scholarly journals An Intelligent Maximum Power Point Using a Fuzzy Log Controller under Severe Weather Conditions

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Khaled Bataineh
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Weather Condition ◽  
Weather Conditions ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Pv Panel ◽  
Simulation Results ◽  
Power Point

This study is aimed at providing a comparison between fuzzy systems and convectional P&O for tracking MPP of a PV system. MATLAB/Simulink is used to investigate the response of both algorithms. Several weather conditions are simulated: (i) uniform irradiation, (ii) sudden changing, and (iii) partial shading. Under partial shading on a PV panel, multipeaks appeared in P-V characteristics of the panel. Simulation results showed that a fuzzy controller effectively finds MPP for all weather condition scenarios. Furthermore, simulation results obtained from the FLC are compared with those obtained from the P&O controller. The comparison shows that the fuzzy logic controller exhibits a much better behavior.

scholarly journals Design and Implementation of Modified INC, Conventional INC, and Fuzzy Logic Controllers Applied to a PV System under Variable Weather Conditions

Designs ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 71
Author(s):  
Maroua Bouksaim ◽  
Mohcin Mekhfioui ◽  
Mohamed Nabil Srifi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Weather Conditions ◽  
Energy Conversion Efficiency ◽  
Fast Response ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Simulation Results ◽  
Power Point

Maximum power point tracking (MPPT) algorithms are used in photovoltaic applications to extract the maximum power that the photovoltaic (PV) panel can produce, which depends on two inputs that are: temperature and irradiance. A DC-DC converter is inserted between the photovoltaic panel and the load to obtain the desired voltage level on the load side. In this paper, incremental conductance (INC) algorithm, modified INC, and fuzzy logic controller (FLC) are designed and assessed to improve energy conversion efficiency. These algorithms are applied to the control of boost converter for tracking the maximum power point (MPP). The modified INC offers fast response and good performance in terms of oscillations than conventional INC and FLC. The Matlab/Simulink environment is used to analyze, interpret the simulation results, and show the performances of each algorithm; and Proteus-based Arduino environment is used to implement the three methods in order to compare the Matlab simulation results with measurements acquired during implementation that is similar to real experiment.

scholarly journals An Intelligent Maximum Power Point Tracking Algorithm for Photovoltaic System

2018 ◽  
Vol 7 (4.35) ◽  
pp. 457
Author(s):  
M. I. Iman ◽  
M. F. Roslan ◽  
Pin Jern Ker ◽  
M. A. Hannan
Keyword(s):  
Fuzzy Logic Controller ◽  
Boost Converter ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Resistive Load ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Pv Panel ◽  
Power Point

This work comprehensively demonstrates the performance analysis of Fuzzy Logic Controller (FLC) with Particle Swarm Optimization (PSO) Maximum Power Point Tracker (MPPT) algorithm on a stand-alone Photovoltaic (PV) applications systems. A PV panel, DC-DC Boost converter and resistive load was utilized as PV system. Three different MPPT algorithms were implemented in the converter. The result obtained from the converter was analyzed and compared to find the best algorithm to be used to identify the point in which maximum power can be achieve in a PV system. The objective is to reduce the time taken for the tracking of maximum power point of PV application system and minimize output power oscillation. The simulation was done by using MATLAB/Simulink with DC-DC Boost converter. The result shows that FLC method with PSO has achieved the fastest response time to track MPP and provide minimum oscillation compared to conventional P&O and FLC techniques.

scholarly journals Real-Time Experimental Assessment of a New MPPT Algorithm Based on the Direct Detection of the Short-Circuit Current for a PV System

2021 ◽  
Vol 19 ◽  
pp. 598-603 ◽  
Author(s):  
C.B. Nzoundja Fapi ◽  
◽  
P. Wira ◽  
M. Kamta ◽  
Keyword(s):  
Fuzzy Logic Controller ◽  
Direct Detection ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Maximum Power ◽  
Solar Irradiation ◽  
Pv System ◽  
Maximum Power Point ◽  
Pv Panel ◽  
Power Point

To substantially increase the efficiency of photovoltaic (PV) systems, it is important that the Maximum Power Point Tracking (MPPT) system has an output close to 100%.This process is handled by MPPT algorithms such as Fractional Open-Circuit Voltage (FOCV), Perturb and Observe (P&O), Fractional Short-Circuit Current (FSCC), Incremental Conductance (INC), Fuzzy Logic Controller (FLC) and Neural Network (NN) controllers. The FSCC algorithm is simple to be implemented and uses only one current sensor. This method is based on the unique existence of the linear approximation between the Maximum Power Point (MPP) current and the short-circuit current in standard conditions. The speed of this MPPT optimization technic is fast, however this algorithm needs to short-circuit the PV panel each time in order to obtain the short circuit current. This process leads to energy losses and high oscillations. In order to improve the FSCC algorithm, we propose a method based on the direct detection of the shortcircuit current by simply reading the output current of the PV panel. This value allows directly calculating the short circuit current by incrementing or decrementing the solar irradiation. Experimental results show time response attenuation, little oscillations, power losses reduction and better MPPT accuracy of the enhanced algorithm compared to the conventional FSCC method.

