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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
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Designing a solid shaft that transmits power from a motor to a machine tool involves a series of calculations to ensure the shaft can withstand the stresses applied by bending moments and torques. First, calculate the torque exerted on the gear, considering the power transmitted by the shaft and its rotational speed. Following this, compute the tangential forces acting on the gears, which directly relate to the torque and the gear radius.
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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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The design of a transmission shaft is governed by two primary specifications: the power it transmits and its rotational speed. These parameters guide the selection of the shaft's material and cross-sectional dimensions, ensuring that the material's maximum shearing stress remains within the elastic limit while transmitting the desired power at the given speed. The system's power is intrinsically linked to the applied torque. The torque applied to the shaft can be calculated by...
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Generator Voltage Control01:21

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Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
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Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning

Baher Azzam1, Ralf Schelenz1, Georg Jacobs1

  • 1Center for Wind Power Drives, RWTH Aachen University, 52074 Aachen, Germany.

Sensors (Basel, Switzerland)
|May 28, 2022
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Summary

Identifying optimal sensor locations is key to reducing the cost of virtual sensing for wind turbine (WT) gearbox loads. This study uses random forest models to pinpoint the most impactful sensor placements for accurate load monitoring.

Keywords:
artificial intelligencedrivetrain simulationfeature importancemachine learningmultivariate data analysistime-series simulationvirtual sensing

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Area of Science:

  • Mechanical Engineering
  • Renewable Energy Systems
  • Condition Monitoring

Background:

  • Increasing wind turbine (WT) size drives higher drivetrain loads, necessitating gearbox load monitoring.
  • Direct load measurement is costly; virtual sensing using stationary sensors offers an economical alternative.
  • Optimizing sensor number and placement is crucial for cost-effective virtual sensing solutions.

Purpose of the Study:

  • To identify optimal sensor locations for virtual sensing of WT 6-degree of freedom (6-DOF) transmission input loads.
  • To reduce the cost of virtual sensing systems by prioritizing essential sensor placements.
  • To evaluate the impact of sensor selection on the accuracy of load prediction.

Main Methods:

  • Utilized random forest (RF) models applied to simulated operational data from a Vestas V52 WT multibody model.
  • Analyzed 6-DOF transmission input loads alongside signals from potential sensor locations (deformations, misalignments, rotational speeds).
  • Employed a statistical test to rank sensor locations based on their impact on virtual load sensing accuracy.

Main Results:

  • RF models successfully identified sensor locations with the highest influence on virtual load sensing accuracy.
  • Model performance was assessed before and after reducing the number of input signals, demonstrating robustness.
  • The study prioritized and reduced the number of necessary input signals for effective load monitoring.

Conclusions:

  • The proposed method shows high promise for optimizing the cost of future virtual WT transmission load sensors.
  • Screening sensor locations prior to real-world implementation can significantly improve cost-efficiency.
  • Virtual sensing offers a viable and economical approach to monitoring intensified WT gearbox loads.