<div>The design of high-performance squirrel-cage induction motors (IMs) entails the capability to predict the motor efficiency map with high accuracy over an operating range. In particular, modeling high-frequency rotor bar currents becomes important for loss analysis in high-speed applications. In theory, it is possible to analyze an IM using time-stepping finite element analysis (TS-FEA); however, this is not viable due to computational limitations. To bridge this gap, we set forth a computationally efficient method to predict the rotor cage loss. This is achieved by coupling magnetostatic FEA with an extended qd-circuit model of the cage. The circuit model is derived in a synchronously rotating reference frame. The proposed IM model includes the effects of saturation, winding and slot harmonics, as well as nonuniform current distribution in the rotor bars. The proposed model is validated by comparing the estimated cage loss and computational effort against a 2-D nonlinear TS-FEA. </div><div>The proposed electromagnetic model is finally coupled to a linear thermal model to predict IM performance over an operating range and the results are validated using experiments. The proposed model is further extended to identify detailed flux density waveforms in the iron to estimate core loss. The flux density waveforms are obtained by conducting a set of magnetostatic FEA studies using the derived rotor bar currents.</div>

風力発電システムは、現在世界中で普及しているが、その地上高と立地条件からしばしば落雷の被害を受け、稼働率の低下が問題視されている。本論文は、垂直導体である風力発電タワーにおける雷サージ解析の高精度化、高速化を目的とした垂直導体回路解析モデルの提案、およびその有用性のについて述べるとともに、風力発電所や洋上風車への拡張性についてまとめたものである。 / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.

Indiana University-Purdue University Indianapolis (IUPUI) / There is a strong urge for advanced diagnosis method, especially in high power battery packs and high energy density cell design applications, such as electric vehicle (EV) and hybrid electric vehicle segment, due to safety concerns. Accurate and robust diagnosis methods are required in order to optimize battery charge utilization and improve EV range. Battery faults cause significant model parameter variation affecting battery internal states and output. This work is focused on developing diagnosis method to reliably detect various faults inside lithium-ion cell using electrochemical model based observer and fuzzy logic algorithm, which is implementable in real-time. The internal states and outputs from battery plant model were compared against those from the electrochemical model based observer to generate the residuals. These residuals and states were further used in a fuzzy logic based residual evaluation algorithm in order to detect the battery faults. Simulation results show that the proposed methodology is able to detect various fault types including overcharge, over-discharge and aged battery quickly and reliably, thus providing an effective and accurate way of diagnosing li-ion battery faults.