Journal Article10.1016/j.suscom.2023.100946
A Comprehensive Comparative Study on Intelligence based Optimization Algorithms used for Maximum Power Tracking in Grid-PV Systems
Shane Marlin,S. Jebaseelan +1 more
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TL;DR: This study compares three meta-heuristic optimization algorithms (Mongoose Optimization, Prairie Dog Optimization Algorithm, and their hybrid) for maximum power point tracking in grid-PV systems, evaluating their performance and selecting the most effective algorithm to maximize energy yield and meet grid energy demands.
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Abstract: For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive optimization algorithms for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems in order to meet the energy demands of grid systems. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s
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Citations
Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis
Shahjahan Alias Sarang,Muhammad Amir Raza,Madeeha Panhwar,Malhar Khan,Ghulam Abbas,Ezzeddine Touti,Abdullah Altamimi,Andika Aji Wijaya +7 more
TL;DR: This study compares conventional and digital MPPT techniques for solar power generation, evaluating 10 controllers across various parameters, and finds AI-based controllers outperforming conventional ones in terms of accuracy, with AI-based techniques achieving up to 99.6% accuracy.
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Deep SORT Related Studies
Abdul Majid,Qinbo Qinbo,Saba Brahmani +2 more
TL;DR: This study presents the deep SORT related studies in which the various algorithms have been presented for the sake of understanding and starting point for the researchers interested in computer vision and deep sorting.
Advanced MPPT Control Algorithms: A Comparative Analysis of Conventional and Intelligent Techniques with Challenges
Vaishnavi Chandra Tella,Boker Agili,Mioa He +2 more
TL;DR: This study compares conventional and intelligent MPPT control algorithms with DC-DC converter topologies, evaluating tracking speed, efficiency, and design elements to optimize solar power production in off-grid and grid-connected applications.
Optimal hybrid PV–TEG systems reconfigurations for effective mitigation of partial shading conditions via cooperative Q-learning and advantage actor–critic algorithm
Lei Zhou,Bo Yang,Chuanyun Tang,Zijian Zhang,Jia-le Li,Zhenning Pan,Hai Lu,Hong-Biao Li,Dengke Gao,Lin Jiang +9 more
Abstract: Partial shading conditions (PSCs) negatively impact the effective generation of photovoltaic (PV) systems, due to its uneven irradiation intensity. In order to alleviate the adverse effects of PSC on PV generation, this paper proposes a reinforcement learning methodology based on cooperative Q-learning and advantage actor–critic (A2C) algorithm for hybrid PV-thermoelectric generation (PV–TEG) system reconfiguration. First, hybrid PV–TEG system is used to fully utilize the inherent temperature of PV system and enhance solar energy utilization. Second, the reconfigurations of electrical connections between modules in hybrid PV–TEG system are optimized considering multiple objectives, including the minimization of detrimental effects of PSC and maximization of power output. To achieve this goal, a reinforcement learning method combining Q-learning and A2C is proposed. This hybrid approach also circumvents local optimum traps through heuristic adjustments, offering high adaptability in dynamic optimization scenarios. Simulations were conducted for hybrid PV–TEG systems, 9 × 9 and 15 × 9, along with a hardware-in-the-loop experiment on a 4 × 4 system. The approach achieved increases in maximum output power of 26.54%, 37.35%, and 59.82%, respectively. Comparative results with state-of-the-art techniques verify the superiority of the proposed method.
Data-driven regression controller-based MPPT with image encryption inspired solar PV array reconfiguration under partial shading conditions
Madavena Kumaraswamy,Kanasottu Anil Naik +1 more
Abstract: Partial shading and environmental variations significantly reduce the power output and efficiency of photovoltaic (PV) systems, posing challenges for conventional maximum power point tracking (MPPT) methods that suffer from slow convergence, local maxima trapping, and high computational cost. To address these limitations, this paper proposes an image encryption-inspired PV array static reconfiguration technique based on the Kolakoski sequence transform (KST), combined with data-driven regression-based MPPT controllers. The proposed KST method minimizes current mismatches by intelligently redistributing shaded modules, while decision tree (DT), support vector machine (SVM), neural network (NN), and machine learning (ML) regression methods are employed to determine the optimal duty cycle for a SEPIC converter under varying irradiance conditions. The system is evaluated on both symmetrical 5 × 5 arrays and unsymmetrical 4 × 6 arrays, including experimental validation using a 250 Wp standalone PV setup. In MPPT performance, the regression-based controllers attain GMP enhancements of 47.09%, 45.14%, 27.27%, 13.62%, and 10.73% for 5 × 5 arrays and 74.96%, 44.11%, 40.14%, 18.29%, and 7.15% for 4 × 6 arrays under diverse environmental conditions. The reconfiguration technique achieves global maximum power (GMP) improvements of 32.79%, 14.98%, and 10.15% across various shading scenarios using 9 × 9 arrays. Notably, the proposed KST integrated with SVM regression-based MPPT delivers up to 68% GMPP enhancement, with >98.5% efficiency, convergence <0.35 s, and ripple ≤1.5%, validated across dynamic shading, temperature variation, rapid irradiance changes, and hotspot conditions. These results confirm the robustness, adaptability, and real-time suitability of the proposed KST integrated with ML-based Regression MPPT approach for practical PV optimization.
References
Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review.
César G. Villegas-Mier,Juvenal Rodríguez-Reséndiz,José Manuel Álvarez-Alvarado,Hugo Rodriguez-Resendiz,Ana M. Herrera-Navarro,Omar Rodríguez-Abreo +5 more
TL;DR: A review of different papers, reports, and other documents using ANN for MPPT control is presented in this paper, where the algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm.
118
Optimizing Step-Size of Perturb & Observe and Incremental Conductance MPPT Techniques Using PSO for Grid-Tied PV System
01 Jan 2023
TL;DR: In this paper , a hybrid particle swarm optimization (PSO) algorithm was proposed to optimize the maximum PV output power and to determine the best design variable for penalising the step size of the conventional methods namely the perturb and observe (PO) and the incremental conductance (IC).
76
Whale Optimization Algorithm for PV Based Water Pumping System Driven by BLDC Motor Using Sliding Mode Controller
Siva Ganesh Malla,Priyanka Malla,Jagan Mohana Rao Malla,Ruchira Singla,Pallavi Choudekar,Rajesh Koilada,Manoj Kumar Sahu +6 more
TL;DR: In this article , a hybrid whale optimization-P&O (WOPO) algorithm was proposed to extract the maximum possible power from photovoltaic (PV)-based water pumping system.
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A novel MPPT controller in PV systems with hybrid whale optimization-PS algorithm based ANFIS under different conditions
Hai Tao,Mehrdad Ghahremani,Faraedoon Waly Ahmed,Wang Jing,Muhammad Shahzad Nazir,Kentaro Ohshima +5 more
TL;DR: In this paper, an adaptive neural-fuzzy inference system (ANFIS) is used to find the maximum power point (MPP) in solar systems among various methods.
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Modified honey badger algorithm based global MPPT for triple-junction solar photovoltaic system under partial shading condition and global optimization
TL;DR: In this article , an efficient local search called Dimensional Learning Hunting (DLH) is injected to the Honey Badger Algorithm (HBA) to tackle these drawbacks, the proposed method named mHBA.
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