Real-time Wind Turbine Research Articles

Design and Implementation of an Augmented Reality-based Interactive and Real-time Wind Turbine Maintenance Auxiliary Platform System

Design and Implementation of an Augmented Reality-based Interactive and Real-time Wind Turbine Maintenance Auxiliary Platform System

From wind conditions to operational strategy: optimal planning of wind turbine damage progression over its lifetime

Abstract. Renewable energies have an entirely different cost structure than fossil-fuel-based electricity generation. This is mainly due to the operation at zero marginal cost, whereas for fossil fuel plants the fuel itself is a major driver of the entire cost of energy. For a wind turbine, most of the materials and resources are spent up front. Over its lifetime, this initial capital and material investment is converted into usable energy. Therefore, it is desirable to gain the maximum benefit from the utilized materials for each individual turbine over its entire operating lifetime. Material usage is closely linked to individual damage progression of various turbine components and their respective failure modes. In this work, we present a novel approach for an optimal long-term planning of the operation of wind energy systems over their entire lifetime. It is based on a process for setting up a mathematical optimization problem that optimally distributes the available damage budget of a given failure mode over the entire lifetime. The complete process ranges from an adaptation of real-time wind turbine control to the evaluation of long-term goals and requirements. During this process, relevant deterministic external conditions and real-time controller setpoints influence the damage progression with equal importance. Finally, the selection of optimal planning strategies is based on an economic evaluation. The method is applied to an example for demonstration. It shows the high potential of the approach for an effective damage reduction in different use cases. The focus of the example is to effectively reduce power of a turbine under conditions where high loads are induced from wake-induced turbulence of neighbouring turbines. Through the optimization approach, the damage budget can be saved or spent under conditions where it pays off most in the long term. This way, it is possible to gain more energy from a given system and thus to reduce cost and ecological impact by a better usage of materials.

Wind Turbine Emulators—A Review

Renewable energy sources have become a significant alternative energy source due to the continuing depletion of conventional energy sources and fluctuation in fuel costs. Currently, wind energy is the foremost among all other renewable energy sources. However, modeling and analyzing industrial wind turbines is complex as the wind turbine power ratio and size have steadily increased. Undoubtedly, industrial wind turbines are huge and challenging to keep in research labs; simultaneously, exploring the controller/power converter performance is practically impossible. Therefore, to overcome the above drawbacks, wind turbine emulators have been developed to achieve the static and dynamic characteristics of wind energy conversion systems. This paper aims to present a comprehensive review of the different wind turbine emulators available in the literature. In addition, the implementation of real-time emulators is classified according to the structure and approaches. Furthermore, an extensive analysis of the emulators was presented based on the significant parameters utilized for the real-time wind turbine emulators. Finally, this review analyzes the different emulator topologies according to cost, accuracy, complexity, and hardware implementation.

Intelligent digital twin – machine learning system for real-time wind turbine wind speed and power generation forecasting

Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent service systems, combining real-time monitoring, predictive capabilities and communication technologies. Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) sensors, with predictions often limited to the timeframe of datasets. This research looks to expand on this, utilising a novel framework for an intelligent DT system powered by k-Nearest Neighbour (kNN) regression models to upscale live wind speed forecasts to higher wind turbine hub-height and then forecast power generation. As there is no live link to a wind turbine, the framework is referred to as a “Simulated Digital Twin” (SimTwin). 2019-2020 SCADA and wind speed data are used to evaluate this, demonstrating that the method provides suitable predictions. Furthermore, full deployment of the SimTwin framework is demonstrated using live wind speed forecasts. This may prove useful for operators by reducing reliance on SCADA systems and provides a research and development tool where live data is limited.

