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Plug-In Hybrid Vehicles -- A Vision For The Future, Mehdi Ferdowsi
Plug-In Hybrid Vehicles -- A Vision For The Future, Mehdi Ferdowsi
Electrical and Computer Engineering Faculty Research & Creative Works
One of the unique advantages of plug-in hybrid vehicles is their capability to integrate the transportation and electric power generation sectors in order to improve the efficiency, fuel economy, and reliability of both systems. This goal is performed via integration of the onboard energy storage units of plug-in vehicles with the power grid by power electronic converters and communication systems. Employing energy storage systems improves the efficiency and reliability of the electric power generation, transmission, and distribution. Similarly, combining an energy storage system with the power train of a conventional vehicle results in a hybrid vehicle with higher fuel efficiency. …
Early Time Charge Replenishment Of The Power Delivery Network In Multi-Layer Pcbs, Giuseppe Selli, Matteo Cocchini, James L. Knighten, Bruce Archambeault, Jun Fan, Samuel R. Connor, Antonio Orlandi, James L. Drewniak
Early Time Charge Replenishment Of The Power Delivery Network In Multi-Layer Pcbs, Giuseppe Selli, Matteo Cocchini, James L. Knighten, Bruce Archambeault, Jun Fan, Samuel R. Connor, Antonio Orlandi, James L. Drewniak
Electrical and Computer Engineering Faculty Research & Creative Works
The investigation of decoupling issues has been extensively treated in the literature in both the frequency and the time domain [1-9]. The two domains describe from different perspectives the same physical phenomenon, being related by a Fourier transform. In this article, well known decoupling issues usually addressed in the frequency domain [1,2] are discussed in the time domain. Moreover, some modeling issues related to the cavity model approach are discussed and, in particular, the circuit extraction feature associated with this methodology is utilized throughout the article to carry out the time domain simulations within a SPICE based-tool. The depletion of …
Digital Ripple Correlation Control For Photovoltaic Applications, Jonathan W. Kimball, Philip T. Krein
Digital Ripple Correlation Control For Photovoltaic Applications, Jonathan W. Kimball, Philip T. Krein
Electrical and Computer Engineering Faculty Research & Creative Works
Ripple correlation control (RCC) is a fast, robust online optimization technique. RCC is particularly suited for switching power converters, where the inherent ripple provides information about the system operating point. The present work examines a digital formulation that has reduced power consumption and greater robustness. A maximum power point tracker for a photovoltaic panel demonstrates greater than 99% tracking accuracy and fast convergence.
A Novel Impedance Measurement Technique For Power Electronic Systems, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
A Novel Impedance Measurement Technique For Power Electronic Systems, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
Electrical and Computer Engineering Faculty Research & Creative Works
When designing and building power systems that contain power electronic switching sources and loads, system integrators must consider the frequency-dependent impedance characteristics at an interface to ensure system stability. Stability criteria have been developed in terms of source and load impedance for both dc and ac systems and it is often necessary to measure system impedance through experiments. Traditional injection-based impedance measurement techniques require multiple online tests which lead to many disadvantages. The impedance identification method proposed in this paper greatly reduces online test time by modeling the system with recurrent neural networks. The recurrent networks are trained with measured …
Singular Perturbation Theory For Dc-Dc Converters And Application To Pfc Converters, Jonathan W. Kimball, Philip T. Krein
Singular Perturbation Theory For Dc-Dc Converters And Application To Pfc Converters, Jonathan W. Kimball, Philip T. Krein
Electrical and Computer Engineering Faculty Research & Creative Works
Many control schemes for dc-dc converters begin with the assertion that inductor currents are "fast" states and capacitor voltages are "slow" states. This assertion must be true for power factor correction (PFC) converters to allow independent control of current and voltage. In the present work, singular perturbation theory is applied to boost converters to provide rigorous justification of the time scale separation. Krylov-Bogoliubov-Mitropolsky (KBM) averaging is used to include switching ripple effects. A relationship between inductance, capacitance, load resistance, and loss resistances derives from an analysis of an approximate model. Similar results hold for buck and buck-boost converters. An experimental …
Impedance Identification Of Integrated Power System Components Using Recurrent Neural Networks., Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
Impedance Identification Of Integrated Power System Components Using Recurrent Neural Networks., Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
Electrical and Computer Engineering Faculty Research & Creative Works
Impedance characteristics of shipboard power systems provide important information for studies on system stability and integration. Existing injection based impedance measurement techniques require multiple tests on the system to obtain characteristics over wide frequency ranges. In this paper, recurrent neural networks (RNNs) are used to model the small signal dynamics of power electronic systems based on a single test in which randomized signals are injected into the system. The trained RNN is then used to extract the small-signal impedances/admittances of the system. A number of tests have been carried out in simulation to verify the effectiveness of the proposed method.
Combined Training Of Recurrent Neural Networks With Particle Swarm Optimization And Backpropagation Algorithms For Impedance Identification, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
Combined Training Of Recurrent Neural Networks With Particle Swarm Optimization And Backpropagation Algorithms For Impedance Identification, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
Electrical and Computer Engineering Faculty Research & Creative Works
A recurrent neural network (RNN) trained with a combination of particle swarm optimization (PSO) and backpropagation (BP) algorithms is proposed in this paper. The network is used as a dynamic system modeling tool to identify the frequency-dependent impedances of power electronic systems such as rectifiers, inverters, and DC-DC converters. As a category of supervised learning methods, the various backpropagation training algorithms developed for recurrent neural networks use gradient descent information to guide their search for optimal weights solutions that minimize the output errors. While they prove to be very robust and effective in training many types of network structures, they …