Modified Firefly‐Based Maximum Power Point Tracking Algorithm for PV Systems Under Partial Shading Conditions

Chitra, A.; Yogitha, G.; Sivaramakrishnan, K.; Sultana, Razia; Sanjeevikumar, P.

doi:10.1002/9781119682035.ch8

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…These algorithms present a slow response under varying irradiation levels. The heuristics-based algorithms like particle swarm optimization (PSO), Firefly Algorithm (FA), and genetic algorithm (GA) have been released to improve the MPPT performance under both partial shading, and uniform irradiance climatic conditions as in [12][13][14] .…”

Section: Related Workmentioning

confidence: 99%

Recursive bit assignment with neural reference adaptive step (RNA) MPPT algorithm for photovoltaic system

Hegazy

Shokair

Saad

2023

Sci Rep

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Recent research has focused on photovoltaic (PV) systems due to their important properties. The efficiency of the PV system can be enhanced by many Maximum Power Point Tracking (MPPT) algorithms proposals. MPPT algorithms are used to achieve maximum PV output power by optimizing the duty cycle of the DC–DC buck/boost converter. This paper introduces an RNA algorithm as an efficient MPPT algorithm for the photovoltaic system. This proposed RNA algorithm consists of two main segments. The first segment is an artificial neural network for generating reference power. The second segment is a proposed Recursive Bit Assignment (RBA) network to allow variable step size of the boost converter duty cycle. The instant PV power adopts the RBA network to produce the variable duty cycle increment value. Additionally, the neural network is implemented in such a way to obtain the best performance. Many simulation results using MATLAB to test the system performance are presented. The performance characteristics of the photovoltaic system with variable irradiance and variable temperature are simulated. From results, the proposed RNA algorithm achieves fast tracking time, high energy efficiency, true maximum power point and acceptable ripple. Additionally, comparisons between the RNA algorithm and other related algorithms such as Perturb and Observe, the Neural Network and the Adaptive Neural Inference System Algorithms are executed. The proposed RNA algorithm achieves the best performance in all case studies such as; irradiance profile variation, severe temperature and irradiance diversions, and partial shading conditions. Besides, the experimental circuit of the PV system is also presented.

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Section: Related Workmentioning

confidence: 99%

Recursive bit assignment with neural reference adaptive step (RNA) MPPT algorithm for photovoltaic system

Hegazy

Shokair

Saad

2023

Sci Rep

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“…The most brightness firefly is only available to move in a direction that will cause increasing its brightness. In [14], a modified firefly algorithm was investigated for tracking the actual GMMP. In the classical MPPT algorithms such as P&O, incremental conductance, and hill climbing techniques, there is confusion between GMPP and local peak point which results in a low power extraction from the PV module.…”

Section: Related Workmentioning

confidence: 99%

Maximum power point tracking based on modified firefly scheme for PV system

2022

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Solar photovoltaic (PV) cells play a major role as natural, renewable energy sources. It is characterized by having nonlinear photoelectric voltage and current characteristics. These properties depend on the amount of solar radiation and temperature. PV can be used as an electrical charge circuit. But due to the low efficiency of the resulting photoelectric power, it should operate in conditions of maximum power point. There are several algorithms for achieving this maximum power point condition. In this paper, a PV system is proposed to obtain the maximum power point using a modified firefly algorithm. The modifications have been made both in fireflies’ locations and their random movement. Several simulations are implemented using MATLAB to verify the performance of the proposed system. From the simulation results, the proposed algorithm outperforms all traditional algorithms such as firefly and perturbation and observation technique. Moreover, the impacts of some variants of the proposed technique are studied. The variants are the number of the fireflies, the randomness, the maximum iterations, and the effect of changing the sampling time. A proposed modified firefly is presented with an MPPT controller in the PV system to ensure operating the PV at the MPP. Additionally, the mathematical expressions are explained. Moreover, MATLAB simulation programs are done to compare the performance of the proposed scheme with other related ones.

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“…These algorithms present a slow response under varying irradiation level. The heuristics based algorithms like particle swarm optimisation (PSO), Firefly Algorithm(FA), and genetic algorithm (GA) have been released to improve the MPPT performance under both partial shading and uniform irradiance climatic conditions as in [11]- [13].…”

Section: Related Workmentioning

confidence: 99%

Recursive Bit Assignment With Neural Reference Adaptive Step (RNA) MPPT Algorithm for Photoviolatic System

Hegazy

Shokair

Saad

2021

Preprint

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BackgroundNowadays, most of the recent research is directed towards photovoltaic harvesting systems due to their great characteristics. To increase the efficiency of a photoviolatic harvesting system, Maximum Power Point Tracking algorithms are utilized to achieve the maximum output power of the PV. This is done by optimizing the duty ratio of the DC-DC boost converter. ResultsIn this paper, a proposed RNA algorithm is introduced as an efficient MPPT algorithm for the PV system. This proposed RNA algorithm depends on two main parts. The First part is an artificial neural network to produce a reference power. The Second one is a proposed Recursive Bit Assignment network to present the variable step size of the duty ratio of the DC-DC boost converter. The RBA network consists of N-bit memory. The instantaneous PV power value sets the contents of the memory to generate the variable step size of the duty ratio. Moreover, the design of the neural network to give its best performance is explained. ConclusionsThe performance of the chosen PV module is simulated for a variable solar radiation and a constant temperature. Simulation studies are performed using MATLAB to evaluate the system performance. From simulation results, the proposed RNA can achieve a fast tracking time, a high power efficiency, an actual maximum power point and an acceptable ripple. Additionally, comparisons between the RNA algorithm and other related algorithms such as Perturbe and Observe, Neural Network, and Adaptive Neural Interference System algorithms are executed. The proposed RNA achieves the best performance in all terms.

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Modified Firefly‐Based Maximum Power Point Tracking Algorithm for PV Systems Under Partial Shading Conditions

Cited by 5 publications

References 17 publications

Recursive bit assignment with neural reference adaptive step (RNA) MPPT algorithm for photovoltaic system

Recursive bit assignment with neural reference adaptive step (RNA) MPPT algorithm for photovoltaic system

Maximum power point tracking based on modified firefly scheme for PV system

Recursive Bit Assignment With Neural Reference Adaptive Step (RNA) MPPT Algorithm for Photoviolatic System

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