Performance comparison of modified elephant herding optimization tuned MPPT for PV based solar energy systems

Circuit World ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Annapoorani Subramanian ◽  
Jayaparvathy R.
Keyword(s):  
Green Energy ◽  
Operating Conditions ◽  
Maximum Power ◽  
Irradiation Condition ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Content Type ◽  
Simulation Results ◽  
Power Point

Purpose The solar photovoltaic (PV) system is one of the outstanding, clean and green energy options available for electrical power generation. The varying meteorological operating conditions impose various challenges in extracting maximum available power from the solar PV system. The drawbacks of conventional and evolutionary algorithms-based maximum power point tracking (MPPT) approaches are its inability to extract maximum power during partial shading conditions and quickly changing irradiations. Hence, the purpose of this paper is to propose a modified elephant herding optimization (MEHO) based MPPT approach to track global maximum power point (GMPP) proficiently during dynamic and steady state operations within less time. Design/methodology/approach A MEHO-based MPPT approach is proposed in this paper by incorporating Gaussian mutation (GM) in the original elephant herding optimization (EHO) to enhance the optimizing capability of determining the optimal value of DC–DC converter’s duty cycle (D) to operate at GMPP. Findings The effectiveness of the proposed system is compared with EHO based MPPT, Firefly Algorithm (FA) MPPT and particle swarm optimization (PSO) MPPT during uniform irradiation condition (UIC) and partial shading situation (PSS) using simulation results. An experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution. Originality/value With the proposed MEHO MPPT, it has been noted that the settling period is lowered by 3.1 times in comparison of FA MPPT, 1.86 times when compared to PSO based MPPT and 1.29 times when compared to EHO based MPPT with augmented efficiency of 99.27%.

scholarly journals A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions

Resources ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 68 ◽  
Author(s):  
Khaled Bataineh ◽  
Naser Eid
Keyword(s):  
Fuzzy Logic Controller ◽  
Maximum Power Point Tracking ◽  
Weather Condition ◽  
Weather Conditions ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Inference System ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

A hybrid MPPT (maximum power point tracking) controller integrates FLC (fuzzy logic controller) and P&O (Perturbation and Observation) method for MMPT of PV (Photovoltaic) under dynamic weather conditions is proposed. An adaptive neuro-fuzzy inference system is used to optimize parameters and membership functions of FLC. FLC is used to find the region of MPP (maximum power point); then, P&O technique is employed to accurately track the MPP. MATLAB/Simulink models are built to evaluate the performance of the proposed hybrid algorithm. In order to validate the performance of the proposed algorithm, comparisons with standalone FLC and P&O are carried out. The performance of the proposed algorithm is tested against dynamic weather condition. The results showed that the proposed algorithm successfully improve the dynamic and steady state responses of PV under severe dynamic weather condition. More specifically, the proposed approach shows its capability to attain the MPP faster than P&O and provided higher power than the standalone FLC. Finally, the proposed algorithm overcomes the limitations associated with FLC and P&O.

Comparison between PSO and FLC: MPPT for Energy Harvesting of PV System under Partial Shading Condition

2015 ◽  
Vol 785 ◽  
pp. 188-192 ◽  
Author(s):  
Nur Atharah Kamarzaman ◽  
S.S. Ramli ◽  
A.A.A. Samat ◽  
Aimi Idzwan Tajudin
Keyword(s):  
Fuzzy Logic Controller ◽  
Characteristic Curve ◽  
Maximum Power ◽  
Stochastic Algorithm ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Simple Implementation ◽  
Power Point

Conventional Maximum Power Point Tracking (MPPT) controllers are widely used due to simple implementation and show a good performance in tracking Maximum Power Point (MPP) when solar irradiance is uniform. However, when partial shading occurs on the PV array, tracking to MPP becomes complicated as multiple peaks exist on the Power-Voltage (P-V) characteristic curve. Several methods based on stochastic algorithm and artificial intelligence has been developed to track true MPP under partial shading conditions. This paper focuses on the performance of MPPT controller to extract maximum power from PV system under partial shading condition. The selected MPPT algorithms that have been implemented in the PV system include Fuzzy Logic Controller and Particle Swarm Optimization. Results show that both the simulated MPPT controllers are capable of tracking the maximum power.

A Novel Salp Swarm Optimization MPP Tracking Algorithm for the Solar Photovoltaic Systems under Partial Shading Conditions

2019 ◽  
Vol 29 (01) ◽  
pp. 2050017
Author(s):  
S. Krishnan ◽  
K. Sathiyasekar
Keyword(s):  
Tracking Algorithm ◽  
Gray Wolf ◽  
Solar Photovoltaic ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Pv Panel ◽  
Whale Optimization ◽  
Simulation Results ◽  
Power Point

To extract the maximum solar power from the photovoltaic (PV) panel/array with the high conversion efficiency under partial shading condition (PSC), this paper discusses a new and an efficient maximum power point (MPP) tracking algorithm. The proposed algorithm is based on the bio-inspired salp swarm optimization (SSO), and the algorithm forecasts the global MPP (GMPP) with the fast convergence to GMPP and high tracking efficiency. The SSO algorithm thus reduces the computational burden as encountered in whale optimization algorithm (WOA), and gray wolf optimization (GWO) algorithm discussed in the various literatures. The modeling and simulation of the proposed SSO algorithm are done with the help of Matlab/Simulink software to validate the effectiveness to locate the MPP during PSCs. The simulation results prove that the proposed SSO algorithm exhibits a high PV power output with the tracking efficiency of more than 95% at the faster convergence rate to GMPP. The SSO algorithm is experimentally verified on the conventional boost converter under different shading conditions.

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.

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