Comparative Study of Optimal PV Array Configurations and MPPT under Partial Shading with Fast Dynamical Change of Hybrid Load

Boghdady, Tarek A.; Kotb, Yasmin E.; Aljumah, Abdullah; Sayed, Mahmoud M.

doi:10.3390/su14052937

Cited by 13 publications

(3 citation statements)

References 44 publications

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“…Therefore, it is essential to modify the correlation coefficient of PV array. Equations ( 2)-( 7) are the modified equations of the PV array [21].…”

Section: The Operational Principle Of Photovoltaicmentioning

confidence: 99%

A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array

Zhang

Wang

et al. 2022

International Journal of Photoenergy

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This article proposes a novel MPPT algorithm based on the firefly algorithm and elite ant system-trained Elman neural network (FA-EAS-ElmanNN). First, the position of fireflies is randomly initialized by the firefly algorithm (FA), meanwhile the firefly individuals with higher attractiveness degree value are selected as the optimal solution. Second, the extra pheromones are artificially released to boost the positive feedback effect and convergence rate of the elite ant system (EAS). Third, the weight and threshold of the Elman neural network (ElmanNN) are updated by the FA and EAS. Also, the trained ElamnNN is employed to acquire the maximum voltage of the photovoltaic (PV) array. At last, the PID controller and PWM technology are adapted to regulate the switch time of the boost converter. Furthermore, MATLAB/Simulink is adopted to acquire the datasets of irradiance, temperature, and maximum voltage and validate the reliability and superiority of the proposed algorithm under complex atmospheric conditions. The tracking characteristic, response speed, and efficiency of the proposed MPPT algorithm are contrasted with the particle swarm optimization (PSO), ant colony optimization (ACO), ACO-artificial neural network (ACO-ANN), and PSO-RBF neural network (PSO-RBNFNN) algorithm via simulation. The efficiency of the FA-EAS-ElmanNN algorithm is 99.73%, compared with the ACO-ANN, PSO-RBFNN, PSO, and ACO algorithm, which is increased by 0.49%, 0.58%, 1.2% %, and 1.5%, respectively. Additionally, the experimental setup is built to demonstrate the tracking characteristic of the proposed MPPT algorithm.

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“…Therefore, it is essential to modify the correlation coefficient of PV array. Equations ( 2)-( 7) are the modified equations of the PV array [21].…”

Section: The Operational Principle Of Photovoltaicmentioning

confidence: 99%

A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array

Zhang

Wang

et al. 2022

International Journal of Photoenergy

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“…In recent years, the continuous development of new photovoltaic energy, domestic and foreign scholars put forward numerous optimization schemes for MPPT algorithm [5][6][7],the traditional MPPT algorithm short-circuit current method and open-circuit voltage method can achieve fast tracking under simple pair control [8],but for the outdoor environment changes, the above algorithm tracking effect is poor. In outdoor scenes considering the local shadow situation, the traditional algorithm is prone to generate into the local optimum can not meet the maximum power tracking [9][10][11][12][13],the current research, based on the continuous development of artificial intelligence algorithms, the introduction of particle swarm algorithm, gray wolf algorithm, etc [14][15],based on the algorithm rules are simple to implement, the advantages of limited parameters are used in a variety of scene conditions tracking optimization.…”

Section: Introductionmentioning

confidence: 99%

MPPT Study of Outdoor Sensing Terminal PV System Based on Improved PSO Algorithm

Xiao

Li²,

Tan³

et al. 2023

J. Phys.: Conf. Ser.

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With the popularity of wireless sensor use, the problem of supplying energy to its outdoor nodes is becoming more and more prominent. An outdoor wireless sensing terminal photovoltaic system is proposed to address the problem of outdoor wireless sensing terminal node power supply. The maximum power tracking (MPPT) is achieved by Adaptive weight adjustment Particle swarm optimization (AwaPSO) to achieve fast tracking and stable and reliable output, so that the Buck main circuit output of the PV system can meet the working requirements of the outdoor sensor terminal. The simulation model is built by Matlab/Simulink, and the simulation results show that the improved PSO algorithm can track the maximum power quickly and output stably and reliably, with the stabilization time shortened to 0.041s and the tracking error up to 0.625%, and its speed and stability can meet the outdoor wireless sensing terminal PV power supply requirements.

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“…PV power production has several advantages over other RES, especially in terms of durability, operation, and integration. Among these, the best alternative is solar power [1,2].…”

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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Comparative Study of Optimal PV Array Configurations and MPPT under Partial Shading with Fast Dynamical Change of Hybrid Load

Cited by 13 publications

References 44 publications

A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array

A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array

MPPT Study of Outdoor Sensing Terminal PV System Based on Improved PSO Algorithm

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

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