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Advancement: Yue Ma 

Speaker Name: 
Yue Ma
Speaker Title: 
PhD Student
Speaker Organization: 
Electrical Engineering PhD
Start Time: 
Monday, November 30, 2020 - 12:30pm
End Time: 
Monday, November 30, 2020 - 1:30pm
Location: 
Zoom - https://ucsc.zoom.us/j/99320520276?pwd=cVNqaWpmUEtMeFkxMERESlpKaW5QZz09 - Passcode: 123456

Abstract: Extensive deployment of power electronics loads in naval ship power systems indicate the ship electrification is inevitable in future trends. Next generation warships require high power density weapons drawing pulse power from dc grid. A particularly concerning issue is that these pulse loads draw large currents in short periods of time similar to fault behavior; and may be indiscernible from a fault. This project will propose three novel machine/deep learning based algorithms, including 1) long short-term memory recurrent neural network based autoencoder neural networks, 2) data-driven clustering based machine learning Approach and 3) undecided machine/deep learning approach to detect dc faults and monitor load conditions applied to naval pulse loads. Two feature extraction methods are also implemented including short-time Fourier transform and wavelet transform. The novel load monitoring solution presented herein can be applied to any load profile that exhibits repetitive transients during normal operation. The frequency-domain features of the load current are extracted for the network training to set the network weights and biases. Once the network training is completed, the machine/deep learning approach will predict both signal classification and fault identifications. Finally the method is demonstrated in naval dual zonal power system experimentally.

Event Type: 
Adancement/Defense
Advisor: 
Keith Corzine
Graduate Program: 
Electrical Engineering PhD