Implementation of Preventive and Corrective measures in SCADA based Wind Turbines

Wind energy is becoming increasingly important in addressing energy demand and environmental sustainability. It is driven by the outstanding offshore wind resource and the need to minimize carbon emissions from energy generation. A growing interest in wind turbine condition monitoring, which allows condition based rather than reactive or scheduled maintenance, stems from the difficulty of accessing and maintaining wind turbines offshore. Vibration analysis and oil debris detection are currently used wind turbine condition monitoring methods. The extra expenditures might be enormous when considering the number of turbines normally put in offshore wind farms, and specialist interpretation is usually required. However, extending SCADA data-based analysis could bring significant value to condition monitoring at little or no expense to the wind farm operator. This Research Paper focuses on wind turbine condition monitoring using only SCADA data. An early detection of operational defects or breakdowns allows for regular maintenance or repair, decreasing downtime and potentially preventing further damage. Comparing incoming operational SCADA data with results for relevant variables, like component temperature, produced from applicable models trained on SCADA data from a healthy wind turbine, yields useful component condition indicators. Anomalies in the variables of interest in the SCADA data indicate impending breakdowns. This method is used to derive machine-to-machine comparison of component condition Compared to developing separate turbine models, this saves significant computing work and model complexity. A real-time wind turbine power curve is also created using SCADA data and compared to a reference power curve to quickly discover abnormal behavior based on slight variations in the power curve.

Wind inflow observation from load harmonics via neural networks: A simulation and field study

In this work, feed-forward neural networks are used to estimate the inflow of a wind turbine in real time during operation. A previously published method achieved this capability by correlating the turbine response to the wind inflow via a piecewise-linear model. Specifically, the existing formulation used the once-per-revolution harmonics of both in- and out-of-plane blade bending moments to estimate four wind parameters: the vertical and horizontal shears, and the vertical and horizontal misalignment angles. The novel method presented here builds on the same concept, but replaces the linear model with neural networks. Compared to the previous approach, the neural-based one is simpler to tune and use, because it decouples the wind parameters, which can be observed each one independently from the others. The proposed formulation is tested using both aeroservoelastic simulations and field data obtained on a 3.5 MW machine. In the field, independent measurements of the inflow are provided by a nearby hub-tall met mast, providing data for the training and verification of the wind observer. Notwithstanding some limitations of the experimental setup, results indicate that the neural networks produce estimates of the shears and yaw misalignment angle with accuracies compatible with typical use cases in wind turbine and wind farm control. Additionally, the neural-based observer appears to slightly outperform the original formulation in the lower wind speed range, where stronger nonlinearities in the load-wind mapping can occur.

Wind Turbine Pitch System Fault Detection Using ssODM-DSTA

A fault detection method of wind turbine pitch system using semi-supervised optimal margin distribution machine (ssODM) optimized by dynamic state transition algorithm (DSTA) [ssODM-DSTA] was proposed to solve the problem of obtaining the optimal hyperparameters of the fault detection model for the pitch system. This method was adopted to input the three hyperparameters of the ssODM into the dynamic state transition algorithm in the form of a three-dimensional vector to obtain the global optimal hyperparameters of the model, thus improving the performance of the fault detection model. Using a random forest to rank the priority of features of the pitch system fault data, the features with large weight proportions were retained. Then, the Pearson correlation method is used to analyze the degree of correlation among features, filter redundant features, and reduce the scale of features. The dataset was divided into a training dataset and a test dataset to train and test the proposed fault detection model, respectively. The real-time wind turbine pitch system fault data were collected from domestic wind farms to carry out fault detection experiments. The results have shown that the proposed method had a positive fault rate (FPR) and fault negative rate (FNR), compared with other optimization algorithms.

Robust wind turbine emulator design using sliding mode controller

This work deals with the development of a real-time wind turbine emulator (WTE) for laboratory purpose using a robust sliding mode controller (SMC). The application of power electronic converters with various control strategies in machine control facilitate the imitation of a practical wind turbine characteristics using a separately excited DC motor. The SMC theory is used here to develop a control law that governs the system to track the reference current set by the mathematical model of the wind turbine to get the desired output. The static, dynamic characteristics and also the transient responses of the WTE are satisfactorily reproduced by the proposed SMC method. To validate the performance of WTE prototype, it is tested under various operating conditions. A performance comparison of the proposed SMC with the existing proportional and integral (PI) controller in terms of tracking speed, peak overshoot and settling time are discussed in detail. The experimental result analysis confirms that the proposed control scheme improves the imitation of the real-time wind turbine characteristics and performs efficiently under both steady-state and transient conditions.

