An experimental study on photovoltaic module with optimum power point tracking method

Thangamani, Kaliraja; Manickam, Martin Leo; Chellaswamy, C.

doi:10.1002/2050-7038.12175

Cited by 10 publications

(7 citation statements)

References 54 publications

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“…The performance of the proposed hybrid GMPPT technique under uniform and shading conditions is compared with both conventional and hybrid techniques shown in Figure 8, and the results are tabulated in Table 4. To measure the accuracy of tracking, static MPPT efficiency η static can be used which is given by (15) F I G U R E 6 Simulation results of GMPPT techniques: A, BF-PSO; B, ABF-PSO; and C, proposed hybrid GMPPT technique. ABF-PSO, adaptive butterfly particle swarm optimization; BF-PSO, butterfly particle swarm optimization; GMPPT, global maximum power point tracking F I G U R E 7 Simulation results of parameter variation of GMPPT techniques: A, BF-PSO; B, ABF-PSO.…”

Section: Simulation Resultsmentioning

confidence: 99%

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A hybrid global maximum power point tracking method based on butterfly particle swarm optimization and perturb and observe algorithms for a photovoltaic system under partially shaded conditions

Mathi

Chinthamalla

2020

Int Trans Electr Energ Syst

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Background: In this paper, a new hybrid global maximum power point tracking (GMPPT) technique is proposed for faster and accurate tracking of global maximum power point (GMPP) without premature convergence. It is a combination of modified particle swarm optimization (PSO) and perturb and observe (P&O) methods. The proposed GMPPT technique, adaptive butterfly PSO (ABF-PSO) uses butterfly swarm intelligence for modifying the conventional PSO algorithm with parameter tuning to avoid premature convergence. Aims: Hybrid global maximum power point tracking (GMPPT) technique is proposed for faster and accurate tracking of global maximum power point (GMPP) without premature convergence. Further, a new reinitialization of particles for any irradiance change is proposed to get faster tracking. Materials & Methods: In the proposed hybrid GMPPT technique, first GP region is easily identified with adaptive sensitivity parameter of the ABF-PSO algorithm and in the region identified, GMPP tracking is continued with P&O algorithm with variable length perturbations to avoid the unnecessary exploration of search space even after reaching global peak (GP) region. Results: The combined effect of adaptive parameters, global region identification with adaptive sensitivity, proposed reinitialization method, and steadystate tracking with variable step P&O results in fast and accurate tracking of GMPP with low power oscillations during GP region identification stage and steady-state.

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Section: Simulation Resultsmentioning

confidence: 99%

“…To obtain the GP among the local maxima of the multipeak P‐V curve, GMPPT techniques are used 15 . PSO‐based GMPPT techniques are simple and most popular GMPPT techniques found in the literature.…”

Section: Introductionmentioning

confidence: 99%

A hybrid global maximum power point tracking method based on butterfly particle swarm optimization and perturb and observe algorithms for a photovoltaic system under partially shaded conditions

Mathi

Chinthamalla

2020

Int Trans Electr Energ Syst

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“…30 The primary being insolation and temperature greatly influences the amount of current generated and output voltage. For instance, irradiation controls the short circuit current delivered by the panel 31 ; while temperature defines the open-circuit voltage. 29 To imply its significance typical solar PV panel of Kotak 80 W V-I characteristics at different insolation and temperature is shown in Figure 2.…”

Section: Solar Pv and Its Characteristicsmentioning

confidence: 99%

Review on parameter estimation techniques of solar photovoltaic systems

Venkateswari

Rajasekar

2021

Int Trans Electr Energ Syst

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Beyond meeting power demand, switching to solar energy especially solar photovoltaic (PV) offers many advantages like modularity, minimal maintenance, pollution free, and zero noise. Yet, its cell modeling is critical in design, simulation analysis, evaluation, and control of solar PV system; most importantly to tap its maximum potential. However, precise PV cell modeling is complicated by PV nonlinearity, presence of large unknown model parameter, and absence of a unique method. Since number of model parameters involved is directly related to model accuracy, and efficiency; determination of its values assume high priority. Besides, application of meta-heuristic algorithms via numerical extraction is popular as it suits for any PV cell/module types and operating conditions. However, existence of many algorithms have drawn attention toward assessment of each method based on its merits, demerits, suitability/ability to parameter estimation problem, and complexity involved. Hence, few authors reviewed the subject of PV model parameter estimation.But existing reviews focused on comparative analysis of analytical and metaheuristic approaches, analysis of models, and application of meta-heuristic methods for model parameter extraction. Thus, lack a comprehensive analysis on methods based on different objective function, assessment on

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“…Photovoltaic array (PV) model is discussed in many literatures [32,33]. The output of photovoltaic panel depends on the solar irradiation energy received by the module plane [34], which depends on the local climate conditions, installation method, module type, etc.…”

Section: Photovoltaic Array Modelmentioning

confidence: 99%

Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

et al. 2020

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The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability and stability, particularly compared with the intermittent wind and solar energy. In this paper, an island hybrid energy microgrid composed of photovoltaic, wind, tidal current, battery and diesel is constructed according to the actual energy sources. A sizing optimization method based on improved multi-objective grey wolf optimizer (IMOGWO) is presented to optimize the hybrid energy system. The proposed method is applied to determine the optimal system size, which is a multi-objective problem including the minimization of annualized cost of system (CACS) and deficiency of power supply probability (DPSP). MATLAB software is utilized to program and simulate the hybrid energy system. Optimization results confirm that IMOGWO is feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. Furthermore, comparison of hybrid systems with and without tidal current turbines is undertaken to confirm that the utilization of tidal current turbines can contribute to enhancing system reliability and reducing system investment, especially in areas with abundant tidal energy sources.

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An experimental study on photovoltaic module with optimum power point tracking method

Cited by 10 publications

References 54 publications

A hybrid global maximum power point tracking method based on butterfly particle swarm optimization and perturb and observe algorithms for a photovoltaic system under partially shaded conditions

A hybrid global maximum power point tracking method based on butterfly particle swarm optimization and perturb and observe algorithms for a photovoltaic system under partially shaded conditions

Review on parameter estimation techniques of solar photovoltaic systems

Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

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