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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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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Related Experiment Video

Updated: Jul 16, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term

Thi-Thu-Huong Le1,2, Yustus Eko Oktian1,2, Uk Jo3

  • 1Blockchain Platform Research Center, Pusan National University, Busan 609735, Republic of Korea.

Sensors (Basel, Switzerland)
|September 9, 2023
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Summary
This summary is machine-generated.

This study introduces a novel method using Fast Fourier Transform (FFT) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks for accurate electrical signal forecasting in Direct Torque Control (DTC) induction motors, improving performance and monitoring.

Keywords:
Bi-LSTMFFTdeep learningfrequency domainsignal processingsimulation modelingthree-phase DTC induction motortime series forecasting

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Accurate electrical signal forecasting in three-phase Direct Torque Control (DTC) induction motors is vital for performance optimization and condition monitoring.
  • Conventional prediction methods face challenges due to the complexity of DTC motors and operational variability.

Purpose of the Study:

  • To develop an innovative forecasting approach for electrical signals in DTC induction motors.
  • To enhance the precision and reliability of motor signal predictions.

Main Methods:

  • Preprocessing simulation data using Fast Fourier Transform (FFT).
  • Utilizing a Bidirectional Long Short-Term Memory (Bi-LSTM) network for signal forecasting.
  • Comparative analysis against Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models.

Main Results:

  • The proposed FFT-Bi-LSTM approach demonstrated superior performance in forecasting induction motor signals.
  • Achieved low Mean Absolute Error (MAE) of 92.6864 for stator current and 93.8802 for rotor current.
  • Exhibited reduced prediction loss with Root Mean Square Error (RMSE) averages of 105.0636 and 85.7820.

Conclusions:

  • The FFT-Bi-LSTM method significantly improves the accuracy and reliability of electrical signal forecasts for DTC induction motors.
  • This approach offers a robust solution for complex motor control systems.
  • The findings highlight the potential for advanced AI techniques in motor diagnostics and prognostics.