A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions

Chtita, Smail; Motahhir, Saad; Hammoumi, Aboubakr El; Chouder, Aissa; Benyoucef, Abou Soufiane; Ghzizal, Abdelaziz El; Derouich, Aziz; Abouhawwash, Mohamed; Askar, Sameh

doi:10.1038/s41598-022-14733-6

Cited by 47 publications

(22 citation statements)

References 30 publications

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“…A unique hybrid maximal power spot tracking approach built on the principles of particle swarm and grey wolf optimization is presented by Chtita et al [42]. The proposed method not only overcomes the typical drawbacks of existing methods but also offers a straightforward and reliable tracking of maximum power points to manage partial shadowing in solar systems successfully.…”

Section: Related Workmentioning

confidence: 99%

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

et al. 2023

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Cloud Computing is the dynamic provisioning of resources to provide services to end-users over the internet. The realization of cloud computing requires addressing several challenges, such as resource discovery, security, scheduling, and load balancing. Among these research issues, load balancing is the most challenging one. Therefore, in the past few years, research into various static and dynamic algorithms to achieve optimal results is gaining importance. This research proposes Swarm Intelligence (SI) as a loadbalancing solution for cloud computing. Several alternatives in the literature (like genetic algorithm, ACO, PSO, BAT, GWO, and many others) are investigated, but none consider the load balancing convergence time with global optimization. Among these algorithms, this research emphasizes Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). A combined approach of GWO-PSO that capitalizes on the benefits of fast convergence and global optimization is proposed in this paper. These two techniques enhance system efficiency and resource allocation, working together to solve the load-balancing challenge. Compared to other traditional approaches, the findings of this research are promising while achieving globally optimized fast convergence and reducing overall response time. On average, the overall response time of the proposed technique is reduced to 12% as compared to other algorithms. Furthermore, the best optimal value obtained from the objective function of the proposed GWO-PSO algorithm improves PSO to 97.253% in terms of convergence.

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Section: Related Workmentioning

confidence: 99%

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

et al. 2023

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“…First, the work in this study does not consider variable load conditions. However, load variation will be included as another input parameter to the proposed NN model in the future work of these authors (Shams et al, 2021a) (Shams et al, 2021b;Chtita et al, 2022). Second, in the hardware setup, there is no motion control for partial shading (Fares et al, 2021) (Pervez et al, 2021).…”

Section: Conclusion and Scope Of The Future Workmentioning

confidence: 99%

Implementation of an MPPT technique of a solar module with supervised machine learning

et al. 2022

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Automated calibration of a maximum power point tracking (MPPT) algorithm for the photovoltaic (PV) system is pivotal for harnessing the maximum possible energy from solar power. However, most existing calibration methods of such an MPPT system are cumbersome and vary greatly with the environmental condition. Hence, an automated pipeline capable of performing suitable adjustments is highly desirable. We proposed a method using supervised machine learning (ML) in a solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulated to create a dataset in MATLAB/Simulink. Thus, by analyzing the output characteristics of a solar cell, an improved MPPT algorithm on the basis of a neural network (NN) method is put forward to track the maximum power point (MPP) of solar cell modules. Moreover, we implemented the algorithm in a hardware setup and verified the theoretical result with the empirical data. Typically, the performance accuracy of the NN models is around 97∼98%. But our proposed model shows an even higher efficiency (99.8% approximately) without adding to any extra computational cost.

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“…The problem gets even more complex when the array receives non-uniform irradiance [5]. The various factors for unequal irradiance on PV modules can be the variation of incline angles of modules in an array, partial shading by the close entity, scramble of birds, single or multiple leaves that oppose the cell/module, snow or dust particle on the same modules within the array [6,7].…”

Section: Motivation and Incitementmentioning

confidence: 99%

Robust Modified Flower Pollination Algorithm for Power Quality Enhancement in an Autonomous 31-Level Cascaded H-Bridge Photovoltaic Inverter with Partial Shading Conditions

Sahu¹,

Nayak

Choudhury

2022

IJRITCC

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The effect of global warming and the scarcity of fossil fuels has created an enormous problem in today’s era. To overcome such a problem, renewable energy sources, particularly solar energy, play a crucial role in meeting the developing need for power. However, the design of the Solar Photovoltaic (PV) system is interrupted by various factors such as the effect of temperature, isolation, aging, partial shading conditions, etc. Among all the factors mentioned, partial shading results in the significant diminution of power. To address this shading effect and enhance the flexibility of the PV system in terms of better utilization and energy extraction, a 31-Level Cascaded H-Bridge Multilevel Inverter (CHB-MLI) has been implemented to the autonomous PV system comprising of Maximum Power Point Tracking (MPPT) controller, boost converter and variable loads in MATLAB/Simulink architecture. To track maximum power from PV during varying irradiance and temperature and to further improve the system performance in terms of better convergence speed, an MPPT system with a Modified Flower Pollination Algorithm (MFPA) based PID controller has been proposed in this paper. To justify the suggested approach, the is-landed PV system is led to variation in irradiance and load. A detailed comparison of the proposed MFPA technique with classical control techniques has been meticulously discussed. The results obtained indicate that the suggested MFPA tuned PID with MLI outperforms the conventional methods in better system stability, reduced harmonics, and enhanced capacity to track maximum power from the PV system. In addition to this, the Total Harmonic Distortion (THD) using Fast Fourier Transform (FFT) has been found to verify IEEE-1547 power quality constraints. The values are found to be well within limits, thus justifying its real-time applications.

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A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions

Cited by 47 publications

References 30 publications

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

Implementation of an MPPT technique of a solar module with supervised machine learning

Robust Modified Flower Pollination Algorithm for Power Quality Enhancement in an Autonomous 31-Level Cascaded H-Bridge Photovoltaic Inverter with Partial Shading Conditions

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