Virgilio Centeno

Imtiaj Khan, Virgilio Centeno

Introduction of PMUs to cyber-physical system pro-vides accurate data acquisition, while posing additional risk of being the victim of cyber attack. Both False Data Injection Attack (FDIA) and GPS-spoofing or timing attack can provide malicious data to the cyber system, though these two attacks require different post-attack contingency plan. Thus accurate detection of timing attack and separating it from conventional FDIA has become a very important research area. In this article, a successful detection of timing attack mechanism is proposed. Firstly, a method to distinguish timing attack and FDIA using unwrapped phase angle data is developed. Secondly, utilizing low rank Henkel matrix property to differentiate timing attack from electrical events is also presented. Finally, an experimental validation of proposed model is performed on IEEE 13 bus system using simulated GPS-spoofing attack. It can be observed that the timing attack can increase the rank 1 approximation error of Henkel matrix of unwrapped angles by 700% for 3 sec delay in GPS time-stamp. The rank 1 approximation error is increased by 500% for 2 sec delay and the increase is insignificant for 1sec delay attack. FDIA doesn't show any significant change in the low rank approximation profile of Henkel matrix.

Iman Niazazari, Amir Ghasemkhani, Yunchuan Liu et al.

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.

Shuchismita Biswas, Rounak Meyur, Virgilio Centeno

Frequently recurring transient faults in a transmission network may be indicative of impending permanent failures. Hence, determining their location is a critical task. This paper proposes a novel image embedding aided deep learning framework called DeVLearn for faulted line location using PMU measurements at generator buses. Inspired by breakthroughs in computer vision, DeVLearn represents measurements (one-dimensional time series data) as two-dimensional unthresholded Recurrent Plot (RP) images. These RP images preserve the temporal relationships present in the original time series and are used to train a deep Variational Auto-Encoder (VAE). The VAE learns the distribution of latent features in the images. Our results show that for faults on two different lines in the IEEE 68-bus network, DeVLearn is able to project PMU measurements into a two-dimensional space such that data for faults at different locations separate into well-defined clusters. This compressed representation may then be used with off-the-shelf classifiers for determining fault location. The efficacy of the proposed framework is demonstrated using local voltage magnitude measurements at two generator buses.