Adaptive Fuzzy Logic Controller for<scp>MPPT</scp>Control in<scp>PMSG</scp>Wind Turbine Generator

Moutchou, Rania; Abbou, Ahmed; Jabri, Bouazza; Rhaili, Salah E.; Chigane, Khalid

doi:10.1002/9781119893998.ch7

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…While there are various versions of controllers that employ fuzzy logic, the term "fuzzy controller" typically refers to a control system with an internal structure that includes a fuzzy system to govern the controller. One of the frequently mentioned advantages of fuzzy controllers, in comparison to other controller types, is their ability to be designed even in the absence of an exact model of the plant to be controlled, as they are rule-based [6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

et al. 2023

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The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics.

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Section: Introductionmentioning

confidence: 99%

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

et al. 2023

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Wind speed estimation and maximum power point tracking using neuro-fuzzy systems for variable-speed wind generator

Hermassi,

Krim,

Kraiem

et al. 2024

Wind Engineering

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This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems.

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Adaptive Fuzzy Logic Controller forMPPTControl inPMSGWind Turbine Generator

Cited by 2 publications

References 17 publications

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

Wind speed estimation and maximum power point tracking using neuro-fuzzy systems for variable-speed wind generator

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