Type-2 fuzzy logic control of a photovoltaic sourced two stages converter

Bay, Ömer Faruk; Yatak, Meral Özarslan

doi:10.3233/jifs-17865

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…It has been implemented in three phase inverter to upgrade the performance of the system compared to T1FLC [138]. A design has been proposed by Bay for DC-DC boost converter that enhances the PV output voltage to three phase inverter input using Interval T2FLC (IT2FLC) [139]. It has been shown that IT2FLC utilized converter system produces superior performance that T1FLC based converter control in handling the uncertainties of the system.…”

Section: Flc Based Advanced Controllermentioning

confidence: 99%

Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

et al. 2019

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Fossil fuels and other conventional energy sources used to generate electricity are finite. Therefore, alternative energy sources should be pursued to meet present and future energy demands. The photovoltaic (PV) is a promising renewable energy source, especially for the remote areas. The PV is a DC power source that needs to be converted into usable AC power using an inverter. However, its nonlinearity and output fluctuation pose challenges in the design of PV based inverter. In this paper, a PV inverter controller system with the fundamentals of a fuzzy logic controller (FLC) and its applications and execution are reviewed. The different fuzzy controllers, inverter control algorithms, and switching techniques are studied. The findings indicate that the fuzzy logic controls have been gaining attention in the area of power control engineering, especially in inverter controller design for PV applications and generation. The FLC has a flexible and intelligent design, expedient user interface, easy computation and learning system, and combinations of different control algorithms. The FLC is also verifiable for completeness, redundancy, and consistency. However, finding the boundaries of membership functions and other rules of FLC requires manual tuning, long computation time, and considerable effort. This paper comprehensively reviews the FLC-based inverter control system to minimize PV output fluctuations, which cause inverter issues related to output harmonics, power factor, switching schemes, losses, and system implementation. The inverter system and its control strategy for future PV applications and generation require further research and development. Consequently, this review focuses on many factors and challenges and provides recommendations for designing capable and efficient inverter control systems for converting PV power to usable AC power. All the highlighted insights of this review will hopefully lead to increased efforts toward the development of the advanced inverter control systems for PV applications for AC loads and the utility grid.

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Section: Flc Based Advanced Controllermentioning

confidence: 99%

Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

et al. 2019

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“…Some researchers [20] have used thresholds to group the individuals. However, the fixed threshold results in uneven number of groups in early and late epochs.…”

Section: Structure Of Dgabcmentioning

confidence: 99%

“…Recently, some researchers have extended the type-1 fuzzy set to the type-2 fuzzy set in a fuzzy system for solving robotic control [12,13,14,15], data classification [16], function approximation [17,18], and automatic control [19,20]. The type-2 fuzzy set incorporate uncertainty about the membership function into fuzzy set theory.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, type-2 fuzzy sets generalize type-1 fuzzy sets and more uncertainty can be handled. In [16,17,18,19,20], the experimental results have shown that the type-2 fuzzy set has a better performance than the type-1 fuzzy set for dealing with uncertainties. The output set corresponding to each rule of the type-2 fuzzy system is a type-2 fuzzy set.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Lin

Wang

Cheng

2018

Sensors

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In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

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Ride Comfort-Road Holding Trade-off Improvement of Full Vehicle Active Suspension System by Interval Type-2 Fuzzy Control

Yatak

Şahin

2021

Engineering Science and Technology, an International Journal

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Type-2 fuzzy logic control of a photovoltaic sourced two stages converter

Cited by 3 publications

References 32 publications

Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Ride Comfort-Road Holding Trade-off Improvement of Full Vehicle Active Suspension System by Interval Type-2 Fuzzy Control

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