scholarly journals Frequency Scan–Based Mitigation Approach of Subsynchronous Control Interaction in Type-3 Wind Turbines

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4626
Author(s):  
Faris Alatar ◽  
Ali Mehrizi-Sani
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Operating Conditions ◽  
Steady State Operation ◽  
Significant Damage ◽  
Frequency Scan ◽  
Type 3 ◽  
Phase Angles ◽  
Wind Energy Resources ◽  
Control Interaction

Integration of wind energy resources into the grid creates several challenges for power system dynamics. More specifically, Type-3 wind turbines are susceptible to subsynchronous control interactions (SSCIs) when they become radially connected to a series-compensated transmission line. SSCIs can cause disruptions in power generation and can result in significant damage to wind farm (WF) components and equipment. This paper proposes an approach to mitigate SSCIs using an online frequency scan, with optimized phase angles of voltage harmonic injection to maintain steady-state operation, to modify the controllers or the operating conditions of the wind turbine. The proposed strategy is simulated in PSCAD/EMTDC software on the IEEE second benchmark model for subsynchronous resonance. Simulation results demonstrate the effectiveness of this strategy by ensuring oscillations do not grow.

scholarly journals Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Operating Conditions ◽  
Wake Effect ◽  
Wind Generators ◽  
Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

Study on Sub-Synchronous Control Interaction (SSCI) of Wind-Power Generator

2014 ◽  
Vol 953-954 ◽  
pp. 518-521 ◽  
Author(s):  
Jin Liu ◽  
Shu Qiang Zhao
Keyword(s):  
Wind Power ◽  
Wind Turbines ◽  
Transmission Lines ◽  
Large Scale ◽  
Wind Farm ◽  
Rapid Development ◽  
Synchronous Oscillation ◽  
Synchronous Control ◽  
Wind Power Generator ◽  
Control Interaction

Large-scale wind turbine generators with power electronic converters that operate near series compensated transmission lines are susceptible to un-damped sub-synchronous oscillations. This sub-synchronous oscillation is called Sub-synchronous Control Interaction (SSCI). With the rapid development of wind power technology, SSCI emerges as a new sub-synchronous oscillation phenomenon. The first SSCI event occurred for a wind farm in America in2009, which resulted in damage to the wind turbines. The origin of SSCI is presented and its characteristics of different types of wind turbines are summarized. Then the research prospect on this field is explored.

scholarly journals Wind Turbine Noise Prediction Using Random Forest Regression

Machines ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 69 ◽  
Author(s):  
Gino Iannace ◽  
Giuseppe Ciaburro ◽  
Amelia Trematerra
Keyword(s):  
Random Forest ◽  
Prediction Model ◽  
Wind Turbines ◽  
Wind Farm ◽  
Renewable Energy Sources ◽  
Pearson Correlation ◽  
Operating Conditions ◽  
Random Forest Algorithm ◽  
Model Based ◽  
Scada System

Wind energy is one of the most widely used renewable energy sources in the world and has grown rapidly in recent years. However, the wind towers generate a noise that is perceived as an annoyance by the population living near the wind farms. It is therefore important to new tools that can help wind farm builders and the administrations. In this study, the measurements of the noise emitted by a wind farm and the data recorded by the supervisory control and data acquisition (SCADA) system were used to construct a prediction model. First, acoustic measurements and control system data have been analyzed to characterize the phenomenon. An appropriate number of observations were then extracted, and these data were pre-processed. Subsequently two models of prediction of sound pressure levels were built at the receiver: a model based on multiple linear regression, and a model based on Random Forest algorithm. As predictors wind speeds measured near the wind turbines and the active power of the turbines were selected. Both data were measured by the SCADA system of wind turbines. The model based on the Random Forest algorithm showed high values of the Pearson correlation coefficient (0.981), indicating a high number of correct predictions. This model can be extremely useful, both for the receiver and for the wind farm manager. Through the results of the model it will be possible to establish for which wind speed values the noise produced by wind turbines become dominant. Furthermore, the predictive model can give an overview of the noise produced by the receiver from the system in different operating conditions. Finally, the prediction model does not require the shutdown of the plant, a very expensive procedure due to the consequent loss of production.

scholarly journals An equivalent modeling method for Offshore Wind Farms based on fault characteristics analysis

