Simulation based testing and performance investigation of induction motor drives using matlab simulink

Makinde, Kehinde Adeleye; Bakare, Mutiu Shola; Akinloye, Benjamin O.; Amole, Abraham Olatide; Adewuyi, Oludamilare Bode; Zubair, Uthman Oluwatosin; Owonikoko, Waheed Olaide

doi:10.1007/s42452-023-05296-w

Cited by 4 publications

(1 citation statement)

References 35 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach offers a cost-effective and efficient means of generating multiple instances of DTC induction motors across varying operating conditions [6]. It serves to address the limitations that deep learning models may encounter when confronted with constrained or fluctuating training data and specific operational scenarios [7][8][9]. However, achieving accurate signal forecasting demands the development of models that intricately replicate the actual dynamics of the motor system.…”

Section: Introductionmentioning

confidence: 99%

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory

Le,

Oktian,

et al. 2023

Sensors

View full text Add to dashboard Cite

Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

show abstract

Section: Introductionmentioning

confidence: 99%

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory

Le,

Oktian,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Improvement of education in the field of marine engineering at the Faculty of Maritime Studies Kotor

Mrdović,

Pudar,

Knežević

et al. 2023

2023 10th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)

View full text Add to dashboard Cite

Time Series Electrical Motor Drives Forecasting based on Simulation Modeling and Bidirectional Long-Short Term Memory

Le¹,

Oktian²,

Jo³

et al. 2023

Preprint

View full text Add to dashboard Cite

Accurately predicting electrical signals of three-phase Direct Torque Control (DTC) induction motors is crucial for optimal motor performance and effective condition monitoring. However, multiple DTC motors’ complexity and operating conditions’ variations pose challenges for traditional prediction methods. To overcome these challenges, we propose an innovative approach that combines Fast Fourier Transform (FFT) to transform electrical motor simulation data and a Bi-directional Long Short-Term Memory (Bi-LSTM) network to forecast processed motor data. By transforming the DTC induction motor signals from the time domain to the frequency domain, we enhance the capability of learning models to capture subtle differences and generate various input features. The Bi-LSTM model effectively captures both forward and backward dependencies in time series data, enabling accurate prediction of electrical signals. We evaluate the proposed approach using our simulated dataset and compare the proposed method to a state-of-the-art model, the Gated Recurrent Unit (GRU), demonstrating its effectiveness in improving the accuracy and reliability of induction motor signals forecasting. The finding provides valuable insights for advancing motor control and operation in industrial applications.

show abstract

Simulation based testing and performance investigation of induction motor drives using matlab simulink

Cited by 4 publications

References 35 publications

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory

Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory

Improvement of education in the field of marine engineering at the Faculty of Maritime Studies Kotor

Time Series Electrical Motor Drives Forecasting based on Simulation Modeling and Bidirectional Long-Short Term Memory

Contact Info

Product

Resources

About