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Using Neural Networks to Detect Line Outages from PMU Data

Ching-pei Lee(ching-pei***at***cs.wisc.edu)
Stephen Wright(swright***at***cs.wisc.edu)

Abstract: We propose an approach based on neural networks and the AC power flow equations to identify single- and double- line outages in a power grid using the information from phasor measurement unit sensors (PMUs). Rather than inferring the outage from the sensor data by inverting the physical model, our approach uses the AC model to simulate sensor responses to all outages of interest under multiple demand and seasonal conditions, and uses the resulting data to train a neural network classifier to recognize and discriminate between different outage events directly from sensor data. After training, real-time deployment of the classifier requires just a few matrix-vector products and simple vector operations. These operations can be executed much more rapidly than inversion of a model based on AC power flow, which consists of nonlinear equations and possibly integer / binary variables representing line outages, as well as the variables representing voltages and power flows. We are motivated to use neural network by its successful application to such areas as computer vision and natural language processing. Neural networks automatically find nonlinear transformations of the raw data that highlight useful features that make the classification task easier. We further consider the problem of classifying outages from sensors placed at only a subset of the buses, describing a principled way to choose sensor locations and showing that highly accurate classification can be achieved from a restricted set of measurements.

Keywords: line outage identification, phasor measurement unit, neural network, optimal PMU placement

Category 1: Applications -- Science and Engineering

Category 2: Nonlinear Optimization

Citation: Technical Report, University of Wisconsin-Madison, October 2017

Download: [PDF]

Entry Submitted: 10/15/2017
Entry Accepted: 10/15/2017
Entry Last Modified: 10/15/2017

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