Design and performance evaluation of different power pad topologies for electric vehicles wireless charging systems

Range limitations and charging of electric vehicles (EVs) are major concerns in the modern electrified transportation systems. In this thesis, design and performance analysis of three power pads considered for EV’s wireless charging systems are carried out. In particular, a comparative performance analysis is conducted for circular and double D (DD) power pads, and a new power pad named DDC power pad is designed by combining these two power pads. Wireless charging systems of EV’s are developed, mainly based on the inductive power transfer (IPT) system where the power is transferred through electromagnetic induction. A systematic approach to power pad design is presented in detail and the Society of Automotive Engineers (SAE) recommended practice J2954 is followed for designing the physical dimension of these power pads. To this end, Finite Element Analysis (FEA) tool ANSYS Maxwell 3D is used for simulation. Extensive simulation studies are carried out to verify the efficiency of the proposed DDC power pad. It is found that the proposed DDC power pad offers significantly improved performance compared to the existing circular and DD power pads under various misaligned positions

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

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