Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

Chavero-Navarrete, Ernesto; Trejo-Perea, Mario; Jáuregui-Correa, Juan Carlos; Carrillo-Serrano, Roberto V.; Ríos-Moreno, José Gabriel

doi:10.3390/app9122469

Cited by 36 publications

(14 citation statements)

References 88 publications

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“…The importance of using genetic algorithms as a solution to the Vapnik model function (Equation ( 1)) is to find the most optimal point to be able to obtain better prediction results. Intelligent search algorithms (SOA) are techniques for searching and exchanging information between the individuals of a population, which will be the ones that can solve a non-linear optimization problem [65]. SOA models are frequently used in forecasting methods where it is quite crucial to determine weights coefficients to reduce the error [66].…”

Section: Search Optimization Algorithmsmentioning

confidence: 99%

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

et al. 2021

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The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To obtain a better accuracy of prediction data, search optimization algorithms (SOA) such as genetic algorithms (GA) and the particle swarm optimization algorithm (PSO) were used to optimize the prediction accuracy by searching the model parameters. This article presents a review of different hybrid SVM models with SOA applied to obtain the best parameters to reduce the prediction error of solar radiation using meteorological variables. Research articles from the last 5 years on solar radiation prediction using SVM models and hybrid SMV optimized models with SOA were studied. The results show that SVM with GA presents a better performance than the classical SVM models using the Radial basis kernel function for prediction parameters.

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Section: Search Optimization Algorithmsmentioning

confidence: 99%

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

et al. 2021

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“…In [4], it focuses on the PMSG itself and the performances of generator under different wind speeds are analyzed. In [5], an expert system is applied to Maximum Power Point Tracking(MPPT) to improve the efficiency of PMSG power generation. In [6], it Researches a small PMSG power generation system combined with energy storage technology to supply household power load.…”

Section: Introductionmentioning

confidence: 99%

Wind Power System for Grid-connection Based on PMSG and PI Controlling under Variable Wind Speed

Liu

2022

J. Phys.: Conf. Ser.

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Wind power is an important part of sustainable energy development and plays a pivotal role in the background of the ‘Dual Carbon’ goal. In this paper, a wind power grid-connected model based on Permanent Magnetic Synchronous Generator(PMSG) is proposed. The pitch angle of the wind turbine and the waveform of the grid-side currents are controlled by the PI controller in order to realize the tracking of the power frequency and phase of the grid voltages, and eventually achieve the purpose of grid-connection. By utilizing MATLAB/Simulink, the simulation results reveal that the final output currents’ frequency and phase meet the requirements of grid-connection, and the Total Harmonic Distortion(THD) of the current waveform is less than 2%, which represents a high power quality. It can provide reference for efficient and convenient grid-connection of wind power generation.

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“…To conclude, Ref. [39] presents an exhaustive revision of fuzzy logic control applied to wind turbines. Finally, the main alternative to fuzzy logic control is the use of machine learning, either to anticipate or predict the wind behavior [30,[40][41][42][43][44], to assist in higher-level control [45] or to implement a controller [46].…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Controllers for Wind Turbines on Semi-Submersible Offshore Platforms: Fuzzy Supervisor Control

et al. 2021

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The use of sea wind energy is restricted by the limited availability of suitable sites in shallow waters. To overcome this challenge, wind turbines located on offshore semi-submersible platforms appear as a valuable option, as they also allow the exploitation of other resources like wave energy or aquaculture. Nevertheless, the literature addressing this kind of design is scarce, and the interactions of the wind turbine and the platform movements increase the complexity of the control system with respect to the wind turbines with fixed foundations. Within this context, fuzzy control is a promising alternative to deal with these issues. However, while fuzzy controllers can be an alternative to substitute conventional PI control, the latter is a well-known, robust choice for operators. In this sense, fuzzy controllers can be designed to work in collaboration with PI controllers to ease their adoption. To this end, this paper addresses those gaps in the literature by presenting a methodology, its application to enhance controllers for large-scale wind turbines in semi-submersible offshore platforms and the results attained. The methodology is based on the implementation of an integrated simulation tool, together with the definition of three indexes that describe the performance of the control system in the overall platform behaviour regarding key aspects of its exploitation. Using it, an Anti-Wind-Up algorithm was designed to improve the behaviour of the conventional controller and is presented and evaluated along a fuzzy supervisor controller. In this kind of configuration, the fuzzy controller modifies the values of the PI controller. Finally, a comparison of the performance using the reference PI and the improved PI, in both cases together with a fuzzy supervisor controller modifying their values, is presented and discussed, contributing to extend the state of the art of controllers for large-scale wind turbines on offshore semi-submersible platforms.

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Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

Cited by 36 publications

References 88 publications

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

Wind Power System for Grid-connection Based on PMSG and PI Controlling under Variable Wind Speed

Improving the Performance of Controllers for Wind Turbines on Semi-Submersible Offshore Platforms: Fuzzy Supervisor Control

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