| solar deployment advances
The University of New Mexico (UNM) and Sandia National Laboratories are collaborating in ground- breaking research to apply artificial intelligence
techniques to improve the reliability of renewable
energy generators, a growing and critical need for American utilities.
The goal is to provide smoother balancing of power
sources with loads, as intermittent sources (wind, solar and
back-up generators) cycle in and out of the grid — plus
early warning of status changes for all sources.
When a small commercial or residential system with a
backup generator suffers
a mechanical or electrical failure, the backup
power can mask the fault
condition. If the owner is
not carefully monitoring
the renewable system, it
may remain off-line for a
Utilities are also
concerned. They want
to know how long they
can rely on these renewable systems to operate
because if the small generators fail, they have a legal
duty to supply energy.
Artificial intelligence techniques, such
as adaptive resonance
theory (ART), hold promise to address these issues. ART
algorithms have the rudimentary capability to learn in the
university of New Mexico researchers (left to right) Hongbo He,
Andrea Mammoli, Dave Menicucci and Tom Caudell have demonstrated the Solar Hot Water Reliability Testbed. Not pictured
is Robert edgar, of Sandia National Laboratories.
same manner as living creatures. For example, ART can be
exposed to a solar generator to learn its normal variability.
This is called “training” and is similar to the training that
humans receive prior to operating equipment. When ART
encounters any unusual operational variability, it can flag
that condition, just as would a human.
UNM and Sandia researchers are experimenting with
ART on renewable generators. They have built the Solar
Hot Water Reliability Testbed (SHWRT). It contains
a solar hot water generator, similar to ones installed on
homes, but with extensive controls and sensors. In testing,
ART has been able to identify when a pump is beginning
to fail, recognizing the system characteristics leading to
Predicting component failures with computer algorithms is extremely difficult. What sets ART apart from
other methods of fault detection is that it uses only those
sensors that are normally available to control the equipment. Thus, ART algorithms can be easily programmed into
modern commercial controllers with few or no hardware
changes that might increase manufacturing costs.
ART can also be configured to continue self-training
after installation. The AR T system can tailor its knowledge
about a particular system and make it better able to identify
and predict failures in the future.
ART technology represents a substantial advance in how
renewable systems can be controlled and monitored. Its
application is broad, and the next stage of testing will be on a
much more complex generator, the solar absorption testbed
that is used to partially heat and cool UNM’s mechanical
engineering building. ART technology is also being proposed
for use on microgrids that include electrical generators, such
as microturbines and fuel cells. —DAVE MENICUCCI
On Cyprus, Green Machine Makes Electricity with Waste Water
Loy sneAry, guLF coAst green energy
A pilot project on Cyprus runs campus wastewater through solar water-heating collectors and then through the electra Therm heat-to-power
16 September/October 2011 SOLAR TODA Y solartoday.org
Copyright © 2011 by the American Solar Energy Society Inc. All rights reserved.