Maximum power point tracking for grid-connected photovoltaic system using Adaptive Fuzzy Logic Controller

Ali, Majid; Ahmad, Mujtaba; Koondhar, Mohsin Ali; Akram, Muhammad Sarfraz; Verma, Aanchal; Khan, Baseem

doi:10.1016/j.compeleceng.2023.108879

Cited by 16 publications

(8 citation statements)

References 18 publications

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“…However, the complex training process and high computational demand limit its widespread adoption. Fuzzy logic control [18] is another AI-based approach that mimics human decision-making processes by incorporating linguistic variables and fuzzy rules. This technique enables the MPPT controller to make real-time adjustments based on imprecise or uncertain input data, enhancing robustness and reliability in dynamic environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Velpula,

Kona

2024

Preprint

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This paper proposes a novel brain inspired intelligent maximum power point tracking (BIIM) algorithm to enhance the performance of grid-connected solar photovoltaic (PV) system. A computational model for the algorithm is developed and implemented by mimicking the functionalities of amygdala, thalamus, orbitofrontal cortex and sensory cortex, present in the limbic system of the human brain. Conventional maximum power point tracking (MPPT) algorithms, such as perturb and observe (P&O) and incremental conductance (IC), often encounter challenges in achieving efficient and accurate maximum power point (MPP) tracking under varying environmental conditions. These algorithms exhibit slow convergence speed, oscillations around the MPP, and reduced tracking accuracy, leading to power losses and suboptimal photovoltaic system performance. To overcome these limitations, a novel brain-inspired intelligent MPPT (BIIM) algorithm has been proposed and implemented. The proposed BIIM algorithm incorporates the limbic system's ability to process sensory inputs, learn from past experiences, and make rapid decisions to achieve efficient MPPT. The efficacy of the proposed BIIM algorithm is comprehensively evaluated under various critical conditions, such as irradiance variations, temperature fluctuations, and grid faults. To validate the effectiveness of the proposed MPPT approach, comparative analysis was conducted with conventional MPPT algorithms such as P&O, proportional-integral (PI) controlled incremental conductance (IC-PI) and particle swarm optimization (PSO). The simulation results reveals the superior dynamic performance of the BIIM algorithm by reducing the response time, undershoot, along with increased efficiency and robustness.

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Section: Literature Reviewmentioning

confidence: 99%

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Velpula,

Kona

2024

Preprint

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“…FLCs, in the context of smart grids, provide a method to acquire expert knowledge and decision-making heuristics, which makes them highly suitable for optimizing the flow, storage, and distribution of energy. [21][22][23][24][25] The literature extensively examines the adaptive decision-making abilities of fuzzy logic systems. Fuzzy logic facilitates the depiction of inaccurate data by using linguistic variables and rules, so allowing controllers to adapt responsively to fluctuating circumstances.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

Sharma,

Raju,

Dhall

et al. 2024

E3S Web Conf.

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This study presents a novel Fuzzy Logic-Based Energy Management model that aims to optimize the incorporation of renewable energy sources into smart grids. The research used simulated data to evaluate the model’s performance in important metrics, revealing significant improvements in the consumption of renewable energy, stability of the grid, dependability of energy storage, and overall efficiency of the system. The Fuzzy Logic Controller adjusts energy distribution in response to current inputs, leading to a significant 20% improvement in the usage of renewable energy. The capacity to adapt is crucial in dealing with the inherent fluctuation of solar, wind, and biomass sources. The approach greatly improves grid stability, as shown by a 15% decrease in grid frequency variations, highlighting its efficacy in assuring a more regulated and steady electricity supply. Furthermore, the dependability of energy storage systems exhibits a notable 25% enhancement in the state of charge, suggesting optimal cycles of charging and discharging. This increased dependability enhances the stability of the energy supply during times of high demand and variations in the power system. The Fuzzy Logic-Based Energy Management model exhibits a significant 22% improvement in total system efficiency when compared to conventional management systems. This indicator encompasses the model’s combined effect on the usage of renewable energy, the stability of the power grid, and the optimization of energy storage. Comparative analyses conducted against traditional control strategies, such as proportional-integralderivative controllers, consistently demonstrate the superiority of the fuzzy logic approach. This approach results in a 10% decrease in grid frequency deviations, a 15% enhancement in energy storage state of charge, and a 12% boost in overall system efficiency. The resilience of the Fuzzy Logic Controller is highlighted by sensitivity analysis, since it demonstrates consistent performance even when parameters vary significantly. The model’s practical usefulness and adherence to ethical principles are further confirmed by validation using real-world data from operational smart grid installations. This study provides valuable insights, establishing the Fuzzy Logic-Based Energy Management model as an innovative method for addressing the difficulties associated with integrating renewable energy into smart grids. This model promotes a more sustainable and efficient energy environment for the future.

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“…The simulation results in MATLAB Simulink demonstrate the efficiency of the proposed hybrid system, highlighting its potential advantages over conventional configurations. Ali et al [12] explored on the design and simulation of a power electronic controller for a grid-connected photovoltaic array with maximum power point tracking (MPPT). The project involves the use of a DC boost converter in conjunction with an MPPT controller to enhance the extraction of power from the solar array.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and Simulation of Grid-Tied Three-Phase PV System in Lahore, Pakistan

Akhtar,

Iqbal

2024

EJECE

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This research examines the implementation of grid-tied solar inverters in Lahore’s energy infrastructure, considering the city’s growing energy demands. Utilizing MATLAB/Simulink for modeling solar photovoltaic systems in Lahore’s arid climate, our study focuses on a house in Askari X housing society, Lahore, Pakistan, with a 5.1 kW load. The results demonstrate the expected system variation of a Grid-Tied PV System during grid disruptions, and the model’s consistent performance across varying irradiance conditions suggests potential economic advantages through net metering. These findings offer practical insights into the economic feasibility and dependability of grid-tied solar inverters in Lahore, highlighting their potential to contribute to the city’s energy needs.

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Maximum power point tracking for grid-connected photovoltaic system using Adaptive Fuzzy Logic Controller

Cited by 16 publications

References 18 publications

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

Modeling and Simulation of Grid-Tied Three-Phase PV System in Lahore, Pakistan

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