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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

181
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.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
181
Turbine-Governor Control01:17

Turbine-Governor Control

318
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...
318
Multimachine Stability01:25

Multimachine Stability

208
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
208
Generation of Three-Phase Voltage01:21

Generation of Three-Phase Voltage

434
A three-phase AC generator has a rotor with a rotating magnet placed within the stator mounted with the stationary three-phase winding to generate three-phase voltages via mutual induction. These windings are evenly distributed around the inner circumference of the stator and are arranged 120 electrical degrees apart. Three-phase stator windings consist of three separate coils or groups of coils, known as phases, each connected in Y (star) configuration or Delta configuration.
As the rotor...
434
Three-Winding Transformers01:19

Three-Winding Transformers

279
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
279
The Swing Equation01:21

The Swing Equation

532
The Swing Equation is a fundamental tool in power system dynamics, especially for analyzing the behavior of generating units like three-phase synchronous generators. This equation emerges from applying Newton's second law to the rotor of a generator, encompassing factors such as inertia, angular acceleration, and the interplay between mechanical and electrical torques.
In a steady-state operation, the mechanical torque (Τm) supplied to the generator is balanced by the electrical torque...
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Related Experiment Video

Updated: Aug 1, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

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Optimized wake-superposition approach for multiturbine wind farms.

Deshun Li1,2,3, Jixiang Chang1, Gaosheng Ma4,5,6

  • 1College of Energy and Power Engineering, Lanzhou University of Technology, 287 LanGongPing Road, Qilihe District, Lanzhou, 730050, China.

Scientific Reports
|April 24, 2023
PubMed
Summary

This study introduces a corrected sum of squares (SS) model for wind turbine wake superposition, improving accuracy in wind farm layout optimization. The new model precisely quantifies mixed wake velocity deficits, overcoming limitations of previous methods.

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Area of Science:

  • Renewable Energy Engineering
  • Fluid Dynamics
  • Aerospace Engineering

Background:

  • Accurate wind turbine wake distribution is crucial for optimizing wind farm layouts and minimizing wake interference.
  • The sum of squares (SS) model, while considered accurate, overestimates mixed wake velocity deficits, hindering engineering applications.
  • Previous optimization efforts relied on approximate power calculations due to the SS model's limitations.

Purpose of the Study:

  • To address the overestimation issue of the sum of squares (SS) model in wind turbine wake superposition.
  • To develop a more accurate method for quantifying mixed wake velocity deficits in wind farms.
  • To improve the physical understanding and applicability of wake superposition models for engineering optimization.

Main Methods:

  • A univariate linear correction approach was developed, based on the observed linear error increase in the SS method.
  • Unknown coefficients for the correction were determined by fitting experimental data.
  • The corrected model was validated for its ability to quantify full-wake two-dimensional distributions.

Main Results:

  • The proposed linear correction method significantly improves the accuracy of mixed wake velocity deficit quantification.
  • The corrected SS model provides a clearer physical interpretation compared to the original model.
  • Experimental data fitting successfully identified the necessary coefficients for the correction.

Conclusions:

  • The developed univariate linear correction method accurately quantifies the two-dimensional distribution of mixed wakes.
  • This improved model overcomes the limitations of the traditional SS model, enabling more reliable wind farm optimization.
  • The findings contribute to more efficient and effective wind farm design through enhanced wake modeling.