The application of soft computing techniques to improve the performance of maximum power point tracker for PV system during partial shading

2014 ◽  
Author(s):  
Z. Salam ◽  
J. Ahmed
Keyword(s):  
Soft Computing ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Maximum Power Point Tracker ◽  
Soft Computing Techniques ◽  
Power Point

scholarly journals A Novel Ten Check Maximum Power Point Tracking Algorithm for a Standalone Solar Photovoltaic System

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 327 ◽  
Author(s):  
Muhammad Afzal Awan ◽  
Tahir Mahmood
Keyword(s):  
Soft Computing ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Flower Pollination Algorithm ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Computing Technique ◽  
Soft Computing Techniques ◽  
Power Point

Optimal energy extraction under partial shading conditions from a photovoltaic (PV) array is particularly challenging. Conventional techniques fail to achieve the global maximum power point (GMPP) under such conditions, while soft computing techniques have provided better results. The main contribution of this paper is to devise an algorithm to track the GMPP accurately and efficiently. For this purpose, a ten check (TC) algorithm was proposed. The effectiveness of this algorithm was tested with different shading patterns. Results were compared with the top conventional algorithm perturb and observe (P&O) and the best soft computing technique flower pollination algorithm (FPA). It was found that the proposed algorithm outperformed them. Analysis demonstrated that the devised algorithm achieved the GMPP efficiently and accurately as compared to the P&O and the FPA algorithms. Simulations were performed in MATLAB/Simulink.

scholarly journals Simulations and dSPACE Real-Time Implementation of Photovoltaic Global Maximum Power Extraction under Partial Shading

2020 ◽  
Vol 12 (9) ◽  
pp. 3652
Author(s):  
Fahd A. Alturki ◽  
Abdullrahman A. Al-Shamma’a ◽  
Hassan M. H. Farh
Keyword(s):  
Cuckoo Search ◽  
Maximum Power ◽  
Global Maximum ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Power Extraction ◽  
Maximum Power Point Tracker ◽  
Power Point ◽  
Global Peak

Under partial shading conditions (PSCs), solar photovoltaic (PV) energy systems generate multiple peaks; one global peak (GP) and several local peaks (LPs). Thus, tracking the GP of the PV systems under PSCs is necessary to enhance the system reliability and efficiency. Conventional maximum power point tracker (MPPT) algorithms are capable of tracking the unique peak under uniform conditions but they fail to track the GP under PSCs. To the best of our knowledge, this paper represents the first study that introduces a comprehensive comparison of three efficient maximum power point tracker (MPPT) algorithms that are used to extract the GP of the PV system under both uniform and PSCs. These MPPT techniques include two metaheuristic techniques, which are cuckoo search optimization (CSO) and particle swarm optimization (PSO) techniques in addition to one conventional MPPT; perturb and observe (P&O). Although the simulation and dSPACE-based experimental results demonstrated the superiority of CSO and PSO in tracking the GP, CSO requires less tracking time and thus provides a higher efficiency than the PSO. In addition, P&O can be used to follow the first peak, regardless if it is a local peak or global peak with notable oscillation.

scholarly journals Critical evaluation of soft computing methods for maximum power point tracking algorithms of photovoltaic systems

2019 ◽  
Vol 10 (1) ◽  
pp. 548 ◽  
Author(s):  
Norazlan Hashim ◽  
Zainal Salam
Keyword(s):  
Soft Computing ◽  
Maximum Power Point Tracking ◽  
Critical Evaluation ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

<span lang="EN-US">With the proliferation of numerous soft computing (SC)–based maximum power point tracking (MPPT) algorithms for photovoltaic (PV) systems, determining which algorithm performs better than others is becoming increasingly difficult. This is primarily due to the absence of standardized methods to benchmark their performances using consistent and systematic procedures. Moreover, the module technology, power ratings, and environmental conditions reported by numerous publications all differ. Based on these concerns, this paper presents a critical evaluation of the five most important and recent SC-based MPPTs, namely, genetic algorithm (GA), cuckoo search (CS), particle swarm optimization (PSO), differential evolution (DE), and evolutionary programming (EP). To perform a fair comparison, the initialization, selection, and stopping criteria for all methods are fixed in similar conditions. Thus, the performance is determined by its respective reproduction process. Simulation tests are performed using the MATLAB/SIMULINK environment. The performance of each algorithm is compared and evaluated based on its speed of convergence, accuracy, complexity, and success rate. The results indicate that EP appears to be the most promising and encouraging SC algorithm to be used in MPPT for a PV system under the multimodal partial shading condition.</span>

scholarly journals Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm

2021 ◽  
Vol 13 (5) ◽  
pp. 2656
Author(s):  
Ahmed G. Abo-Khalil ◽  
Walied Alharbi ◽  
Abdel-Rahman Al-Qawasmi ◽  
Mohammad Alobaid ◽  
Ibrahim M. Alarifi
Keyword(s):  
Firefly Algorithm ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Opposition Based Learning ◽  
Power Point Tracking ◽  
Convergence Process ◽  
Power Point

