Wind power is the most reliable and developed renewable energy source over past decades. With the rapid penetration of the wind generators in the power system grid, it is very essential to utilize the maximum available power from the wind and to operate the wind turbine (WT) at its maximal energy conversion output. For this, the wind energy conversion system (WECS) has to track or operate at the maximum power point (MPP). A decent variety of publication report on various maximum power point tracking (MPPT) algorithms for a WECS. However, making a choice on an exact MPPT algorithm for a particular case require sufficient proficiency because each algorithm has its own merits and demerits. For this reason, an appropriate review of those algorithms is essential. However, only a few attempts have been made in this concern. In this paper, different available MPPT algorithms are described for extracting maximum power which are classified according to the power measurement i.e. direct or indirect power controller. Merits, demerits and comprehensive comparison of the different MPPT algorithms also highlighted in the terms of complexity, wind speed requirement, prior training, speed responses, etc. and also the ability to acquire the maximal energy output. This paper serves as a proper reference for future MPPT users in selecting appropriate MPPT algorithm for their requirement.

A review of conventional and advanced MPPT algorithms for wind energy systems

A detailed review of the current state-of-art for wind turbine blade design is presented, including theoretical maximum efficiency, propulsion, practical efficiency, HAWT blade design, and blade loads. The review provides a complete picture of wind turbine blade design and shows the dominance of modern turbines almost exclusive use of horizontal axis rotors. The aerodynamic design principles for a modern wind turbine blade are detailed, including blade plan shape/quantity, aerofoil selection and optimal attack angles. A detailed review of design loads on wind turbine blades is offered, describing aerodynamic, gravitational, centrifugal, gyroscopic and operational conditions.

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Wind turbine blade design

Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network

A Survey of Artificial Neural Network in Wind Energy Systems

The fundamental theory of design and operation of wind turbines is derived based on a first principles approach using conservation of mass and conservation of energy in a wind stream. A detailed derivation of the “Betz Equation” and the “Betz Criterion” or “Betz Limit” is presented, and its subtleties, insights as well as the pitfalls in its derivation and application are discussed. This fundamental equation was first introduced by the German engineer Albert Betz in 1919 and published in his book “Wind Energie und ihre Ausnutzung durch Windmuhlen,” or “Wind Energy and its Extraction through Wind Mills” in 1926. The theory that is developed applies to both horizontal and vertical axis wind turbines. The power coefficient of a wind turbine is defined and is related to the Betz Limit. A description of the optimal rotor tip speed ratio of a wind turbine is also presented. This is compared with a description based on Schmitz whirlpool ratios accounting for the different losses and efficiencies encountered in the operation of wind energy conversion systems. The theoretical and a corrected graph of the different wind turbine operational regimes and configurations, relating the power coefficient to the rotor tip speed ratio are shown. The general common principles underlying wind, hydroelectric and thermal energy conversion are discussed.

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Wind Turbines Theory - The Betz Equation and Optimal Rotor Tip Speed Ratio

Neural networks approaches are becoming useful as an alternate way to classical methods. As a computation and learning paradigm, they are presented as a different modeling approach to solve complicated problems. They have been used to solve complicated practical problems in various areas, such as engineering, medicine, business, manufacturing, military etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, evaluation, classification, and control of complex systems. During the last three decades, artificial neural network have been extensively employed in numerous fields of science and technology. They are not programmed in the conventional procedure but they are trained using data exemplifying the behaviour of a system. This study presents various applications of neural networks used in wind energy systems. The applications of neural networks in wind energy systems could be grouped in three major categories: forecasting and prediction, prediction and control, identification and evaluation. The main purpose of this paper is to present an overview of the neural network applications in wind energy systems. Published literature presented in this study indicate the potential of ANN as a useful tool for wind energy systems. Author strongly believes that this survey will be very much useful to the researchers, scientific engineers working in this area to find out the relevant references and current state of the field.

Artificial neural networks applications in wind energy systems: a review

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model to predict the tip speed ratio (TSR) and the power factor of a wind turbine. This model is based on the parameters for LS-1 and NACA4415 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the TSR and power factor were taken as output variables. After a successful learning and training process, the proposed model produced reasonable mean errors. The results indicate that the errors of ANFIS models in predicting TSR and power factor are less than those of the ANN method.

An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines

Assessment of optimum tip speed ratio in wind turbines using artificial neural networks

The first thing to do in wind turbine blade design is to select tip speed ratio. Generally speaking, the speed ratio depends on the profile type used and the number of blades. Various speed ratios could be chosen for different types of profiles with different number of blades. Therefore, an optimization procedure should be applied to find the best ratio since this directly affects the energy generated from the turbine and in turn the investment made. This study presents a procedure to assess the optimum speed ratios for various profile types used in practice with various numbers of blades. Keywords: Wind turbine, design tip speed ratio, turbine blade, power factor.

https://www.mdpi.com/2297-8747/10/1/147/pdf

Assessment of optimum tip speed ratio of wind turbines

Wind energy applications and turbine installations at different scales have increased since the beginning of this century. As wind energy is an alternative clean energy source compared to the fossil fuels that pollute the atmosphere, systems that convert wind energy to electricity have developed rapidly. Turkey’s domestic fossil fuel resources are extremely limited. In addition, Turkey’s geographical location has several advantages for extensive use of wind power. In this context, renewable energy resources appear to be one of the most efficient and effective solutions for sustainable energy development and environmental pollution prevention in Turkey. Among the renewable sources, Turkey has very high wind energy potential. According to the Organization for Economic Cooperation and Development (OECD) Turkey theoretically has 166 TWh a year of wind potential. However the installed wind power capacity is approximately 14% of total economical wind potential. In this study, Turkey’s installed electric power capacity and electric energy production are investigated and also the current situation of wind energy in Turkey is examined. The wind data used in this study were taken from Turkish Wind Energy Association (TUREB) for the year 2012. This paper reviews the assessment of wind energy in Turkey as of the end of July 2012 including wind energy applications.

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Raşit Ata

Papers

Artificial neural networks applications in wind energy systems: a review

An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines

Assessment of optimum tip speed ratio in wind turbines using artificial neural networks

Assessment of optimum tip speed ratio of wind turbines

The Current Situation of Wind Energy in Turkey