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Rashkin, Lee Joshua

Publications and source records attributed to Rashkin, Lee Joshua.

At least 19 records

Industrial PLC Network Modeling and Parameter Identification Using Sensitivity Analysis and Mean Field Variational Inference

A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables (three-phase conductors and a ground conductor) and has several load types, including motor loads. The model is calibrated to data using mean field variational inference with a sensitivity analysis to reduce the parameter space. The results show that the inference method can accurately identify many of the model parameters, and the model is accurate even when the network is modified.

47 OTHER INSTRUMENTATION↗

Excitation control of dual-wound machines for coupling mitigation

A dual-wound machine comprises a dual-wound generator supplying power to two separate powered zones. The generator comprises a wound rotor with a field winding and a stator with two sets of phase windings and a field control loop that controls the excitation voltage applied to the field winding and therefore the magnetic field produced by the rotor, in order to maintain a constant field flux in the generator and mitigate dynamic coupling between the two sets of phase windings when supplying power to unbalanced loads.

Rashkin, Lee Joshua↗

Passive high-power-density grid interface device

A hybrid switched capacitor power converter for high-power applications is provided. The converter has a transistor-switched input-stage boost converter followed by a capacitor-and-diode ladder circuit. The converter is adapted to produce an output voltage of at least 5 kV at a power level of at least 0.5 kW. The ladder circuit includes one or more multi-stage rails.

Brocato, Robert W.↗

Co-Optimization of Boost Converter Reliability and Volumetric Power Density [Brief]

Optimized designs were achieved using a genetic algorithm to evaluate multi-objective trade space, including Mean-Time-Between-Failure (MTBF) and volumetric power density. This work provides a foundational platform that can be used to optimize additional power converters, such as an inverter for the EV traction drive system as well as trade-offs in thermal management due to the use of different device substrate materials.

33 ADVANCED PROPULSION SYSTEMS↗