A novel global MPPT technique using improved PS-FW algorithm for PV system under partial shading conditions

2021 ◽  
Vol 246 ◽  
pp. 114639
Author(s):  
Lucas Gao King Chai ◽  
Lenin Gopal ◽  
Filbert H. Juwono ◽  
Choo W.R. Chiong ◽  
Huo-Chong Ling ◽  
...  
Keyword(s):  
Pv System ◽  
Partial Shading ◽  
Global Mppt

scholarly journals Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4971
Author(s):  
Hegazy Rezk ◽  
Ahmed Fathy
Keyword(s):  
Optimization Algorithm ◽  
Triple Junction ◽  
Maximum Power ◽  
Global Maximum ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Power Point ◽  
Global Mppt ◽  
Stochastic Fractal Search

A significant growth in PV (photovoltaic) system installations have been observed during the last decade. The PV array has a nonlinear output characteristic because of weather intermittency. Partial shading is an environmental phenomenon that causes multiple peaks in the power curve and has a negative effect on the efficiency of the conventional maximum power point tracking (MPPT) methods. This tends to have a substantial effect on the overall performance of the PV system. Therefore, to enhance the performance of the PV system under shading conditions, the global MPPT technique is mandatory to force the PV system to operate close to the global maximum. In this paper, for the first time, a stochastic fractal search (SFS) optimization algorithm is applied to solve the dilemma of tracking the global power of PV system based triple-junction solar cells under shading conditions. SFS has been nominated because it can converge to the best solution at a fast rate. Moreover, balance between exploration and exploitation phases is one of its main advantages. Therefore, the SFS algorithm has been selected to extract the global maximum power point (MPP) under partial shading conditions. To prove the superiority of the proposed global MPPT–SFS based tracker, several shading scenarios have been considered. The idea of changing the shading scenario is to change the position of the global MPP. The obtained results are compared with common optimizers: Antlion Optimizer (ALO), Cuckoo Search (CS), Flower Pollination Algorithm (FPA), Firefly-Algorithm (FA), Invasive-Weed-Optimization (IWO), JAYA and Gravitational Search Algorithm (GSA). The results of comparison confirmed the effectiveness and robustness of the proposed global MPPT–SFS based tracker over ALO, CS, FPA, FA, IWO, JAYA, and GSA.

scholarly journals Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions

2020 ◽  
Vol 10 (2) ◽  
pp. 700 ◽  
Author(s):  
Christos Kalogerakis ◽  
Eftichis Koutroulis ◽  
Michail G. Lagoudakis
Keyword(s):  
Machine Learning ◽  
Search Time ◽  
Convergence Time ◽  
Global Maximum ◽  
Pv System ◽  
Partial Shading ◽  
Pv Systems ◽  
Pv Array ◽  
Time Required ◽  
Global Mppt

A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates under partial shading conditions. This paper presents a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of the operational characteristics of the PV modules comprising the PV system, or the PV array structure. Additionally, due to its inherent learning capability, it is capable of detecting the GMPP in significantly fewer search steps and, therefore, it is suitable for employment in PV applications, where the shading pattern may change quickly (e.g., wearable PV systems, building-integrated PV systems etc.). The numerical results presented in the paper demonstrate that the time required for detecting the global MPP, when unknown partial shading patterns are applied, is reduced by 80.5%–98.3% by executing the proposed Q-learning-based GMPPT algorithm, compared to the convergence time required by a GMPPT process based on the particle swarm optimization (PSO) algorithm.

Comprehensive improvement of artificial fish swarm algorithm for global MPPT in PV system under partial shading conditions

2017 ◽  
Vol 40 (7) ◽  
pp. 2178-2199 ◽  
Author(s):  
Mingxuan Mao ◽  
Qichang Duan ◽  
Pan Duan ◽  
Bei Hu
Keyword(s):  
Pso Algorithm ◽  
Learning Ability ◽  
Pv System ◽  
Partial Shading ◽  
Artificial Fish Swarm Algorithm ◽  
Pv Systems ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Power Point ◽  
Global Mppt

Due to the non-linear characteristics I–V of the photovoltaic (PV) curve, the tracking of the maximum power point (MPP) under partial shading (PS) conditions can sometimes be a challenging task. This paper presents a modified artificial fish swarm algorithm (AFSA) for MPP tracking (MPPT) in PV modules under PS. In this algorithm, the AFSA optimized by particle swarm optimization (PSO) algorithm with extended memory (PSOEM-FSA) is improved by hybridizing it with adaptive visual and step, and the resulting algorithm is a comprehensive improvement on the AFSA (abbreviated as CIAFSA). Combining the searching capabilities of the PSOEM-FSA and the self-learning ability of adaptive visual and step for AFSA, CIAFSA is developed. To validate the effectiveness of this novel MPPT technique, the PV system along with the proposed MPPT algorithm is simulated using the Matlab/Simulink Simscape toolbox. Results show that the proposed approach is more effective in MPPT in PV systems under PS conditions when compared with other methods in searching precision.

scholarly journals Reconfigurable Distributed Power Electronics Technique for Solar PV Systems

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1121
Author(s):  
Kamran Ali Khan Niazi ◽  
Yongheng Yang ◽  
Tamas Kerekes ◽  
Dezso Sera
Keyword(s):  
Energy Yield ◽  
Solar Pv ◽  
Charge Redistribution ◽  
Pv System ◽  
Partial Shading ◽  
Pv Systems ◽  
Simulation Based ◽  
In Series ◽  
A Cell ◽  
Power Balancing

A reconfiguration technique using a switched-capacitor (SC)-based voltage equalizer differential power processing (DPP) concept is proposed in this paper for photovoltaic (PV) systems at a cell/subpanel/panel-level. The proposed active diffusion charge redistribution (ADCR) architecture increases the energy yield during mismatch and adds a voltage boosting capability to the PV system under no mismatch by connected the available PV cells/panels in series. The technique performs a reconfiguration by measuring the PV cell/panel voltages and their irradiances. The power balancing is achieved by charge redistribution through SC under mismatch conditions, e.g., partial shading. Moreover, PV cells/panels remain in series under no mismatch. Overall, this paper analyzes, simulates, and evaluates the effectiveness of the proposed DPP architecture through a simulation-based model prepared in PSIM. Additionally, the effectiveness is also demonstrated by comparing it with existing conventional DPP and traditional bypass diode architecture.

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 An Optimized PV Control System Based on the Emperor Penguin Optimizer

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 751
Author(s):  
Mariam A. Sameh ◽  
Mostafa I. Marei ◽  
M. A. Badr ◽  
Mahmoud A. Attia
Keyword(s):  
Duty Cycle ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Boost Converter ◽  
Emperor Penguin ◽  
Pv System ◽  
Partial Shading ◽  
Pv Systems ◽  
Point Tracking ◽  
Grid Connected Inverter

During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.

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