scholarly journals Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model

2021 ◽  
Vol 11 (14) ◽  
pp. 6405
Author(s):  
Pere Marti-Puig ◽  
Alejandro Bennásar-Sevillá ◽  
Alejandro Blanco-M. ◽  
Jordi Solé-Casals
Keyword(s):  
Wind Turbine ◽  
Low Cost ◽  
Wind Farms ◽  
Temporal Aggregation ◽  
Time Interval ◽  
Irreversible Loss ◽  
Loss Of Information ◽  
Statistical Measures ◽  
Maximum Value ◽  
Additional Equipment

Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical measures (mean, standard deviation, maximum value, and minimum value) of hundreds of wind turbine magnitudes, usually in a 5-min or 10-min interval. Several studies have analysed the loss of information associated with the reduction of information when using five minutes instead of four seconds as a sampling frequency, or when compressing a time series recorded at 5 min to 10 min, concluding that some, but not all, of these magnitudes are seriously affected. However, to our knowledge, there are no studies on increasing the time interval beyond 10 min to take these four statistical values, and how this aggregation affects prognosis models. Our work shows that, despite the irreversible loss of information that occurs in the first 5 min, increasing the time considered to take the four representative statistical values improves the performance of the predicted targets in normality models.

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

scholarly journals Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3333
Author(s):  
Maria del Cisne Feijóo ◽  
Yovana Zambrano ◽  
Yolanda Vidal ◽  
Christian Tutivén
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Small Subset ◽  
Scaled Model ◽  
Vibration Data ◽  
Large Water

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

scholarly journals Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2319
Author(s):  
Hyun-Goo Kim ◽  
Jin-Young Kim
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Free Stream ◽  
Turbine Blades ◽  
Wind Farms ◽  
Lidar Measurement ◽  
Wind Turbine Blades ◽  
Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

scholarly journals Dynamic Modeling of an Offshore Floating Wind Turbine for Application in the Mediterranean Sea

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 248
Author(s):  
Lorenzo Cottura ◽  
Riccardo Caradonna ◽  
Alberto Ghigo ◽  
Riccardo Novo ◽  
Giovanni Bracco ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Mediterranean Sea ◽  
Wind Farm ◽  
Reference Model ◽  
Wide Spectrum ◽  
Wind Farms ◽  
Offshore Wind ◽  
Floating Wind Turbine ◽  
The Mediterranean ◽  
Offshore Floating Wind Turbine

Wind power is emerging as one of the most sustainable and low-cost options for energy production. Far-offshore floating wind turbines are attractive in view of exploiting high wind availability sites while minimizing environmental and landscape impact. In the last few years, some offshore floating wind farms were deployed in Northern Europe for technology validation, with very promising results. At present time, however, no offshore wind farm installations have been developed in the Mediterranean Sea. The aim of this work is to comprehensively model an offshore floating wind turbine and examine the behavior resulting from a wide spectrum of sea and wind states typical of the Mediterranean Sea. The flexible and accessible in-house model developed for this purpose is compared with the reference model FAST v8.16 for verifying its reliability. Then, a simulation campaign is carried out to estimate the wind turbine LCOE (Levelized Cost of Energy). Based on this, the best substructure is chosen and the convenience of the investment is evaluated.

scholarly journals Effect of Wind Turbine Classes on the Electricity Production of Wind Farms in Cyprus Island

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yiannis A. Katsigiannis ◽  
George S. Stavrakakis ◽  
Christodoulos Pharconides
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Wind Farms ◽  
Electricity Production ◽  
Wind Speeds ◽  
Wind Potential

This paper examines the effect of different wind turbine classes on the electricity production of wind farms in two areas of Cyprus Island, which present low and medium wind potentials: Xylofagou and Limassol. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from five different manufacturers have been used. For each manufacturer, two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC II and IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (IEC III class) in both locations, in terms of energy production. This improvement is higher for the location with the lower wind potential and starts from 7%, while it can reach more than 50%.

