Induction motors fault diagnosis using machine learning and advanced signal processing techniques

In this thesis, induction motors fault diagnosis are investigated using machine learning and advanced signal processing techniques considering two scenarios: 1) induction motors are directly connected online; and 2) induction motors are fed by variable frequency drives (VFDs). The research is based on experimental data obtained in the lab. Various single- and multi- electrical and/or mechanical faults were applied to two identical induction motors in experiments. Stator currents and vibration signals of the two motors were measured simultaneously during experiments and were used in developing the fault diagnosis method. Signal processing techniques such as Matching Pursuit (MP) and Discrete Wavelet Transform (DWT) are chosen for feature extraction. Classification algorithms, including decision trees, support vector machine (SVM), K-nearest neighbors (KNN), and Ensemble algorithms are used in the study to evaluate the performance and suitability of different classifiers for induction motor fault diagnosis. Novel curve or surface fitting techniques are implemented to obtain features for conditions that have not been tested in experiments. The proposed fault diagnosis method can accurately detect single- or multi- electrical and mechanical faults in induction motors either directly online or fed by VFDs. In addition to the machine learning method, a threshold method using the stator current signal processed by DWT is also proposed in the thesis

Online detection of interturn short-circuit fault in induction motor based on 5th harmonic current tracking using Vold-Kalman filter

In this paper we propose a strategy for real-time detection of interturn short-circuit faults (ISCF) on three-phase induction motor (IM) by using a Vold-Kalman filter (VKF) algorithm. ISCF produce a thermal stress into the stator winding due to large current that flows through the short-circuited turns. Therefore, incipient fault detection is required in order to avoid catastrophic failures such as phase to phase, or phase to ground faults. The strategy is based on an analytical IM model that includes a ISCF fault in any of the phase windings and considering the h-th harmonic in the voltage supply. Based on equivalent electrical circuits with harmonics in sequence components, we propose a strategy for detection of an ISCF on IM by tracking the 5th harmonic current component using a VKF algorithm. The proposed model is experimentally validated using a three-phase IM with modified stator windings to generate ISCF. Also, the IM is feeded by a programmable voltage source to synthesize distorted voltage supply with the 5th harmonic. The results demonstrated that the positive-sequence magnitude for the 5th harmonic current component is a good indicator of the fault severity once it exceeds a threshold limit value, even under load variations and unbalanced voltages

Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors

In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions

Advanced control schemes for wind power plants and renewable energy-based islanded microgrids

Renewable energy sources are increasingly integrated in power grids, creating significant challenges for control and system operation. Among various renewable energy sources, wind power is one of the dominant forms, mainly generated from large-scale transmission-connected wind power plants (WPPs). The grid-connected WPPs are required to follow grid codes to maintain a predefined power factor range under normal operation and supply required reactive power under faulty conditions. To meet grid code requirements, a WPP control architecture is developed in this thesis. The control system consists of a central WPP controller and a local wind turbine generator (WTG) controller, both operate in the voltage control mode. Therefore, the controller can respond faster and is robust to communication failures. Under normal operating conditions, the proposed controller regulates the WPP’s operation within its steady-state reactive power capability and meets the power factor limits. Under faulty conditions, the controller forces the WPP to its maximum capability to contribute more reactive power support to the grid. Two mathematical models representing the steady-state and maximum reactive power capability of the WPP are developed through regression and analytic approaches, respectively. In the second part of the thesis, a model predictive control (MPC)-based distributed generation (DG) controller is proposed to regulate the voltage and frequency at the point of common coupling (PCC) in an islanded microgrid. A data-driven input-output Box-Jenkins polynomial predictive model for DG control is developed using the Gauss-Newton-based nonlinear least square method with the prediction optimization focus. The model inputs are direct- and quadrature-axis components of the control signal, and the model outputs are deviations of the voltage and frequency from their nominal values at the PCC. The proposed MPC controller operates using the PCC data and does not require the microgrid’s central controllers or DG-to-DG communication networks. It can effectively compensate voltage and frequency deviations at the PCC and ensure proportional reactive power sharing among DGs without a secondary controller and a virtual impedance loop. The integrated Kalman filter in the MPC structure enables a robust controller design when subjected to impedance variations and measurement noises