38 citations · 42 across the 4 of their papers we have counts for
5 papers · 1 filter
Early Anomaly Detection in Power Systems Based on Random Matrix Theory
Xin Shi, Robert Qiu
It is important for detecting the anomaly in power systems before it expands and causes serious faults such as power failures or system blackout. With the deployments of phasor mea…
Short-term Electric Load Forecasting Using TensorFlow and Deep Auto-Encoders
Xin Shi
This paper conducts research on the short-term electric load forecast method under the background of big data. It builds a new electric load forecast model based on Deep Auto-Encod…
Improving Power System State Estimation Based on Matrix-Level Cleaning
Haosen Yang, Robert C. Qiu, Lei Chu +3
Power system state estimation is heavily subjected to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. Trad…
Spatio-Temporal Correlation Analysis of Online Monitoring Data for Anomaly Detection and Location in Distribution Networks
Xin Shi, Robert Qiu, Zenan Ling +3
The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal co…
Unsupervised Feature Learning for Online Voltage Stability Evaluation and Monitoring Based on Variational Autoencoder
Haosen Yang, Robert C. Qiu, Xin Shi +1
With the increase of uncertain elements in power systems and extensive deployment of online monitoring devices, it is necessary to search a more real-time and robust voltage stabil…