Hardware-in-the-loop Testing of Wind Turbine Nacelles for Electrical Certification on a Dynamometer Test Rig

This paper presents the design and implementation of a hardware-in-the-loop testing framework, for electrical certification of wind turbine nacelles on a dynamometer test rig. This is to enable a realistic test environment in the laboratory, comparable to that in the field. Assessment of electrical properties of wind turbines are usually executed in the field for the complete prototype, however, in the laboratory, missing system components have to be emulated and the effect of test rig’s own actuators are to be attenuated. Therefore, implementation of an adequate real-time wind turbine simulation tool, an efficient control system for the test rig drive train, and a real-time automation system are considered and presented here. Furthermore, execution of type certification experiments according to national and international guidelines is described here and illustrative measurements are provided. The measurements in this case are obtained at Dynamic Nacelle Laboratory, while testing with the Enercon E-115E2 turbine. The results achieved demonstrate successful test execution in the laboratory and declare the advantage of test rigs.

Locating and tracking sound sources on a horizontal axis wind turbine using a compact microphone array based on beamforming

As the number and capacity of wind-power installations increases rapidly worldwide, the noise problems generated by wind turbines, which are increasingly being installed near residential areas, become increasingly prominent. Wind turbines can be approached as aerodynamic noise generation mechanisms for noise reduction purposes, for which a precisely localized sound source is key. Sound source recognition algorithms capable of locating the radial sound source position and relative azimuth angle of a rotating wind turbine blade in real time are rare. This paper proposes an algorithm based on classical beamforming theory with instantaneous speed and delayed azimuth angle rotation correction. First, the sound identification and tracking accuracies of the algorithm are verified using a rotating simulation bench; next, a small horizontal axis wind turbine is tracked and identified according to the given step size in the wind tunnel. The results show that the algorithm can precisely tracked and identified sound source of horizontal axis wind turbine in real time. Some errors were identified between the difference in two consecutive azimuth angles and a set step size of 60°, caused by fluctuations in rotating speed and characteristic vibrations of the wind turbine.

Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, α ∈ [ 1 % , 13 % ] , the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.

Wind turbine remote control using Android devices

This paper describes the remote control of a wind turbine system over the internet using an Android device, namely a tablet or a smartphone. The wind turbine workstation contains a LabVIEW program which monitors the entire wind turbine energy conversion system (WECS). The Android device connects to the LabVIEW application, working as a remote interface to the wind turbine. The communication between the devices needs to be secured because it takes place over the internet. Hence, the data are encrypted before being sent through the network. The scope was the design of remote control software capable of visualizing real-time wind turbine data through a secure connection. Since the WECS is fully automated and no full-time human operator exists, unattended access to the turbine workstation is needed. Therefore the device must not require any confirmation or permission from the computer operator in order to control it. Another condition is that Android application does not have any root requirements.

Pitch control for wind turbine systems using optimization, estimation and compensation

Delays caused by hydraulic pressure driven units in wind turbines may degrade and even destabilize pitch control system, leading to the performance degradation or collapse of the whole wind turbine energy conversion system. As a result, there is a strong motivation to improve pitch control technique to overcome the adverse effects from the unknown delays caused by the hydraulic driven units. In this study, a novel pitch control approach is developed by integrating optimization, delay-perturbation estimation, and signal compensation techniques. Specifically, an optimal PI controller is designed by applying direct search optimization to ideal delay-free pitch model. A delay estimator is next designed to estimate the perturbation caused by the delay. The signal compensation technique is then implemented to remove the effect from the delay-perturbation to the turbine output so that the actual output is consistent with the optimal output of the ideal delay-free model. Furthermore, the technique is extended to PID-based pitch control systems. The blade-tower dynamics models of three wind turbines, respectively with rated powers 1.5 MW, 275 kW and 50 kW, are investigated and the effectiveness of the proposed techniques are demonstrated by detailed simulation studies. It is worthy to point out a priori on the delay is not necessary in this design, which much meets the situations in practical wind turbine systems. Finally, a 4.8 MW benchmark wind turbine energy conversion system is investigated via the proposed pitch control technique, which has shown that delay perturbation estimate based compensation can effectively improve the pitch control performance in the real-time wind turbine energy conversion system.

