A Hybrid Algorithm for Tracking of GMPP Based on P&amp;O and PSO With Reduced Power Oscillation in String Inverters

Chakkarapani, M.; Raman, G. Venkat; Raman, Gurupraanesh; Nagamani, C.

doi:10.1109/tie.2016.2590382

Cited by 152 publications

(82 citation statements)

References 27 publications

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“…To improve tracking efficiency, convergence speed, and steady-state response as well as reduce the searching space, a particle swarm optimization (PSO) scheme is integrated with P&O. In this method, the P&O scheme consistently searches for a unique MPP under uniform environmental conditions to track a current LMPP at non-uniform environmental conditions [45,46]. Then, the PSO technique is employed to regulate the perturbation size of the P&O method at an instant of the PSCs [45,47].…”

Section: Pando Mpptsmentioning

confidence: 99%

“…In this method, the P&O scheme consistently searches for a unique MPP under uniform environmental conditions to track a current LMPP at non-uniform environmental conditions [45,46]. Then, the PSO technique is employed to regulate the perturbation size of the P&O method at an instant of the PSCs [45,47]. An adaptive P&O algorithm is merged with FLC to obtain a high tracking accuracy and reduced computational time without requiring PIC.…”

Section: Pando Mpptsmentioning

confidence: 99%

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Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems

et al. 2018

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This paper elaborates a comprehensive overview of a photovoltaic (PV) system model, and compares the attributes of various conventional and improved incremental conductance algorithms, perturbation and observation techniques, and other maximum power point tracking (MPPT) algorithms in normal and partial shading conditions. Performance evaluation techniques are discussed on the basis of the dynamic parameters of the PV system. Following a discussion of the MPPT algorithms in each category, a table is drawn to summarize their key specifications. In the performance evaluation section, the appropriate PV module technologies, atmospheric effects on PV panels, design complexity, and number of sensors and internal parameters of the PV system are outlined. In the last phase, a comparative table presents performance-evaluating parameters of MPPT design criterion. This paper is organized in such a way that future researchers and engineers can select an appropriate MPPT scheme without complication.

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Section: Pando Mpptsmentioning

confidence: 99%

Section: Pando Mpptsmentioning

confidence: 99%

Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems

et al. 2018

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“…A bioinspired algorithm-based MPPT employed for PSC was elaborated in [12]. A modified beta algorithm and PSO-based tracking of the GMPP was discussed in [13,14].…”

Section: Introductionmentioning

confidence: 99%

Global maximum operating point tracking for PV system using fast convergence Firefly algorithm

2019

Turk J Elec Eng & Comp Sci

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Global maximum operating point (GMOP) tracking is an important requirement of solar photovoltaic (PV) systems under partial shading conditions (PSCs). Though the perturb and observe algorithm is simple and effective, it fails to recognize the GMOP. This paper explores the application of the firefly algorithm (FA) to the maximum power point tracking (MPPT) problem of PV systems. In order to determine the shortest path to reach the GMOP under various PSCs, a new fast convergence firefly algorithm (FA) is proposed. Additionally, the change in firefly position is limited to a maximum value identified based on the characteristics of the PSC. The fast convergence method is guaranteed to find the GMOP, avoiding the local operating point obstacle through a repeated space search technique.Using MATLAB, the algorithm is implemented on a model PV system. An experimental 300-W PV system is developed to validate the operating point of the PV system under various PSCs. The proposed method is tested on a 5-kW solar power plant. The results demonstrate that the proposed MPPT algorithm outperforms particle swarm optimization, FA-based MPPTs, and other methods available in the literature.

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“…The use of by-pass diodes has limited this problem, but power potentially available from by-passed cells is lost. Various control methods, such as such as perturb and observe (P&O) (Femia et al,2009;Killi and Samanta, 2015;Elbaset et al, 2016), Incremental conductance (IncCond) (Elgendy et al, 2012;Radjai et al, 2014;Li et al, 2016), hill climbing (HC) (Alajmi et al, 2011;Xiao et al, 2016), fuzzy logic (Messai et al, 2011;Letting et al, 2012;Cheng et al, 2015;Rezvani and Gandomkar, 2016), artificial neural network (ANN) ( Liu et al, 2013;Boumaaraf et al 2015;Lin et al, 2016;Messalti et al, 2017), particle swarm optimization (PSO) Cheng et al, 2015;Letting et al, 2012;Manickam et al, 2016;Renaudineau et al, 2015), sliding mode (Kim, 2007;Chu and Chen, 2009;Zhang et al, 2015;Mojallizadeh et al, 2016;Ouchen et al, 2016) and so on, have been proposed to enable optimal power generation from the chained PV strings with by-pass diodes and under partial shading conditions, but satisfactory solutions in terms of simultaneously maximizing the power generated and protecting the PV panels have still been a challenge (Rezk and Eltamaly, 2015;Chen et al, 2015;Liu et al, 2016;Kumar and Chatterjee, 2016;Gupta et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents a new MPPT control strategy based on a modified particle swarm optimization (MPSO) for the multilevel DC-link PV system proposed in (Abdalla et al, 2016) operating under PSCs and load variations. PSO is an evolutionary computation algorithm and has been applied for MPPT of PV systems in some investigations Koad et al, 2016;Manickam et al, 2016;Seyedmahmoudian et al, 2015;Sundareswaran et al, 2015;Liu et al, 2012;Babu et al, 2015), but none of these treat the multilevel DC-link PV converter system. In this context the benefit of using the MPSO is clear; it can predict all the PV-Converter voltages at their respective MPPs as defined by their light levels quickly and without disturbing the system.…”

Section: Introductionmentioning

confidence: 99%

Multilevel DC-link converter photovoltaic system with modified PSO based on maximum power point tracking

et al. 2017

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Abstract:The paper presents a modified particle swarm optimization (MPSO) control strategy for the maximum power point tracking applied to a multilevel DC-link PV power generation system. This system consists of a chain of PV modules with individual DC-DC converters and has a DC-AC converter at its terminals for generating AC voltage. The proposed MPSO control method can predict all PV voltages at their maximum power point on the basis of respective light level and temperature. A PWM with permutation of DC converter switching is applied to balance switch utilization. The method is applied to a system of five-voltage levels with two PV-converter units under different light intensities and the results are compared with the perturb and observe (P&O) method and the traditional PSO method under the same operating conditions. The paper shows that the new method gives more rapid convergence, increased power output, and lower total harmonic distortion.

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A Hybrid Algorithm for Tracking of GMPP Based on P&O and PSO With Reduced Power Oscillation in String Inverters

Cited by 152 publications

References 27 publications

Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems

Performance Evaluation of Maximum Power Point Tracking Approaches and Photovoltaic Systems

Global maximum operating point tracking for PV system using fast convergence Firefly algorithm

Multilevel DC-link converter photovoltaic system with modified PSO based on maximum power point tracking

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