2019 ◽  
Vol 107 ◽  
pp. 01004
Author(s):  
Haiyan Tang ◽  
Guanglei Li ◽  
Linan Qu ◽  
Yan Li
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Modeling Method ◽  
Operating Conditions ◽  
Offshore Wind ◽  
Equivalent Model ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Characteristics Analysis

A large offshore wind farm usually consists of dozens or even hundreds of wind turbines. Due to the limitation of the simulation scale, it is necessary to develop an equivalent model of offshore wind farms for power system studies. At present, the aggregation method is widely adopted for wind farm equivalent modeling. In this paper, the topology, electrical parameters, operating conditions and individual turbine characteristics of the offshore wind farms are taken into consideration. Firstly, the output power distribution of offshore wind farm, the voltage distribution of the collector system and the fault ride-through characteristics of wind turbines are analyzed. Then, a dynamic equivalent modeling method for offshore wind farms is developed based on the fault characteristics analysis. Finally, the proposed method is validated through time-domain simulation.

scholarly journals Modified Power Curves for Prediction of Power Output of Wind Farms

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1805 ◽  
Author(s):  
Mohsen Vahidzadeh ◽  
Corey D. Markfort
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Sonic Anemometer ◽  
Wind Farms ◽  
Operating Conditions ◽  
Horizontal Axis Wind Turbine ◽  
Power Curves

Power curves are used to model power generation of wind turbines, which in turn is used for wind energy assessment and forecasting total wind farm power output of operating wind farms. Power curves are based on ideal uniform inflow conditions, however, as wind turbines are installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in variability of the inflow must be considered. We propose an approach to include turbulence, yaw error, air density, wind veer and shear in the prediction of turbine power by using high resolution wind measurements. In this study, two modified power curves using standard ten-minute wind speed and high resolution one-second data along with a derived power surface were tested and compared to the standard operating curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and wind speed measurements from a nearby meteorological tower are used in the models. The results show that all of the proposed models perform better than the standard power curve while the power surface results in the most accurate power prediction.

Wind Speed Dependency of Low-Frequency Vibration Levels in Full-Scale Wind Turbines

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Xavier Escaler ◽  
Toufik Mebarki
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
High Speed ◽  
Wind Farm ◽  
Low Frequency ◽  
Average Frequency ◽  
Operating Conditions ◽  
Positive Slope ◽  
Low Frequency Vibration ◽  
Linear Fit

A series of continuous vibration measurements in 14 upwind wind turbines of the same model and belonging to the same wind farm have been conducted. The data were acquired over a period lasting approximately half a year. The tower axial vibration acceleration has been monitored in the frequency band from 0 to 10 Hz with an accelerometer mounted on the gearbox casing between the intermediate and the high-speed shafts. It has been observed that the average frequency spectrum is dominated by the blade passing frequency in all the wind turbines. The evolution of the vibration magnitudes over the entire range of operating conditions is also very similar for all the wind turbines. The root-mean-square (rms) acceleration value has been correlated with the wind speed, and it has been found that a linear fit with a positive slope is a useful model for prediction purposes.

scholarly journals Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 982 ◽  
Author(s):  
Xin Wu ◽  
Hong Wang ◽  
Guoqian Jiang ◽  
Ping Xie ◽  
Xiaoli Li
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Operating Conditions ◽  
Dynamic Threshold ◽  
Detection Accuracy ◽  
Echo State Network ◽  
Monitoring Method ◽  
Vibration Data ◽  
Static Monitoring ◽  
Threshold Monitoring

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

scholarly journals Maintenance Planning of Offshore Wind Turbine Using Condition Monitoring Information

2009 ◽  
Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Planning ◽  
Offshore Wind Turbines ◽  
Inspection And Maintenance ◽  
Monitoring Information

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.

scholarly journals Wealth of Wind and Visitors: Tourist Industry Attitudes towards Wind Energy Development in Iceland

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 693
Author(s):  
Anna Dóra Sæþórsdóttir ◽  
Margrét Wendt ◽  
Edita Tverijonaite
Keyword(s):  
Land Use ◽  
Wind Energy ◽  
Wind Turbines ◽  
Wind Farm ◽  
Service Providers ◽  
Wind Farms ◽  
Energy Development ◽  
Tourism Industry ◽  
Land Use Conflicts ◽  
Wind Energy Development

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.

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