Data Properties Underlying Human Monitoring Performance

Rantanen, Esa M.; Fokoué, Ernest; Gegner, Kathleen M.; Haut, Jacob; Overbye, Thomas J.

doi:10.1177/1541931213601976

Proceedings of the Human Factors and Ergonomics Society Annual

2017

DOI: 10.1177/1541931213601976

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Data Properties Underlying Human Monitoring Performance

Esa M. Rantanen

Ernest Fokoué

Kathleen M. Gegner

et al.

Abstract: The application of phasor measurement unit (PMU) data in the power industry is currently an area of intense interest. The key driver for PMU technology is to use the precise time sources provided by Global Positioning System (GPS) satellites to accurately measure the relative voltage and current phase angles at buses across an interconnect at a very high sampling rate. Presenting PMU data to power system operators in a format that is truly useful for them and that affords improved situation awareness (SA) and … Show more

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Multi-stage fault warning for large electric grids using anomaly detection and machine learning

Raja¹,

Fokoué²

2020

Math. Appl.

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In the monitoring of a complex electric grid, it is of paramount importance to provide operators with early warnings of anomalies detected on the network, along with a precise classification and diagnosis of the specific fault type. In this paper, we propose a novel multi-stage early warning system prototype for electric grid fault detection, classification, subgroup discovery, and visualization. In the first stage, a computationally efficient anomaly detection method based on quartiles detects the presence of a fault in real time. In the second stage, the fault is classified into one of nine predefined disaster scenarios. The time series data are first mapped to highly discriminative features by applying dimensionality reduction based on temporal autocorrelation. The features are then mapped through one of three classification techniques: support vector machine, random forest, and artificial neural network. Finally in the third stage, intra-class clustering based on dynamic time warping is used to characterize the fault with further granularity. Results on the Bonneville Power Administration electric grid data show that i) the proposed anomaly detector is both fast and accurate; ii) dimensionality reduction leads to dramatic improvement in classification accuracy and speed; iii) the random forest method offers the most accurate, consistent, and robust fault classification; and iv) time series within a given class naturally separate into five distinct clusters which correspond closely to the geographical distribution of electric grid buses.

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Multi-stage fault warning for large electric grids using anomaly detection and machine learning

Raja¹,

Fokoué²

2020

Math. Appl.

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show abstract

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Data Properties Underlying Human Monitoring Performance

Cited by 1 publication

References 4 publications

Multi-stage fault warning for large electric grids using anomaly detection and machine learning

Multi-stage fault warning for large electric grids using anomaly detection and machine learning

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