AI-Based Wind Tracking and Yaw Control System for Optimizing Wind Turbine Efficiency

Owing to its being an important component of renewable energy, wind energy is a kind of power generation method with the most mature, highly developed technologies and broad commercial prospects. The stable and efficient conversion of wind energy by wind turbines depends not only on the reliability of the wind power generation equipment itself, but also on the wind turbine control system, which, in turn, contributes to long-term safe and reliable operation of the wind farm fleet. It is exactly the wind turbine control system that is the main subject of this study. The key to efficient and stable operation of the entire wind energy conversion system is the control technology, which includes yaw control, pitch angle control, and maximum power point tracking control. The active yaw control system is one of the important components of a horizontal axis wind turbine's control system. To eliminate the uncertainty of wind direction influence on the turbine power output, a composite yaw control system has been checked. By using an active yaw system and maximum power point tracking system, the turbine position and its rotation speed are adjusted to enable the wind turbine to accurately track the wind direction and capture the wind energy to the fullest extent.

Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions

Electrical Parts, Control Systems and Power Electronics of Wind Turbines

The ability to significantly contribute to the frequency regulation and provide valuable ancillary services to the transmission system operator (TSO) is one of the present wind farm (WF) challenges, due to the limitations of wind speed forecasting and insufficient power reserve in certain operating conditions notably. In this work, the feasibility of WFs to participate in frequency restoration reserve (FRR) through yaw control is assessed. To this end, a distributed yaw optimization method is developed to evaluate the power gain achieved by yaw redirection based on wind turbine cooperation and compared with a greedy approach. The method relies on a static wake model whose parameters are estimated in a systematic way from simulation data generated with FAST.Farm. Through a case study based on a scaled version of the Belgian Mermaid offshore WF, it is demonstrated that the requirements of the TSO are fulfilled both in terms of response time and level of power reserve for most wind directions. The assessment is limited to wind speeds below the rated speed of the considered wind turbines.

The effective exploitation of renewable energy sources is one of the most effective solutions to counter the energy, environmental and economic problems associated with the use of fossil fuels. Small-scale wind turbines (converting wind energy into electric energy with a power output lower than 50 kW) have received tremendous attention over the past few decades thanks to their reduced environmental impact, high efficiency, low maintenance cost, high reliability, wide wind operation range, self-starting capability at low wind speed, limited installation space, reduced dependence on grid-connected power and long transmission lines, low capital costs, as well as the possibility to be installed in some urban settings. However, there are significant challenges and drawbacks associated with this technology from many different perspectives, including the significant discrepancy between theoretical performance data provided by the manufacturers and real field operation, that need to be investigated in greater depth in order to enable a more widespread deployment of small-scale wind turbines. In this review, a complete and updated list of more than 200 commercially available small-scale horizontal and vertical wind turbine models is provided and analysed, detailing the corresponding characteristics in terms of the number and material of blades, start-up wind speed, cut-in wind speed, cut-out wind speed, survival wind speed, maximum power, noise level, rotor diameter, turbine length, tower height, and specific capital cost. In addition, several scientific papers focusing on the experimental assessment of field performance of commercially available small-scale horizontal and vertical wind turbines have been reviewed and the corresponding measured data have been compared with the rated performance derived from the manufacturers’ datasheets in order to underline the discrepancies. This review represents an opportunity for the scientific community to have a clear and up-to-date picture of small-scale horizontal as well as vertical wind turbines on the market today, with a precise summary of their geometric, performance, and economic characteristics, which can enable a more accurate and informed choice of the wind turbine to be used depending on the application. It also describes the differences between theoretical and in-situ performance, emphasizing the need for further experimental research and highlighting the direction in which future studies should be directed for more efficient design and use of building-integrated small-scale wind turbines.