This work presents an alternative to the conventional photovoltaic maximum power point tracking (MPPT) methods, by using an opposition-based learning firefly algorithm (OFA) that improves the performance of the Photovoltaic (PV) system both in the uniform irradiance changes and in partial shading conditions. The firefly algorithm is based on fireflies’ search for food, according to which individuals emit progressively more intense glows as they approach the objective, attracting the other fireflies. Therefore, the simulation of this behavior can be conducted by solving the objective function that is directly proportional to the distance from the desired result. To implement this algorithm in case of partial shading conditions, it was necessary to adjust the Firefly Algorithm (FA) parameters to fit the MPPT application. These parameters have been extensively tested, converging satisfactorily and guaranteeing to extract the global maximum power point (GMPP) in the cases of normal and partial shading conditions analyzed. The precise adjustment of the coefficients was made possible by visualizing the movement of the particles during the convergence process, while opposition-based learning (OBL) was used with FA to accelerate the convergence process by allowing the particle to move in the opposite direction. The proposed algorithm was simulated in the closest possible way to authentic operating conditions, and variable irradiance and partial shading conditions were implemented experimentally for a 60 [W] PV system. A two-stage PV grid-connected system was designed and deployed to validate the proposed algorithm. In addition, a comparison between the performance of the Perturbation and Observation (P&O) method and the proposed method was carried out to prove the effectiveness of this method over the conventional methods in tracking the GMPP.

scholarly journals A Modification of the Perturb and Observe Method to Improve the Energy Harvesting of PV Systems under Partial Shading Conditions

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2521
Author(s):  
Alfredo Gil-Velasco ◽  
Carlos Aguilar-Castillo
Keyword(s):  
Maximum Power ◽  
Global Maximum ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Computational Resources

There are multiples conditions that lead to partial shading conditions (PSC) in photovoltaic systems (PV). Under these conditions, the harvested energy decreases in the PV system. The maximum power point tracking (MPPT) controller aims to harvest the greatest amount of energy even under partial shading conditions. The simplest available MPPT algorithms fail on PSC, whereas the complex ones are effective but require high computational resources and experience in this type of systems. This paper presents a new MPPT algorithm that is simple but effective in tracking the global maximum power point even in PSC. The simulation and experimental results show excellent performance of the proposed algorithm. Additionally, a comparison with a previously proposed algorithm is presented. The comparison shows that the proposal in this paper is faster in tracking the maximum power point than complex algorithms.

scholarly journals A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 483
Author(s):  
Novie Ayub Windarko ◽  
Muhammad Nizar Habibi ◽  
Bambang Sumantri ◽  
Eka Prasetyono ◽  
Moh. Zaenal Efendi ◽  
...  
Keyword(s):  
Power Generation ◽  
Duty Cycle ◽  
Operating Conditions ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Photovoltaic Panel ◽  
Maximum Power Point Tracker ◽  
Photovoltaic Power ◽  
Power Point

During its operation, a photovoltaic system may encounter many practical issues such as receiving uniform or non-uniform irradiance caused mainly by partial shading. Under uniform irradiance a photovoltaic panel has a single maximum power point. Conversely under non-uniform irradiance, a photovoltaic panel has several local maximum power points and a single global maximum power point. To maximize energy production, a maximum power point tracker algorithm is commonly implemented to achieve the maximum power operating point of the photovoltaic panel. However, the performance of the algorithm will depend on operating conditions such as variation in irradiance. Presently, most of existing maximum power point tracker algorithms work only in a single condition: either uniform or non-uniform irradiance. This paper proposes a new maximum power point tracker algorithm for photovoltaic power generation that is designed to work under uniform and partial shading irradiance conditions. Additionally, the proposed maximum power point tracker algorithm aims to provide: (1) a simple math algorithm to reduce computational load, (2) fast tracking by evaluating progress for every single executed duty cycle, (3) without random steps to prevent jumping duty cycle, and (4) smooth variable steps to increase accuracy. The performances of the proposed algorithm are evaluated by three conditions of uniform and partial shading irradiance where a targeted maximum power point is located: (1) far from, (2) near, and (3) laid between initial positions of particles. The simulation shows that the proposed algorithm successfully tracks the maximum power point by resulting in similar power values in those three conditions. The proposed algorithm could handle the partial shading condition by avoiding the local maxima power point and finding the global maxima power point. Comparisons of the proposed algorithm and other well-known algorithms such as differential evolution, firefly, particle swarm optimization, and grey wolf optimization are provided to show the superiority of the proposed algorithm. The results show the proposed algorithm has better performance by providing faster tracking, faster settling time, higher accuracy, minimum oscillation and jumping duty cycle, and higher energy harvesting.

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