A review on the micro energy harvester in Structural Health Monitoring (SHM) of biocomposite material for Vertical Axis Wind Turbine (VAWT) system: A Malaysia perspective

The usage of wind energy as a form of renewable energy is becoming increasingly popular year by year. This technology has been applied widely in several regions in the world and already reached maturity in terms of technology, infrastructure and cost competitiveness. The performance of the wind turbine system depends upon factors such as design, aerodynamic performance and material selection. Thus, Structural Health Monitoring (SHM) has become crucial in evaluating the performance of wind turbine in real time. Furthermore, the application of smart material in SHM can be utilized as micro energy harvester as well. Nonetheless, the application of SHM in Malaysia׳s climate for wind turbine is still premature, especially in the approach biocomposites material towards its blade system. Several issues are highlighted in this paper such as Vertical Axis Wind Turbine (VAWT), biocomposites material selection and the issue in the micro energy harvester as well. The issues are discussed and compared with the recent finding in this review. Several recommendations are suggested for future studies in benefiting the Malaysian especially on the application of wind energy to promote better green technology for tomorrow.

Development of a high speed wideband frequency tunable infra-red laser source for real-time wind turbine array sensing applications

The global demand for low emission and renewable energy production continues to increase. Wind turbine-based power generators, harnessing the kinetic energy from wind continue to proliferate owing to its potential for scalability and ease of deployment. To enable maximum efficiency of the turbines, it is necessary to integrate complex real-time sensing systems to monitor the magnitude and direction of the wind front. This paper reports the development of an advanced LIDAR (LIght Detection And Ranging) system for monitoring the profile of the incoming wind front. By using a solid-state tunable spectrally pure laser source, combined with fast frequency tuning capability and frequency selective multiplex technique, the LIDAR system can support multi-frequencies for simultaneous measurements, therefore a single tunable frequency source design can replace many fixed-frequency bulky laser sources currently used for each wind farm installation, leading to a significant reduction in inventory complexity and associated cost of installation and operation. The newly developed multi-frequency LIDAR system also demonstrates a high degree of measurement accuracy comparative with existing systems.

Simulation Model of Wind Turbine 3p Torque Oscillations due to Wind Shear and Tower Shadow

To determine the control structures and possible power quality issues, the dynamic torque generated by the blades of a wind turbine must be represented. This paper presents an analytical formulation of the generated aerodynamic torque of a three-bladed wind turbine including the effects of wind shear and tower shadow. The comprehensive model includes turbine-specific parameters such as radius, height, and tower dimensions, as well as the site-specific parameter, the wind shear exponent. The model proves the existence of a 3p pulsation due to wind shear and explains why it cannot be easily identified in field measurements. The proportionality constant between the torque and the wind speed is determined allowing direct aerodynamic torque calculation from an equivalent wind speed. It is shown that the tower shadow effect is more dominant than the wind shear effect in determining the dynamic torque, although there is a small dc reduction in the torque oscillation due to wind shear. The model is suitable for real-time wind turbine simulation or other time domain simulation of wind turbines in power systems

Large band simulation of the wind speed for real time wind turbine simulators

In this paper, the authors propose two modeling procedures for wind speed simulation. These procedures could be implemented on the structure of a wind turbine simulator during studies concerning stand-alone or hybrid wind systems. The evolution of a horizontal wind speed has been synthesized taking into account two components. The medium- and long-term component is described by a power spectrum associated to a specific site. The turbulence component is assumed to be dependent on the medium- and long-term wind speed evolution. It is considered as a nonstationary process. Two simulation methods for this component, using rational and nonrational filters are proposed. In both procedures, the turbulence model is defined by two parameters, which are either obtained experimentally, or adopted a priori, according to information from the considered site. Numerical results and implementation aspects are also discussed.

Error Evaluation for Models of Real Time Wind Turbine Simulators

This paper deals with errors inherent in modelling wind turbine simulators. Firstly, different wind turbine simulators are categorised. The work concerns simulators having a real-time soft simulator and an electromechanical tracking system, using any type of servomotor 15–17. Having presented the wind turbine linearised models, with respect to types of servomotor control (in speed or in torque), an analysis is made of simulator mathematical models and simulation performance evaluation, according to the dynamical behaviour of the electromechanical tracking systems. Next, the simulator performances calculation is presented, followed by a simulation error analysis for some particular cases, in order to highlight the differences between the speed and torque control structures. Finally, a servomotor choice principle is established, illustrated by numerical results obtained with the servomotor of a real time wind turbine simulator.