A crucial step in evaluating a potential location for a wind turbine, especially small-scale wind turbines, is a proper wind site survey. Eventually the wind site survey is used to calculate the annual energy production (AEP) of the wind turbine and determine if this location will be profitable. Generally, a wind classification of 3 or above is recommended for any wind turbine site, according to the U.S. Department of Energy. Wind Classes of 1–2 are not considered suitable; however, data suggests that a wind site with Class of 2 wind has the potential to be more cost effective than even the least expensive offshore wind and deserves consideration. Wind data usually exists at locations such as local airports; however, the height at which this data are taken is not representative of the heights at which wind turbines will be installed and thus, airport wind data should not be used. Also, with the variability in wind from location to location, the airport data are generally not near the potential site for the wind turbine and thus, are not useful. A local wind site survey generally entails a two year study of the site using a meteorological (MET) tower. Waco, TX is being studied for the application of small-scale wind turbines. Waco is in a Class 2 wind area; however, no proper wind survey had ever been accomplished. Such a study was undertaken using a MET tower of 100 ft with two anemometers at 100 ft, one anemometer at 75 ft and one anemometer at 50 ft. This paper will describe the potential of Class 2 wind as an energy source, the erection of the MET tower, collection of the data and analysis of the data for the potential of locating a small-scale wind turbine at the site. Techniques for analyzing data when two anemometers are present will be discussed. Focus will be on identifying invalid data with an emphasis on correcting this invalid data. The data from two anemometers was then used in a novel way to identify and correct the invalid data found at both the 75 ft and 50 ft elevations. A filtering technique has also been developed to help identify invalid data. Based on the results of the wind survey, it will be shown that it is feasible to purposely design wind turbine blades for Class 2 wind which will perform better than commercially available small-scale wind turbines.

In order to meet the increasing demand of wind energy utilization, wind turbines (WTs) are developing toward the trend of large size and large capacity. In such a trend, various advanced yaw control strategies have been proposed to improve large WTs' comprehensive performance, but the analysis and summary of these strategies are still lacking. Therefore, it is necessary to have a review of yaw control, which not only enables readers to understand the current status of yaw control research but also promotes the development of wind energy technology. This paper presents a review of the current situation of yaw control for WTs, focusing on the mechanical/aerodynamic parts. The mechanical part is concerned with the WT yaw system and its effect on the fatigue load of the WT, and the aerodynamic part involves the wind energy capture and wake redirection to reduce the impact on adjacent WTs. In this review, the existing yaw control methods are classified in term of three control objectives: (1) increasing the wind energy capture of a single WT, (2) reducing the fatigue load of a single WT, and (3) maximizing the total power production of the whole wind farm and optimizing the wind farm fatigue load. On this basis, the control mechanism, the control algorithm, and the results are presented and analyzed in detail. Meanwhile, the advantages and disadvantages of the existing achievements are discussed. In addition, in a conclusion of the review, the future research direction has been identified.

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

The wind turbines in a wind farm must turn to face the wind to achieve maximum power production. Until now, each turbine achieves this individually: the nacelle-mounted wind vane measures the yaw misalignment, from which the turbine controller decides when to make a yaw correction by using a combination of heavy filtering and counters or dead-band hysteresis. In a wind farm context, each turbine could make use of information from its neighbours, and therefore, in principle, it should be possible to achieve better yaw control by using a centralised farm-level yaw control algorithm which uses input measurements from all the turbines, and then tells each turbine how to yaw. “Better” yaw control will always be a compromise between maximising energy production and minimising yaw actuator duty. Using a realistic dynamic simulation model of a wind farm which uses actual measured wind data as input, different possible yaw strategies are tested, and compared in terms of energy production and yaw actuator duty. The results indicate that centralised yaw control is likely to achieve a better compromise. There also is much current interest in the use of wake steering, where yaw setpoints are optimised to steer the wakes of some turbines away from others. Centralised yaw control may offer a better way to implement these setpoints, rather than sending yaw offsets to be acted on by the normal turbine yaw controllers with their own filtering, dead-band logic, etc.

