Affiliation:
1. CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal
2. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes—Quinta da Nora, P-3030-199 Coimbra, Portugal
3. GEB Laboratory, Department of Electrical Engineering, Mohamed Khider University, Biskra 07000, Algeria
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).
Funder
Portuguese Foundation for Science and Technology
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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