SEKE 2012 Proceedings - Knowledge Systems Institute

Forecasting Fault Events in Power Distribution Grids
Using Machine Learning
Aldo Dagnino and Karen Smiley
ABB Corporate Research
Industrial Software Systems
Raleigh, NC, USA
aldo.dagnino@us.abb.com
karen.smiley@us.abb.com
Lakshmi Ramachandran
North Carolina State University
Department of Computer Science
Raleigh, NC. USA
lramach@ncsu.edu
Abstract— Fault events in distribution grids and substations can
cause costly power outages. Forecasting these fault events can
reduce response time and enhance preparedness to repair the
outage, which result in significant cost savings. Identification of
fault events in distribution grids has been mostly a reactive and
manual process with a relatively low level of automation. For this
reason, any tools that can automate the diagnostics or prediction
of fault events in the grid are welcome in the industry. The
objective of the investigation presented in this paper is to develop
machine-learning models capable of predicting fault events and
their location in power distribution grids. Data related to
historical fault events, grid electrical values, types of
infrastructure, and historical weather were combined to create
the forecasting models. A variety of machine learning algorithms
such as Neural Networks, Support Vector Machines, Recursive
Partitioning, and Naïve Bayes were utilized to create the machine
learning models. Neural Network models performed best at
forecasting fault events given certain weather conditions, and
identifying the specific grid zone where a fault occurred. The
Recursive Partitioning models were better at forecasting the
substation and feeder where a fault occurred. An implementation
at a US utility was prototyped to demonstrate the forecasting
capabilities of these models.
Keywords - machine learning, data mining, fault events,
forecasting, power distribution grids, substations, weather.
I. INTRODUCTION
The electrical power utilized in cities, factories, office
buildings, industries, and housing is p roduced in power
generating stations or power plants. Such generating stations
are conversion facilities where heat energy from fuel (coal, oil,
gas, or uranium) and sun, as well as mechanical energy from
wind, or falling water is converted into electricity. The
transmission system transports electricity in large quantities
from generating stations to the consumption areas. Electric
power delivered by transmission circuits and power lines must
be “stepped-down” in facilities called substations, to voltages
more suitable for use in industries, buildings, and residential
areas. The segment of the electric power system that takes
power from a bulk-power substation to consumers, commonly
about 35% of the total plant investment, is called the
distribution system, and includes the power distribution grid,
electrical equipment, and the substations. Based on ABB’s
experience, it is estimated that over half of the power
transmission and distribution infrastructure in the US and other
parts of the western world is over 50 years old. A key issue
currently facing Utilities is to efficiently distribute their limited
maintenance and r epair funding. Studies in th e UK show that
more than 70% of unplanned customer minutes lost of
electrical power are due to problems in the distribution grid
caused by deterioration or weather [8]. According to a survey
conducted by the Lawrence Berkeley National Laboratory,
power outages or interruptions cost the US around $80 billion
annually [7]. Prediction of fault events in distribution grids is
an important capability that helps utilities to reduce outage
costs. For this reason, developing models that can forecast
these fault events and their locations is h ighly desirable. In
many utilities, distribution grid operators rely only on manual
methods and reactive approaches to diagnose outages, and very
limited fault forecasting methods. This makes the dispatching
of repair crews a slow process that can be highly improved by
utilizing more automated diagnostic and fault forecasting
capabilities.
II. SOURCES OF FAULTS IN POWER DISTRIBUTION GRIDS
There are many factors identified in the literature that can
cause fault events in a distribution grid [2] [3] [4] [6] [9] [11]
[12] [14]. Fig 1 presents a summary of these factors.
Figure 1. Factors that can contribute to faults in power distribution grids
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