Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

Ammar, Hossam Hassan; Azar, Ahmad Taher; Shalaby, Raafat; Mahmoud, Mohamed I.

doi:10.1155/2019/7687891

Cited by 71 publications

(21 citation statements)

References 22 publications

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“…The widespread use of fossil fuels is an undeniable fact in modern industrial life and today, with the reduction of fossil fuel resources and their harm to the environment, the need for renewable energy sources has increased. Meanwhile, the solar energy which is available in abundance is free of pollution and it is one of the options that has received a lot of attention [4][5][6]. PV power generation relishes many paybacks such as pollution-free, maintenance-free, limitlessness and noiselessness.…”

Section: Introductionmentioning

confidence: 99%

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

et al. 2022

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The performance of a photovoltaic (PV) array depends on temperature, radiation, shading and load size. Conventional maximum power point tracking (MPPT) methods have acceptable efficiencies under uniform conditions (irradiance = 1000 W/m 2 and temprature = 25 °C), but in dynamic weather conditions, load changes, and also in partial shading conditions due to the presence of several local maximum power points (MPP) in the P-V characteristic, the conventional tracking method does not work well in finding the main MPP. To extract maximum power in all conditions, many algorithms have been proposed, all of which have limitations in terms of convergence speed, output power ripple and efficiency. This research proposes an optimized Fuzzy Logic Controller (FLC) based on the Cuckoo Optimization Algorithm (COA) for MPPT under uniform conditions, dynamic weather conditions, partial shading and under load changes. Finally, the research compared the simulation results with four other popular methods. According to the simulation observations and the result, COA-FLC overcomes the mentioned limitations such as low convergence speed, output power ripple and low tracking efficiency in all conditions. Simulations are performed with MATLAB / Simulink software.

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Section: Introductionmentioning

confidence: 99%

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

et al. 2022

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“…A buck-boost converter connects the PV unit's configurable sources and solar photovoltaic profile virtualization software configuration to the DC bus. Using the maximum power point tracking (MPPT) method, the buck-boost converter extracts the output power of the PV unit [18]. Two converters, DC/DC buck-boost converters, provide electron flow between its ESSs as well as the DC link.…”

Section: Methodsmentioning

confidence: 99%

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

Sahoo

Amirthalakshmi²,

Ramesh³

et al. 2022

International Journal of Photoenergy

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The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Li-ion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

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“…For the best group , teacher phase is calculated using Equations ( 6)-( 8) and (10). After that student phase is conducted using Equations ( 11) and (12).…”

Section: Fundingmentioning

confidence: 99%

“…Voltage, current, and duty cycle are utilized, along with a mathematical model of PV cells, to devise fundamental techniques, and are utilized to predict optimized operating points on the P-V curve. In open circuit voltage (OCV), V OC is used similarly, and I SC is utilized to model MPPT control [10]. In this model mathematical approximations are utilized to predict the operating point, and no real-time monitoring of output power is involved, so the accuracy cannot be reliable.…”

Section: Introductionmentioning

confidence: 99%

Group Teaching Optimization Algorithm Based MPPT Control of PV Systems under Partial Shading and Complex Partial Shading

et al. 2020

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The most cost-effective electrical energy is produced by photovoltaic (PV) systems, and with the smallest carbon footprint, making it a sustainable renewable energy. They provide an excellent alternative to the existing fossil fuel-based energy systems, while providing 4% of global electricity demand. PV system efficiency is significantly reduced by the intrinsic non-linear model, maximum power point (MPP), and partial shading (PS) effects. These two problems cause major power loss. To devise the maximum power point tracking (MPPT) control of the PV system, a novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading (CPS) conditions. Four case studies were employed that included fast-changing irradiance, PS, and CPS to test the robustness of the proposed MPPT technique. The performance of the GTOA was compared with the latest bio-inspired techniques, i.e., dragon fly optimization (DFO), cuckoo search (CS), particle swarm optimization (PSO), particle swarm optimization gravitational search (PSOGS), and conventional perturb and observe (P&O). The GTOA tracked global MPP with the highest 99.9% efficiency, while maintaining the magnitude of the oscillation <0.5 W at global maxima (GM). Moreover, 13–35% faster tracking times, and 54% settling times were achieved, compared to existing techniques. Statistical analysis was carried out to validate the robustness and effectiveness of the GTOA. Comprehensive analytical and statistical analysis solidified the superior performance of the proposed GTOA based MPPT technique.

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Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

Cited by 71 publications

References 22 publications

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

Group Teaching Optimization Algorithm Based MPPT Control of PV Systems under Partial Shading and Complex Partial Shading

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