Development of Cuckoo Search MPPT Algorithm for Partially Shaded Solar PV SEPIC Converter

Basha, CH Hussaian; Bansal, Viraj; Rani, C.; Brisilla, R. M.; Odofin, Sarah

doi:10.1007/978-981-15-0035-0_59

Cited by 53 publications

(10 citation statements)

References 13 publications

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“…However, these algorithms lack the functionality necessary to follow MPP under complex PSCs and deliver optimum performance in terms of convergence and tracking speed for GMPP harvesting. The optimization techniques are Cuckoo Search (Hussaian Basha et al, 2020), flower pollination (FP) (Pei et al, 2018), Artificial Bee Colony (Sundareswaran et al, 2014), Grey-Wolf Optimization (GWO) (Mohanty et al, 2016) and particle swarm optimization (PSO) (Khare and Rangnekar, 2013), differential evolution (Tajuddin et al, 2018). PSO presents a solution to discover a global maximum with improved precision and rapid convergence (Khare and Rangnekar, 2013).…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of a novel hybrid MPPT technique for fast tracking of GMPP in solar PV system under PSCs

Bonthagorla

Mikkili

2023

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Purpose To generate electricity, solar photovoltaic (PV) systems are among the best, most eco-friendly and most cost-effective solutions available. Extraction of maximum possible electricity from the solar PV system is complicated by a number of factors brought on by the ever-changing weather conditions under which it must operate. Many conventional and evolutionary algorithm-based maximum power point tracking (MPPT) techniques have the limitation of not being able to extract maximum power under partial shade and rapidly varying irradiance. Hence, the purpose of this paper is to propose a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid bridge link-honey comb (BL-HC) configured PV system to enhance the better maximum power during dynamic and steady state operations within less time. Design/methodology/approach In this method, a hybridization of two algorithms is proposed to track the true with faster convergence under PSCs. Initially, the slime mould optimization (SMO) algorithm is initiated for exploration of optimum duty cycles and later P&O algorithm is initiated for exploitation of global duty cycle for the DC–DC converter to operate at GMPP and for fast convergence. Findings The effectiveness of the proposed HSMO MPPT is compared with adaptive coefficient particle swarm optimization (ACPSO), flower pollination algorithm and SMO MPPT techniques in terms of tracked GMPP, convergence time/tracking speed and efficacy under six complex partial shading conditions. From the results, it is noticed that the proposed algorithm tracks the true GMPP under most of the shading conditions with less tracking time when compared to other MPPT techniques. Originality/value This paper proposes a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid BL-HC configured PV system enhance the better maximum power under partial shading conditions (PSCs). This method operated in two stages as SMO for exploration and P&O for exploitation for faster convergence and to track true GMPP under PSCs. The proposed approach largely improves the performance of the MPP tracking of the PV systems. Initially, the proposed MPPT technique is simulated in MATLAB/Simulink environment. Furthermore, an experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

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Section: Introductionmentioning

confidence: 99%

Hardware implementation of a novel hybrid MPPT technique for fast tracking of GMPP in solar PV system under PSCs

Bonthagorla

Mikkili

2023

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“…Furthermore, the DMPP techniques should give the advantages of both soft computing and conventional DMPP algorithms [14]. The optimization methods for MPPT in PV systems include Artificial Bee Colony (ABC) [15], Ant Colony Optimization (ACO) [16], Genetic Algorithm (GA) [17], Differential Evaluation (DE) [18], Grey Wolf Optimization (GFO) [19], and Cuckoo's Search (CS) [20]. The PV system's efficiencies have been rated based on reaction speed, accuracy, efficiency, partial shading, and performance under quick response.…”

Section: Introductionmentioning

confidence: 99%

Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

et al. 2022

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Photovoltaic (PV) solar energy is a very promising renewable energy technology, as solar PV systems are less efficient because of climate conditions, temperature, and irradiance change. So, to resolve this problem, two PV topologies are used, i.e., centralized and distributed PV systems. The centralized technique is quicker than the distributed technique in terms of convergence speed and a faster power tracking approach. In the event of uniform irradiance, the centralized system also has the benefit of supplying superior energy, but in PS scenarios, a huge amount of energy is lost. However, the distributed approach requires current and voltage measurements at each panel, resulting in a massive data set. Nevertheless, in the event of shading circumstances, the distributed technique is highly effective because a modular level power electronics (MLPE) converter is used. While in a centralized PV system, there is only a single DC-DC converter for the whole PV system. In this research work, a DFO-based DC-DC converter is designed for modular level, with an ability to perform a rapid shutdown of the module under fire hazard conditions, troubleshooting, and monitoring of a module in a very efficient way. The robustness of the proposed MPPT DFO algorithm is tested with different techniques such as Cuckoo Search (CS), Fruit Fly Optimization (FFO), Particle swarm optimization (PSO), Incremental conductance (InC), and Perturb and observe(P&O) techniques. The proposed technique shows better results in terms of MPPT efficiency, dynamic responsiveness, and harmonics. Furthermore, the result of MLPE and the centralized system is verified by using the Helioscope with different inverter companies like SMA, Tigo, Enphase, Solar edge, and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining the maximum power point.

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“…Despite the many MPPT methods available to photovoltaic system users, it is possible to select the best suitable method for the required application [10][11][12][13][14][15]. Occurrence of several local maximum points due to relative shadow in a photovoltaic array can be a real obstacle to the proper operation of an MPP tracker.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

Kamarposhti¹,

Shokouhandeh²,

Çolak³

et al. 2022

Computers, Materials &Amp; Continua

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The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector. The capability of online fuzzy tracking systems is maximum power, resistance to radiation and temperature changes, and no need for external sensors to measure radiation intensity and temperature. However, the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing. The controller used in the maximum power point tracking (MPPT) circuit must be able to adapt to the new radiation conditions. Therefore, in this paper, to more accurately track the maximum power point of the solar system and receive more electrical power at its output, an adaptive fuzzy control was proposed, the parameters of which are optimized by the whale algorithm. The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm (P&O) method, which is a practical and high-performance method. To evaluate the performance of the proposed algorithm, the particle swarm algorithm optimized the adaptive fuzzy controller. The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system. Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method, which distinguishes it from other methods. On the other hand, the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm, which confirms the higher accuracy of the proposed algorithm.

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Development of Cuckoo Search MPPT Algorithm for Partially Shaded Solar PV SEPIC Converter

Cited by 53 publications

References 13 publications

Hardware implementation of a novel hybrid MPPT technique for fast tracking of GMPP in solar PV system under PSCs

Hardware implementation of a novel hybrid MPPT technique for fast tracking of GMPP in solar PV system under PSCs

Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

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