Fuzzy-Logic-Based Comparative Analysis of Different Maximum Power Point Tracking Controllers for Hybrid Renewal Energy Systems

Khan, Mohammad Junaid; Mathew, Lini; Alotaibi, Majed A.; Malik, Hasmat; Nassar, Mohamed

doi:10.3390/math10030529

Cited by 15 publications

(5 citation statements)

References 33 publications

(58 reference statements)

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“…The hybrid methods proposed by [42,45,46], which combine P&O and FL-based techniques, can adapt to changes in wind speed and direction, resulting in improved tracking accuracy and energy conversion efficiency. Similarly, hybrid MPPT methods that combine P&O and neural network-based techniques yielded a hybrid system that successfully tracked the System MPPT [47].…”

Section: Hybrid Methods (Traditional and Intelligent-based Mppt)mentioning

confidence: 99%

“…Additionally, 110 W more power was generated with the proposed controller than with the P&O method and 60 W more than the FLC controller [45]. Moreover, 36.38% of energy yield was recorded with the new controller in [46]. [42,45,46] P&O+ANN By integrating ANN into the traditional P&O algorithm, an increase in accuracy was achieved.…”

Section: Pando+flcmentioning

confidence: 99%

“…Moreover, 36.38% of energy yield was recorded with the new controller in [46]. [42,45,46] P&O+ANN By integrating ANN into the traditional P&O algorithm, an increase in accuracy was achieved.…”

Section: Pando+flcmentioning

confidence: 99%

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Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

Umar,

Alkawsi,

Jailani

et al. 2023

Processes

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As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques comprising two different maximum power point tracking (MPPT) algorithms were further proposed to eliminate the shortages. Furtherly, Artificial Intelligence (AI)-based MPPT algorithms were proposed for the WEHS as either standalone or integrated with the traditional MPPT methods. Therefore, this research focused on the review of the AI-based MPPT and their performances as applied to WEHS. Traditional MPPT methods that are studied in the previous articles were discussed briefly. In addition, AI-based MPPT and different hybrid methods were also discussed in detail. Our study highlights the effectiveness of AI-based MPPT techniques in WEHS using an artificial neural network (ANN), fuzzy logic controller (FLC), and particle swarm optimization (PSO). These techniques were applied either as standalone methods or in various hybrid combinations, resulting in a significant increase in the system’s power extraction performance. Our findings suggest that utilizing AI-based MPPT techniques can improve the efficiency and overall performance of WEHS, providing a promising solution for enhancing renewable energy systems.

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Section: Hybrid Methods (Traditional and Intelligent-based Mppt)mentioning

confidence: 99%

Section: Pando+flcmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

Umar,

Alkawsi,

Jailani

et al. 2023

Processes

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“…Newer techniques have also been studied for MPPT algorithms. Fuzzy logic controllers (FLC) [29] based on artificial neural networks (ANN) can be found in the literature. Al-Giziz et al [30] compared FLC and classical methods, showing that FLC achieves faster response and greater stability at steady-state compared with P&O and InC methods.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

et al. 2023

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Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained.

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“…Intelligent control systems such as fuzzy logic control (FLC) [20], neural network (NN) [21], and the hybrid of the two as an adaptive neuro-fuzzy inference system (AN-FIS) [22] were presented to improve the MPPT techniques. The ANFIS algorithm can convert linguistics into numerical values through membership functions and best alternatives, but this process is time-consuming, and require sophisticated hardware and complicated programming.…”

Section: Introductionmentioning

confidence: 99%

Direct Power Control for Three-Level Multifunctional Voltage Source Inverter of PV Systems Using a Simplified Super-Twisting Algorithm

et al. 2023

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This study proposes a simplified super-twisting algorithm (SSTA) control strategy for improving the power quality of grid-connected photovoltaic (PV) power systems. Some quality issues are considered in this study including the power factor, reducing the total harmonic distortion (THD) of current, compensating the reactive power, and injecting at the same time the energy supplied by the PV system into the grid considering non-linear load. This improvement is achieved by two topologies; controlling both the boost DC–DC converter and the DC–AC inverter that links the PV system to the grid. The DC–DC converter is controlled using proportional-integral (PI) and SSTA to maximize the power generated from the PV panel regardless of its normal and abnormal conditions, while the DC–AC inverter is employed to direct power control strategy with modified space vector modulation using the phase-locked loop (PLL) technique of a three-level neutral-point-clamped (NPC) inverter based on the proposed strategies (PI and SSTA). In addition, a shunt active power filter (SAPF) is used to connect the PV system to the AC grid and feed a non-linear load. To validate the simulation results presented in this paper using Matlab software, a comparative study between the PI controller and the SSTA is presented. The results show the effectiveness and moderation of the suggested SSTA technique in terms of feasibility, tracking performance, less power ripple, dynamic response, THD value, overshoot, steady-state error, and robustness under varying irradiation, temperature, and non-linear conditions.

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Fuzzy-Logic-Based Comparative Analysis of Different Maximum Power Point Tracking Controllers for Hybrid Renewal Energy Systems

Cited by 15 publications

References 33 publications

Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Direct Power Control for Three-Level Multifunctional Voltage Source Inverter of PV Systems Using a Simplified Super-Twisting Algorithm

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