IEEE Power & Energy Magazine - May/June 2018 - 32

Since 2015, several µPMU devices have been installed at pilot
test sites in the state of California, including multiple 12.47-kV
test feeders in the city of Riverside.

actions. for instance, in example 4, the action to reduce
transformer load was taken quickly, without major operator or field crew intervention, and the transformer was then
flagged for repair. such proactive approaches help prevent
consumer interruptions, enable utilities to repair rather
than run to failure, and minimize field crew dispatch time.

Unlocking the Power of Big Data

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Group 2

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from examples 1-5, it is clear that there is an enormous
amount of information to extract from even a small number of
µPMus, as long as one can zoom in on, detect, and scrutinize
each event within its millisecond time scale. In particular,
one can expand the core ideas presented by these examples
to remotely monitor hundreds of pieces of grid equipment,
assets, distributed energy resources, inverters, and loads on
each feeder, thus building the key to true situational awareness in power distribution systems.
however, the main challenge is to go beyond manual methods based on the intuition and heuristics of human experts,
such as those described in examples 1-5. Instead, it is crucial
to develop the machine intelligence needed to automate and
scale up the analytics on billions of µPMu measurements
and terabytes of data on a daily basis and in real time. In this
section, we make the case that big data analytics (Bda) is
the key to addressing the challenges in working with µPMu
measurements and so turn the data into actionable insights in
a scalable fashion.
Bda is the process of examining big data to uncover hidden patterns, unknown correlations, customer preferences,

population behaviors, incipient failures, operation irregularities, and other useful data-driven intelligence. In power systems, the findings from Bda may lead to relieving threats;
preventing, predicting, or responding more quickly to faults;
improved efficiency; new revenue opportunities; and better
customer service.
significant technical advances have recently been made
within the area of Bda in the form of new predictive and
forecasting techniques; data mining and machine learning
tools to enhance classification, regression, clustering, and
dimension reduction; artificial intelligence to enable cognitive simulation, expert systems, and perception; statistical
analysis; and advanced data visualization. as computational
power grows and is potentially distributed out to the grid
edge, the analytics field will expand and enable the modernized power distribution grid to develop.
the first, and most important, Bda application in the
context of this article is diagnosis and prognosis based on
µPMu data in an automated fashion using machine intelligence-as opposed to case by case or even manually, as
is currently the norm in this field. for instance, recall from
example 3 that filtering out the voltage transient events
caused at the transmission level is a critical task that must
be performed continuously to allow a focus on events that
may indicate any potential anomaly in terms of grid equipment and customer loads on the distribution feeder of interest. therefore, the visual classification approach discussed
in the "descriptive analytics" section has limited use in a
real-world application. fortunately, one can use proper

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figure 6. A statistical analysis of synchronized transient voltage events on two neighboring feeders: (a) phase A, (b) phase
b, and (c) phase C.
32

ieee power & energy magazine

may/june 2018



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2018

Contents
IEEE Power & Energy Magazine - May/June 2018 - Cover1
IEEE Power & Energy Magazine - May/June 2018 - Cover2
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