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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

99
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...
99
Turbine-Governor Control01:17

Turbine-Governor Control

159
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...
159
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

176
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.
In this model, each generator is connected to a...
176
Generator Voltage Control01:21

Generator Voltage Control

117
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,...
117
Multimachine Stability01:25

Multimachine Stability

138
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:
138
Three-Winding Transformers01:19

Three-Winding Transformers

191
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...
191

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Related Experiment Video

Updated: May 31, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental

Montaser Abdelsattar1, Mohamed A Ismeil2, Karim Menoufi3

  • 1Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena, Egypt.

Plos One
|January 23, 2025
PubMed
Summary

Deep learning models, particularly Artificial Neural Networks (ANN), slightly outperform machine learning models like Extra Trees (ET) in predicting wind turbine power output. This advancement offers improved accuracy for renewable energy optimization.

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Area of Science:

  • Renewable Energy Systems
  • Computational Intelligence
  • Environmental Engineering

Background:

  • Accurate wind turbine power output prediction is crucial for grid integration and energy management.
  • Machine learning (ML) and deep learning (DL) offer promising approaches for enhancing forecasting accuracy.

Purpose of the Study:

  • To conduct a comprehensive comparative analysis of various ML and DL models for wind turbine power output prediction.
  • To evaluate model performance using key metrics such as R-squared, MAE, and RMSE.
  • To identify the most effective algorithms for forecasting wind energy generation.

Main Methods:

  • Evaluated a diverse set of ML models including Linear Regression, Support Vector Regressor, Random Forest, Extra Trees, Adaptive Boosting, Categorical Boosting, Extreme Gradient Boosting, and Light Gradient Boosting Machine.
  • Assessed deep learning models such as Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN).
  • Utilized a dataset of 40,000 observations and applied preprocessing techniques like feature scaling and parameter tuning.

Main Results:

  • Extra Trees (ET) demonstrated the highest performance among ML models, achieving an R-squared of 0.7231 and RMSE of 0.1512.
  • Artificial Neural Network (ANN) showed the best performance among DL models, with an R-squared of 0.7248 and RMSE of 0.1516.
  • DL models, especially ANN, exhibited slightly superior performance, indicating better capability in modeling non-linear dependencies.

Conclusions:

  • Deep learning models, particularly ANN, offer enhanced predictive accuracy for wind turbine power output compared to top-performing ML models.
  • The study highlights the potential of advanced computational methods for optimizing renewable energy systems.
  • Preprocessing techniques significantly contribute to improving model performance and forecasting accuracy.