Global MPPT algorithm with coordinated control of PSO and INC for rooftop PV array

Liu, Jinhua; Li, Junyin; Wu, Jianan; Zhou, Wenhui

doi:10.1049/joe.2017.0437

Cited by 44 publications

(23 citation statements)

References 3 publications

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“…Artificial intelligence (AI)-based MPPT algorithms are proposed in [26][27][28][29], which include particle swarm optimization (PSO), ant colony, and firefly algorithms. The PSO algorithm is popular for MPPT because of its simplicity of the mathematical structure and implementation [30,31].…”

Section: Introductionmentioning

confidence: 99%

Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

Alshareef

Lin

et al. 2019

Energies

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This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP.

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Section: Introductionmentioning

confidence: 99%

Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

Alshareef

Lin

et al. 2019

Energies

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“…But its popularity is limited due to weak exploration because of which there could be premature convergence. The weak exploration is due to the attraction of all particles more towards the swarm leader in conventional PSO as discussed in [11,12]. To balance the exploration and exploitation of PSO algorithm, control parameters need to be tuned, these being problem specific.…”

Section: Introductionmentioning

confidence: 99%

Enhanced leader adaptive velocity particle swarm optimisation based global maximum power point tracking technique for a PV string under partially shaded conditions

Mathi

Chinthamalla

2019

IET Renewable Power Generation

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Under partially shaded conditions (PSC), obtaining the global maximum on the multi-peak power-voltage (P-V) curve by particle swarm optimisation (PSO) method requires proper tuning of parameters to avoid premature convergence problems. In this study, a new fast tracking global maximum power point tracking (GMPPT) technique for a photovoltaic (PV) string under PSC is proposed. The proposed GMPPT technique has the advantages of two PSO techniques: leader PSO (LPSO) and adaptive velocity PSO (AVPSO). The proposed enhanced leader adaptive velocity PSO (ELAVPSO) GMPPT technique overcomes the limitations of conventional PSO such as premature convergence and difficulty in parameter tuning. Furthermore, a new shading detection scheme is proposed, which accurately finds the type of shading from P-V curve scanning. With the proposed scheme, ELAVPSO GMPPT method is used only to find the global maximum power point during PSC and perturb & observe algorithm is used for maximum power point tracking during uniform irradiance. Under PSC, the limits of the search space of the proposed GMPPT technique can be identified at the time of curve scanning. The proposed algorithm is simulated and experimentally validated by using a boost direct current-direct current (DC-DC) converter prototype.

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“…However, in many situations of operation, the PV array may be subjected to partial shading conditions. As a result, the adopted MPPT technique can track a local maximum power point (LMPP) instead of the global maximum power point (GMPP) [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of MPPT algorithms bio‐inspired by grey wolves employing a feed‐forward control loop in a three‐phase grid‐connected photovoltaic system

Sampaio

Rocha

Silva

et al. 2019

IET Renewable Power Generation

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It is well-known that performance of photovoltaic (PV) systems can be severely deteriorated when PV arrays are subjected to partial shading conditions, once the traditional techniques used for maximum power point tracking (MPPT) could not operate in the global maximum power point (GMPP). Thus, to overcome this problem and achieve the GMPP, four MPPT techniques bio-inspired in the grey wolf optimisation (GWO) are presented. These MPPT techniques, which are named as GWO, GWO-Beta, GWO-IC (Incremental Conductance), and GWO-P&O (Perturb and Observe), are evaluated and compared to each other by employing a double-stage three-phase grid-connected PV system, which is composed of DC/DC converter and three-phase inverter. Commonly, the DC-bus voltage regulation of double-stage PV systems presents slow dynamic behaviour to avoid disturbances in the currents injected into the grid. As a result, MPPT algorithms suffer with this problem since they must be executed considering this condition. To overcome this problem, a feed-forward control loop (FFCL) is implemented to improve the DC-bus voltage regulation during abrupt solar irradiance changes, as well as accelerating the MPPT algorithms dynamics. By means of extensive experimental and simulation results, the performance and effectiveness of the four MPPT techniques, as well as the FFCL, are evaluated.

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Global MPPT algorithm with coordinated control of PSO and INC for rooftop PV array

Cited by 44 publications

References 3 publications

Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

Enhanced leader adaptive velocity particle swarm optimisation based global maximum power point tracking technique for a PV string under partially shaded conditions

Comparative analysis of MPPT algorithms bio‐inspired by grey wolves employing a feed‐forward control loop in a three‐phase grid‐connected photovoltaic system

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