization strategy for conventional MPPT algorithms is proposed to define the best position tracking process start over P–V curve. Consequently, global maximum power point (GMPP) becomes nearest or first among existing multiple MPPs under partial shading condition (PSC). addition, step size of applied algorithm minimized based on its proximity actual GMPP. Therefore, speed improved, and loss can be reduced by approach. The major advantages approach are eliminating need modify original algorithm, hybridizing with other algorithms, employing any complex procedures, as in metaheuristic optimization algorithms. Hence, it overcoming main drawbacks MPPT. work, simplest technique, which perturbation observation (P&O) show enhancement performance without introduce processes. MATLAB/Simulink simulation model hardware implementation digital signal processing (DSP) controller TMS320F28335 two distinct methodologies used validate outperformance when applying technique PSC various weather fluctuations. outcomes that was successful extracting peak while also improving time response, accuracy, generating oscillations.

Язык: Английский

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High-efficiency hybrid PV-TEG system with intelligent control to harvest maximum energy under various non-static operating conditions

Journal of Cleaner Production, Год журнала: 2021, Номер 320, С. 128643 - 128643

Опубликована: Авг. 13, 2021

Язык: Английский

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Maximum energy harvesting of centralized thermoelectric power generation systems with non-uniform temperature distribution based on novel equilibrium optimizer

Energy Conversion and Management, Год журнала: 2021, Номер 246, С. 114694 - 114694

Опубликована: Сен. 2, 2021

Язык: Английский

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