Rob Hovsapian

Bang L. H. Nguyen, Tuyen Vu, Thai-Thanh Nguyen et al.

This paper presents a 1-D convolutional graph neural network for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined recurrent graph convolutional neural networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The achievable accuracy of 99.27%, 98.1%, 98.75%, and 95.6% for fault detection, fault type classification, fault phase identification, and fault location respectively.

Yusheng Luo, Sanjeev Srivastava, Manish Mohanpurkar et al.

Medium voltage direct-current based integrated power system is projected as one of the solutions for powering the all-electric ship. It faces significant challenges for accurately energizing advanced loads, especially the pulsed power load, which can be rail gun, high power radar, and other state of art equipment. Energy storage based on supercapacitors is proposed as a technique for buffering the direct impact of pulsed power load on the power systems. However, the high magnitude of charging current of the energy storage can pose as a disturbance to both distribution and generation systems. This paper presents a fast switching device based real time control system that can achieve a desired balance between maintaining the required power quality and fast charging the energy storage in required time. Test results are shown to verify the performance of the proposed control algorithm.

Yusheng Luo, Min Xian, Manish Mohanpurkar et al.

Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicles in future taxi industry. The cost of hydrogen production is minimized by utilizing the proposed forecasting tool to reduce the hydrogen produced during high cost on-peak hours and guide hydrogen producer to store sufficient hydrogen during low cost off-peak hours.