scholarly journals Modified Levy Flight Optimization for a Maximum Power Point Tracking Algorithm under Partial Shading

2021 ◽  
Vol 11 (3) ◽  
pp. 992
Author(s):  
Chanuri Charin ◽  
Dahaman Ishak ◽  
Muhammad Ammirrul Atiqi Mohd Zainuri ◽  
Baharuddin Ismail
Keyword(s):  
High Efficiency ◽  
Energy System ◽  
Fast Response ◽  
Lévy Flight ◽  
Partial Shading ◽  
Levy Flight ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Steady State Oscillation ◽  
Power Point

This paper presents a novel modified Levy flight optimization for a photovoltaic PV solar energy system. Conventionally, the Perturb and Observe (P&O) algorithm has been widely deployed in most applications due to its simplicity and ease of implementation. However, P&O suffers from steady-state oscillation and stability, besides its failure in tracking the optimum power under partial shading conditions and fast irradiance changes. Therefore, a modified Levy flight optimization is proposed by incorporating a global search of beta parameters, which can significantly improve the tracking capability in local and global searches compared to the conventional methods. The proposed modified Levy flight optimization is verified with simulations and experiments under uniform, non-uniform, and dynamic conditions. All results prove the advantages of the proposed modified Levy flight optimization in extracting the optimal power with a fast response and high efficiency from the PV arrays.

scholarly journals A maximum power point tracking based on levy flight optimization

2020 ◽  
Vol 11 (3) ◽  
pp. 1499
Author(s):  
C. Charin ◽  
Dahaman Ishak ◽  
Muhammad Ammirrul Atiqi Mohd Zainuri
Keyword(s):  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Global Maximum ◽  
Lévy Flight ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Levy Flight ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

This paper proposes a Levy flight global maximum power point tracking for solar photovoltaic (PV) system under partial shading conditions. The proposed method comes with merits such as simplicity, fast response and free of oscillation. This algorithm uses random search over the exploration space and compares the previous and current states to obtain the best solution. For evaluation and comparative analysis, performance of the proposed method is also measured against Perturb and Observe (P&O) and Particle Swarm Optimization (PSO). All three algorithms are simulated in MATLAB/Simulink environment. Simulation results are satisfactory over the conducted tests under uniform and non-uniform irradiance. The proposed algorithm is able to track global maximum power point (GMPP) under partial shading conditions with fast tracking time and zero ripple at steady-state.

A Comparative Study on Maximum Power Point Tracking Techniques of Photovoltaic Systems

2018 ◽  
Vol 7 (1) ◽  
pp. 66-85 ◽  
Author(s):  
Afef Badis ◽  
Mohamed Habib Boujmil ◽  
Mohamed Nejib Mansouri
Keyword(s):  
High Efficiency ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Conventional Methods ◽  
Power Point

This article concerns maximizing the energy reproduced from the photovoltaic (PV) system, ensured by using an efficient Maximum Power Point Tracking (MPPT) process. The process should be fast, rigorous and simple for implementation because the PV characteristics are extremely affected by fast changing conditions and Partial Shading (PS). PV systems are popularly known to have many peaks (one Global Peak (GP) and several local peaks). Therefore, the MPPT algorithm should be able to accurately detect the unique GP as the maximum power point (MPP), and avoid any other peak to mitigate the effect of (PS). Usually, with no shading, nearly all the conventional methods can easily reach the MPP with high efficiency. Nonetheless, they fail to extract the GP when PS occurs. To overcome this problem, Evolutionary Algorithms (AEs), namely the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are simulated and compared to the conventional methods (Perturb & Observe) under the same software.

Maximum power point tracking and power flow management of hybrid renewable energy system with partial shading capability: A hybrid technique

2020 ◽  
Vol 42 (12) ◽  
pp. 2276-2296
Author(s):  
S Satheesh Kumar ◽  
A Immanuel Selvakumar
Keyword(s):  
Power Flow ◽  
Energy System ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Grey Wolf ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

A grid connected hybrid energy system combining wind turbine (WT) and photovoltaic (PV) array generating system with energy storage system to supply continuous power to the load using hybrid technique is exhibited in this dissertation. The proposed hybrid technique is the joint execution of both the binary chaotic crow search optimizer (BCCSO) with grey wolf optimizer and random forest algorithm (GWORFA) and hence it is named as BCCSO-GWORFA technique. The main aim of the proposal is to optimally track the maximum power point tracking (MPPT) and to maintain the power flow of the grid connected HRES. Here, the BCCSO-based MPPT procedure optimizes the exact duty cycles required for the DC-DC converter of the PV under partial shading conditions and WT system under variable speed conditions based on the voltage and current parameters. On the other hand, the grey wolf optimizer (GWO) learning procedure-based random forest algorithm (RFA) predicts the control signals of the voltage source inverter (VSI) based on the active and reactive power variations available in the load side. To predict the control parameters, the proposed technique considers power balance constraints like RES accessibility, storage element state of charge, and load side power demand. The proposed strategy is implemented in MATLAB/Simulink working platform. The performance of the HRES is assessed by utilizing the comparison analysis with the existing techniques. The comparison results invariably prove the proposed hybrid technique effectiveness and confirm its potential to solve the related issues with efficiency of 99.5%.

scholarly journals An Immune Firefly Algorithm for Tracking the Maximum Power Point of PV Array under Partial Shading Conditions

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3083 ◽  
Author(s):  
Mingrui Zhang ◽  
Zheyang Chen ◽  
Li Wei
Keyword(s):  
Firefly Algorithm ◽  
Maximum Power ◽  
Convergence Time ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Steady State Oscillation ◽  
Power Point

Photovoltaic (PV) string exhibits complex multiple-peak characteristics under various partial shading conditions (PSC). If the maximum power point tracking cannot be achieved quickly and accurately, it will lead to a large amount of energy loss. Therefore, it has become a hot topic to study a reliable maximum power tracking control algorithm to ensure the PV system can still output maximum power under PSC. This paper proposes an immune firefly algorithm (IFA), which utilizes vaccine data-base to shorten the convergence time, eliminates the influence of bad individuals in time by immune replenishment operation, and reduces the steady-state oscillation by the improving iteration formula. The simulations in static and dynamic environments verify that the immune firefly algorithm can track the maximum power point under various partial shading conditions. Compared with conventional firefly algorithm (FA), IFA has faster convergence speed, and can effectively restrain the oscillation of voltage and power.

scholarly journals Hybrid Energy System with MPPT Techniques: A Review

2020 ◽  
Vol 6 (7) ◽  
pp. 20-23
Author(s):  
Suraj Kumar ◽  
Varsha Mehar
Keyword(s):  
Energy System ◽  
Fast Response ◽  
Tracking Accuracy ◽  
Maximum Power Point ◽  
Hybrid Energy ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Design Requirements ◽  
Power Point

The efficient use of energy produced from renewable energies is more important in this scenario. This document provides a detailed overview of the hybrid power system (HPS) with solar and MPPT controls. An autonomous solar system is the best choice for a rural area to provide uninterrupted energy. MPPT (Maximum Power Point Tracking) is generally used in photovoltaic (PV) systems to maximize the output power of photovoltaic panels regardless of climate change. Fast response and high tracking accuracy are two essential design requirements for MPPT control.

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.

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