Integrated Maximum Power Point Tracking System for Photovoltaic Energy Harvesting Applications

Mandourarakis, Ioannis; Gogolou, Vasiliki; Koutroulis, Eftichios; Siskos, S.

doi:10.1109/tpel.2022.3156400

Cited by 30 publications

(6 citation statements)

References 48 publications

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“…To maintain www.nature.com/scientificreports/ constant sunlight on PV modules, there are several power points identifying methods available in the literature. However, the demerits of those conventional strategies are moderate tracking speed, suitable only for uniform sunlight conditions, high power consumption at the time of perturbation, dependent on PV array manufacturing, a high number of sensors needed for sensing the PV parameters, plus more voltage ripples have existed in the converter generated power 78 . Also, these techniques may not apply to rapid changes in sunlight temperatures.…”

Section: Design Of Various Mppt Controllersmentioning

confidence: 99%

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions

Hussaian Basha,

Palati,

Dhanamjayulu

et al. 2024

Sci Rep

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At present, fossil fuel-based power generation systems are reducing drastically because of their less availability in nature. In addition, it produces hazardous gasses and high environmental pollution. So, in this work, the solar natural source is selected for generating the electricity. Due to the nonlinear behavior of PV, achieving maximum voltage from the Photovoltaic (PV) system is a more tough job. In this work, various hybrid optimization controllers are studied for tracing the working power point of the PV under different Partial Shading Conditions. The studied hybrid optimization MPPT methods are equated in terms of oscillations across MPP, output power extraction, settling time of the MPP, dependency on the PV modeling, operating duty value of the converter, error finding accuracy of MPPT, algorithm complexity, tracking speed, periodic tuning required, and the number of sensing parameters utilized. Based on the simulative comparison results, it has been observed that the modified Grey Wolf Optimization based ANFIS hybrid MPPT method provides good results when equated with the other power point tracking techniques. Here, the conventional converter helps increase the PV source voltage from one level to another level. The proposed system is investigated by using the MATLAB/Simulink tool.

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Section: Design Of Various Mppt Controllersmentioning

confidence: 99%

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions

Hussaian Basha,

Palati,

Dhanamjayulu

et al. 2024

Sci Rep

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“…The algorithm calculates the output power (P) and the power change (ΔP) by using these inputs. The ΔP is utilized to determine if the system has entered a stable state, and in combination with the voltage change (ΔV), it is used to determine if the algorithm should be restarted [26]. When there is a change in the external environment, the algorithm compares ΔP and ΔV with predefined thresholds to assess the extent of the deviation from the new maximum power point.…”

Section: B Fuzzy Logic Algorithm Based Afomentioning

confidence: 99%

Improved Photovoltaic MPPT Algorithm Based on Ant Colony Optimization and Fuzzy Logic Under Conditions of Partial Shading

Xia,

Li,

Zhu

2024

IEEE Access

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Under conditions of partial shadowing, traditional Maximum Power Point Tracking (MPPT) algorithms face difficulties in precisely locating the maximum power point (MPP) of the system. To address this problem, this paper proposes an optimization algorithm, Ant-Fuzzy Optimization (AFO) algorithm. AFO utilizes the global search capability of the ant colony optimization (ACO) algorithm and the high precision performance of the fuzzy logic (FL) algorithm, mitigating the tendency of the fuzzy algorithm to fall into local optima in shadow conditions. Internally, the AFO algorithm comprises two parallel logics, selecting different strategies for tracking based on varying environmental states, achieving a balance between tracking accuracy and computational efficiency. This intelligent logic selection mechanism allows the algorithm to flexibly adapt to diverse working environments of photovoltaic (PV) arrays, enhancing the robustness and adaptability of the system. The paper establishes corresponding simulation models in MATLAB/SIMULINK and validates AFO through hardware experiments on the dSPACE real-time simulation system. The results demonstrate the feasibility and effectiveness of AFO in practical environments. Both simulation and experimental prototypes indicate that AFO can rapidly and accurately extract the maximum power point with an accuracy of 98.7%. Furthermore, AFO exhibits rapid dynamic response characteristics, reaching steady state within 0.9 seconds, providing a reliable solution for optimizing the output power of photovoltaic arrays.INDEX TERMS Ant Colony Optimization (ACO), fuzzy logic (FL), Maximum Power Point Tracking (MPPT), partial shading conditions, photovoltaic (PV).

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“…The continual pursuit of efficiency in photovoltaic systems has led to the development and study of various MPPT techniques. Among the most common methods are Perturb and Observe (P&O), which continuously adjusts the solar panel voltage to maximize output power, and Incremental Conductance, which uses an analysis of the current-voltage characteristic curve to determine the maximum power point (Vinnikov et al, 2021;Mandourarakis et al, 2022;Hai et al, 2023;Katche et al, 2023). Other approaches include the Sequential Search Algorithm, systematically exploring operating points to identify MPPT, and techniques based on artificial intelligence algorithms such as neural networks and genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Robust power control for PV and battery systems: integrating sliding mode MPPT with dual buck converters

Fekik,

Hamida,

Azar

et al. 2024

Front. Energy Res.

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This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future.

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Integrated Maximum Power Point Tracking System for Photovoltaic Energy Harvesting Applications

Cited by 30 publications

References 48 publications

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions

Improved Photovoltaic MPPT Algorithm Based on Ant Colony Optimization and Fuzzy Logic Under Conditions of Partial Shading

Robust power control for PV and battery systems: integrating sliding mode MPPT with dual buck converters

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