Influence of yaw control on flicker produced by wind turbines

The restructuring of the energy industry is imperative, as New Zealand strives to reduce greenhouse gas emissions. New Zealand has abundant renewable energy resources, and about 85% of current electricity generation is from renewable energy sources. However, in recent years, it appears that a considerable fraction of wind energy has been underutilized. This article reviews the history, current status, and future trends of wind energy development in New Zealand. The main challenges to the current development of wind energy are summarized compared to other countries. The main challenges come from the bi-cultural influence, environmental influence, and economic and social influence due to the variable nature of wind power, it is critical to store and operate power safely and reliably during peak power generation periods. This article compares seven mainstream wind energy storage technologies and analyzes the best solution for wind energy storage in New Zealand. This article analyzes the feasibility of using small-scale household (standard power rating range from 0.004 to 16 kW) wind turbines in New Zealand cities regarding their construction and operation process. The life cycle and the maximum capacity coefficient of such small-scale wind turbines are overviewed via three case studies and later compared with large commercial wind turbines (standard power rating ranges from 1 to 3 MW) in power generation capacity. It has been found that small-scale household wind turbines have notable power generation potential and economic benefits in the long term.

Wind energy is a valuable renewable resource that holds great potential for generating electrical power through wind turbines. However, there is a lack of practical and cost-effective small-scale wind turbines designed specifically for household usage. This study aims to address this need by focusing on the design and performance analysis of a small-scale vertical Darrieus wind turbine tailored for household applications in Malaysia. The objective is to propose an optimized turbine design that maximizes energy conversion efficiency. Computational fluid dynamics (CFD) simulations using ANSYS Fluent software are conducted to evaluate the turbine's performance, with the power coefficient (Cp) calculated at various tip speed ratios (TSR). Two 3D designs with different blade configurations are developed using SolidWorks for the CFD simulations. The findings from this study contribute to the design optimization and performance evaluation of vertical Darrieus wind turbines, providing a sustainable and viable alternative for residential energy needs. This research addresses the pressing demand for clean and affordable energy solutions in Malaysia and supports the transition towards renewable sources.

Language models based on Long Short-Term Memory (LSTM) neural networks have been widely applied to automatic speech recognition, natural language processing research. However, current LSTMs are trained by employing the cross-entropy loss function, which only considers the target category without considering the competing categories during the training processes. Thus, current training methods cannot fully exploit the discriminative information provided by the data labels. To tackle this problem, we propose a Large Margin Long Short-Term Memory Neural Network (LMLSTM) model in this paper. Our model employs the large margin discriminative principle as a heuristic term to navigate the convergence process during training. Our model has improved the discriminative ability of the original LSTM while maintaining the capability when generating sequential data. Our proposed large margin term was tested on the Penn Treebank corpus language modelling task. Experimental results demonstrate that the proposed LMLSTM model outperforms current LSTM models in terms of accuracy and perplexity without increasing the depth.

In wind turbines the power generation (power capture) and output torque depends on wind speed. Due to the rapid variations in wind speed, wind and inflow angle evaluating the output power becomes a challenging problem. Many optimization control techniques seek to extract the output power continuously. This paper focusses on dynamic analysis and control of variable wind turbine's rotor yaw angle and angular deflection. Earlier work used wind direction and pitch angle to control the performance of wind turbine. Using rotor yaw and angular deflection control of horizontal and vertical axis wind turbines is relatively new. For dynamic analysis, rotor yaw and rotor angular deflection, model techniques and experimental setup mechanisms were done in detail. Simulations were carried out in C++ to program an Arduino microcontroller and motor driver. Matlab software was used to interface wind turbine experimental setup, Arduino microcontroller and show the functions and aerodynamic forces. The result shows that the performance of wind turbine with this technique gives better result by increasing the power capture by about 15% more.

With the increasing capacity of wind turbines, the importance of the yaw system has gradually become more prominent. The supervisory control and data acquisition (SCADA) system of XEMC Windpower Co., Ltd. is used in this paper to analyze the yaw data of the No. 5 wind turbine of Baozhong Mountain Wind Farm in Hunan Province. In order to evaluate the power generation and the damage equivalent load (DEL) of the yaw bearing during the yaw process, an economic model predictive control (EMPC) yaw strategy based on light detection and ranging (LIDAR) was proposed. EMPC takes the yaw error and wind speed as the disturbance of the cost function (CF) at the same time by establishing the yaw bearing DEL look-up table in advance. Discrete adaptive yaw speed control set is proposed to adapt to different wind conditions sets. Finally, the effectiveness of EMPC is verified using the model of XE112-2000 wind turbine in simulation software Bladed.