Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors
Open Access
Abstract
Induction motors (IMs) are widely used in industrial applications due to their advantages over other motor types. However, the efficiency and lifespan of IMs can be significantly impacted by operating conditions, especially Unbalanced Supply Voltages (USV), which are common in industrial plants. Detecting and accurately assessing the severity of USV in real-time is crucial to prevent major breakdowns and enhance reliability and safety in industrial facilities. This paper presented a reliable method for precise online detection of USV by monitoring a relevant indicator, denominated by negative voltage factor (NVF), which, in turn, is obtained using the voltage symmetrical components. On the other hand, impedance estimation proves to be fundamental to understand the behavior of motors and identify possible problems. IM impedance affects its performance, namely torque, power factor and efficiency. Furthermore, as the presence of faults or abnormalities is manifested by the modification of the IM impedance, its estimation is particularly useful in this context. This paper proposed two machine learning (ML) models, the first one estimated the IM stator phase impedance, and the second one detected USV conditions. Therefore, the first ML model was capable of estimating the IM phases impedances using just the phase currents with no need for extra sensors, as the currents were used to control the IM. The second ML model required both phase currents and voltages to estimate NVF. The proposed approach used a combination of a Regressor Decision Tree (DTR) model with the Short Time Least Squares Prony (STLSP) technique. The STLSP algorithm was used to create the datasets that will be used in the training and testing phase of the DTR model, being crucial in the creation of both features and targets. After the training phase, the STLSP technique was again used on completely new data to obtain the DTR model inputs, from which the ML models can estimate desired physical quantities (phases impedance or NVF).Keywords
Funding Information
- Portuguese Foundation for Science and Technology (UIDB/04131/2020, UIDP/04131/2020)
This publication has 25 references indexed in Scilit:
- The Use of a Modified Prony's Method to Detect the Airgap-Eccentricity Occurrence in Induction MotorsIEEE Transactions on Industry Applications, 2016
- Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive dataMechanical Systems and Signal Processing, 2016
- A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang TransformSensors, 2015
- Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural NetworksISA Transactions, 2015
- Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration SignalSensors, 2015
- Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signalsApplied Acoustics, 2015
- The Use of a Modified Prony Method to Track the Broken Rotor Bar Characteristic Frequencies and Amplitudes in Three-Phase Induction MotorsIEEE Transactions on Industry Applications, 2014
- Condition Monitoring and Fault Diagnosis of Electrical Motors—A ReviewIEEE Transactions on Energy Conversion, 2005
- PCA-Based Feature Selection Scheme for Machine Defect ClassificationIEEE Transactions on Instrumentation and Measurement, 2004
- Bearing fault diagnosis based on wavelet transform and fuzzy inferenceMechanical Systems and Signal Processing, 2004