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Daniel Douglas, RPI, and Zhe Yang, UTK, Give CURENT Power and Energy Seminar on February 14

Daniel Douglas, Rensselaer Polytechnic Institute, and Zhe Yang, University of Tennessee, will give the CURENT Power and Energy Seminar on February 14 from 12:20 pm to 1:10 pm in MHK 404.  Seminar is available for remote viewing through ZOOM (info at bottom of page). 

Presenters: Daniel Douglas, Rensselaer Polytechnic Institute, and Zhe Yang, University of Tennessee

Time: Friday, February 14, 12:20 PM - 1:10 PM EST

Location: Min H. Kao Building, Room 404


Thévenin Equivalent Prediction of Real-World PMU Data using ThevNN Method

An on-line Thévenin estimation method was developed to periodically compute the Thévenin equivalent voltage and reactance of a power system at regular intervals or on demand using measured values. The method was validated with simulated PMU data and ten days of 24-hour SCADA data at a wind hub on a medium voltage transmission system in the U.S. western interconnection. In this paper, the method has been further validated using two weeks of real-world PMU ambient data from a high-voltage substation in the U.S. eastern interconnection. Further, using the optimally estimated Thévenin values as a multivariate time-series target, a Long Short Term Memory Recursive Neural Network (LSTM-RNN) is trained toward performing automatic Thévenin equivalent prediction from measured PMU data in real-time. As a verification, values predicted by the ThevNN model are used in the back-calculation of the bus voltage, which is then compared with the measured bus voltage using the normalized root-mean-square error.

Bio: Daniel Douglas is a PhD student in the Electrical, Computer, and Systems Engineering department at Rensselaer Polytechnic Institute. His CURENT research focuses on applied machine learning and power system generation, operation, and control.

Presenter: Zhe Yang, University of Tennessee

 Loss Modeling for GaN-based Inverter

This talk will present the study of converter loss discrepancy between calculation and testing results by considering five factors: impacts of parasitic capacitance on switching loss, parameter variation of devices, time-varying power dissipation and junction temperature, thermal modeling using Finite Element Analysis, and some practical issues of the passive components. A 4.5 kW hard-switching inverter prototype using GaN devices was used as an example to demonstrate the impacts of each factor, and the improvement of loss model. The results show that after considering above factors, loss discrepancy reduces from 35.7 W (35%) to 1.4 W (4%) at heavy load and, from 3.9 W (28%) to 2.6 W (less than 16%) at light load.

Zhe Yang received the Bachelor’s and Master’s degrees in electrical engineering from University of Hong Kong, in 2014 and 2016, respectively. He is working toward a Ph.D. degree in electrical engineering at CURENT, University of Tennessee, Knoxville. His current research interests include converter design and optimization, and application of wide bandgap devices.


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