2020/08/24 by Iman Niazazari, Niazazari, Iman, Amir Ghasemkhani +11
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Power System Optimization and Stability #Power Systems Fault Detection #Signal Processing (eess.SP) #Smart Grid and Power Systems #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.10151
openalex publication_date 2020/08/24 · openalex created_date 2020/09/01 · openalex updated_date 2026/07/28
This paper presents a wide-area event classification in transmission power grids. The deep neural network (DNN) based classifier is developed based on the availability of data from time-synchronized phasor measurement units (PMUs). The proposed DNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset of the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency event, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The simulation results show that ROCOF as input feature gives the best classification performance. In addition, it is shown that the classifier trained with higher sampling rate PMUs and a larger dataset has higher accuracy.