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Automated modeling and implementation of power converters on a real-time FPGA-based emulatorDe Cuyper, Kevin 07 December 2015 (has links) (PDF)
Designing a new power electronic conversion system is a multi-step process that requires the R\&D team(s) to go through an extended prototyping phase whose goal is to validate the design in its nominal state, as well as to test its behavior when it is subjected to abnormal conditions. To properly and safely validate all devices that are external to the power stage itself, such as the controllers and the protection systems, one of the best-suited device is a real-time emulator of the converter circuit, a platform that obeys the same mathematical laws and produces the same signals as the original device withoutactually realizing the power conversion. Unfortunately, these models are often based on analog solvers which are difficult to build, must be redesigned for each modification and are subject to drift and aging. While multiple digital real-time emulators have appeared on the market in the last decades, they typically require powerful and expensive computing platforms to perform their calculations or are not generic enough to emulate the more complex power circuits. In this work, we present a new framework that allows the rapid prototyping of a wide range of power converters by translating a power converter schematic drawn on a computer to a real-time equivalent set of equations which is processed by an FPGA with an emulation time-step of less than one microsecond. Contrary to the previously published works, our tools enable the use of entry-level FPGAs even for the emulation circuits composed of twenty switches or more. This framework takes the form of a tool-chain that starts by extracting the necessary information and a standard description from the initial circuit. However, due to the intricate ways in which the switches and diodes can change their state, this raw information is too complex to be processed and emulated directly.Our first major contribution to the state of the art is a way to automatically analyze these changes in order to reduce the complexity of the problem as much as possible while keeping all the necessary information intact. In this thesis, we develop two tools that are able to find all possible changes in the state of the switches that may appear in the immediate future, thereby reducing the quantity of information required to emulate the circuit. Thanks to the global optimization provided by our tools, simulating a typical AC-to-DC converter composed of 12 switches could require 80\% less resources when compared to existing emulators.To enable the emulation or large power converters, we have created a partitioning method which divides the circuit in multiple sub-circuits which are analyzed and optimized separately. The performances of this partitioning are demonstrated by the emulation of a three-phase three-level converter with a relative error of a less that 5% on the signals.To handle our new framework, a dedicated digital platform has been developed. In order to provide the best results even on small FPGAs, particular attention is given to the low resources usage and the low latency of our design. Through multiple examples, we show that this inexpensive real-time emulation platform is able to accurately emulate many circuits in open- or closed-loop operation with a sampling rate higher than 1 MHz / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished
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COUPLED FINITE ELEMENT AND EXTENDED-QD CIRCUIT MODEL FOR INDUCTION MACHINE ANALYSISAyesha Sayed (9166721) 10 September 2022 (has links)
<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>
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垂直導体のサージ伝搬特性を考慮した風力発電タワー周波数依存回路解析モデル / スイチョク ドウタイ ノ サージ デンパン トクセイ オ コウリョシタ フウリョク ハツデン タワー シュウハスウ イゾン カイロ カイセキ モデル池田 陽紀, Yoki Ikeda 22 March 2015 (has links)
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
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Fault diagnosis of lithium ion battery using multiple model adaptive estimationSidhu, Amardeep Singh 12 1900 (has links)
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.
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Electrochemical model based condition monitoring of a Li-ion battery using fuzzy logicShimoga Muddappa, Vinay Kumar January 2014 (has links)
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.
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