The massive digital information generated in conjunction with the ever-increasing phasor
measurement data in the power grid has led to a tremendous constraint on the analysis and …

Power grid operation continuously undergoes state transitions caused by internal and
external uncertainties, eg, equipment failures and weather-driven faults, among others. This …

With the continuous accumulation of large-scale power grid data, the traditional centralized
data analysis method is more and more expensive for data transmission. Based on this, we …

This paper presents a data-driven approach for event classification via a regional
segmentation of power systems. The data-driven approach is suitable for the complex power …

Edge computing provides an ideal platform to enable many critical and time-sensitive
applications in monitoring and operation of critical cyber-physical systems, such as smart …

Online power system event identification and classification are crucial to enhancing the
reliability of transmission systems. In this paper, we develop a deep neural network (DNN) …

Real-time classification of events facilitates corrective control strategies, supervisory
protection schemes, and on-line transient stability assessment of a power system. The …

In this paper, a novel framework for dynamic event monitoring in smart grid with
synchrophasor data is proposed. The fundamental principle is that the nonstationary …

This paper presents a suite of analytics that are proposed to be embedded in next-
generation smart sensors in electric power grids. The proposed analytics take the electrical …

Due to the proliferation of internet-of-things (IoT)-based technologies in the last several
years, digital computing hardware and software technologies have seen massive …