Model Test and Numerical Analysis of an Offshore Bottom Fixed Pentapod Wind Turbine Under Seismic Loads

2016 ◽  
Author(s):  
Wenhua Wang ◽  
Zhen Gao ◽  
Xin Li ◽  
Torgeir Moan ◽  
Bin Wang
Keyword(s):  
Wind Turbine ◽  
Wind Wave ◽  
Seismic Analysis ◽  
Wind Farms ◽  
Dynamic Responses ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Test Model ◽  
Structural Dynamic ◽  
Load Conditions

In the last decade the wind energy industry has developed rapidly in China, especially offshore. For a water depth less than 20m, monopile and multi-pile substructures (tripod, pentapod) are applied widely in offshore wind farms. Some wind farms in China are located in high seismicity regions, thus, the earthquake load may become the dominant load for offshore wind turbines. This paper deals with the seismic behavior of an offshore wind turbine (OWT) consisting of the NREL 5MW baseline wind turbine, a pentapod substructure and a pile foundation of a real offshore wind turbine in China. A test model of the OWT is designed based on the hydro-elastic similarity. Test cases of different load combinations are performed with the environmental conditions generated by the Joint Earthquake, Wave and Current Simulation System and the Simple Wind Field Generation System at Dalian University of Technology, China, in order to investigate the structural dynamic responses under different load conditions. In the tests, a circular disk is used to model the rotor-nacelle system, and a force gauge is fixed at the center of the disk to measure the wind forces during the tests. A series of accelerometers are arranged along the model tower and the pentapod piles, and strain gauges glued on the substructure members are intended to measure the structural dynamic responses. A finite element model of the complete wind turbine is also established in order to compare the theoretical results with the test data. The hydro-elastic similarity is validated based on the comparison of the measured dynamic characteristics and the results of the prototype modal analysis. The numerical results agree well with the experimental data. Based on the comparisons of the results, the effect of the wind and sea loads on the structural responses subjected to seismic is demonstrated, especially the influence on the global response of the structure. It is seen that the effect of the combined seismic, wind, wave and current load conditions can not be simply superimposed. Hence the interaction effect in the seismic analysis should be considered when the wind, wave and current loads have a non-negligible effect.

CALCULATION OF THE PARAMETERS OF THE WIND TURBINE ROTOR TEMPERATURE SENSOR

2021 ◽  
Vol 118 (3) ◽  
pp. 141-149
Author(s):  
Alina Fazylova
Keyword(s):  
Wind Turbine ◽  
Temperature Sensor ◽  
Wind Farms ◽  
Turbine Rotor ◽  
Correct Selection ◽  
Temperature Control System ◽  
Working Pressure ◽  
Generator System ◽  
Wind Generator ◽  
The Brain

Today, all the processes associated with technology, mainly operate in autonomous modes, so devices such as a temperature sensor are a must. Since technical progress is taking place in industry and production by leaps and bounds, all equipment most often used in various kinds of processes and work has an automatic principle of operation [1] However, for productive work, albeit automated units, it is necessary to comply with all the exact indicators at which the device's performance will be the highest. These indicators include values, namely the required, more precisely, the working pressure, speed, and temperature. To prevent rapid wear and overload of automated equipment, the temperature level must be measured. Of course, this is not done with a simple thermometer or thermometer. For these purposes, special devices are used, such as temperature sensors. Wind power is one of the most important renewable energy solutions. As a rule, wind farms are located far from civilization, in remote regions - in hilly areas, on the sea coast [2]. These types of terrain are chosen because wind energy becomes profitable only under conditions that allow obtaining the maximum amount of wind throughout the year. However, the harsher environment calls for more sophisticated wind turbines, usually consisting of towers, blades, hubs and nacelles. To ensure control over all parts of the wind turbine, companies install controllers near the tower. A wind generator controller, like a PLC, is the brain of any wind generator, providing control over the system as a whole, the ability to generate reports and monitoring. The generator must be controlled and programmed; without a controller, it couldn't work correctly. For this reason, the controllers must be connected to a single system for remote monitoring, power generation reporting, parameter monitoring and diagnostic maintenance [3].This article provides an algorithm for calculating a temperature sensor for its correct selection for a wind generator system, which will subsequently be used for a temperature control system in a generator system.

Machine Learning Aided Prediction of Rain Erosion Damage on Wind Turbine Blade Sections

2021 ◽  
Author(s):  
Alessio Castorrini ◽  
Paolo Venturini ◽  
Fabrizio Gerboni ◽  
Alessandro Corsini ◽  
Franco Rispoli
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Energy Production ◽  
Turbine Blades ◽  
Wind Farms ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Coating Materials ◽  
Erosion Modelling ◽  
Rain Erosion

Abstract Rain erosion of wind turbine blades represents an interesting topic of study due to its non-negligible impact on annual energy production of the wind farms installed in rainy sites. A considerable amount of recent research works has been oriented to this subject, proposing rain erosion modelling, performance losses prediction, structural issues studies, etc. This work aims to present a new method to predict the damage on a wind turbine blade. The method is applied here to study the effect of different rain conditions and blade coating materials, on the damage produced by the rain over a representative section of a reference 5MW turbine blade operating in normal turbulence wind conditions.

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