Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors
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. Det...
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7989 |
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| author | Khaled Laadjal Acácio M. R. Amaral Mohamed Sahraoui Antonio J. Marques Cardoso |
| author_facet | Khaled Laadjal Acácio M. R. Amaral Mohamed Sahraoui Antonio J. Marques Cardoso |
| author_sort | Khaled Laadjal |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-10T22:01:42Z |
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| language | English |
| last_indexed | 2024-03-10T22:01:42Z |
| publishDate | 2023-09-01 |
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| series | Sensors |
| spelling | doaj.art-b8eac0cdda56488080e20bf1ab482d1e2023-11-19T12:57:05ZengMDPI AGSensors1424-82202023-09-012318798910.3390/s23187989Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction MotorsKhaled Laadjal0Acácio M. R. Amaral1Mohamed Sahraoui2Antonio J. Marques Cardoso3CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, PortugalCISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, PortugalCISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, PortugalCISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, PortugalInduction 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).https://www.mdpi.com/1424-8220/23/18/7989three-phase IMsunbalanced supply voltage (USV)voltage negative factor (VNF)fortescue transform (FT)short time least square Prony’s method (STLSP)impedance estimation |
| spellingShingle | Khaled Laadjal Acácio M. R. Amaral Mohamed Sahraoui Antonio J. Marques Cardoso Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors Sensors three-phase IMs unbalanced supply voltage (USV) voltage negative factor (VNF) fortescue transform (FT) short time least square Prony’s method (STLSP) impedance estimation |
| title | Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors |
| title_full | Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors |
| title_fullStr | Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors |
| title_full_unstemmed | Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors |
| title_short | Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors |
| title_sort | machine learning based method for impedance estimation and unbalance supply voltage detection in induction motors |
| topic | three-phase IMs unbalanced supply voltage (USV) voltage negative factor (VNF) fortescue transform (FT) short time least square Prony’s method (STLSP) impedance estimation |
| url | https://www.mdpi.com/1424-8220/23/18/7989 |
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