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Texas Instruments Predictive Maintenance in Smart Meters Using Real-Time Monitoring Application notes
Predictive Maintenance in Smart Meters Using Real-Time
Monitoring
Peggy Liska and Eric Djakam
When designing these next-generation smart meters, it
is important to keep several key factors in mind: 1)
high reliability, 2) low power consumption, and 3) small
size. With higher reliability, utilities spend less on
maintaining existing infrastructure and reduce overall
operating costs. With lower power, meters are able to
operate longer on a single battery. With smaller size,
more sensors can be placed in more locations further
increasing the intelligence and efficiency of the 'grid' whether water, gas, heat, or electricity. For instance,
with waters meters the addition of information about
the pipe pressures will allow for optimization of the
pressure injection into the grid and extend the lifetime
of the pipes.
More details about how this type of system-level
monitoring can be implemented is explained in the
Battery and System Health Monitoring of Battery
Powered Smart Flow Meters Reference Design.
The autonomous monitoring capability of a device like
the ADS7142 enables higher system reliability by
raising an alert when the current consumption goes
outside of the known good operating range. This could
be caused by aging of the ultrasonic electrodes,
changes in urban configuration requiring higher RF
transmission time, aging capacitors and inductors, or
software malfunction. All of these would cause higher
current consumption that would be detected and
preemptively addressed before the lifetime of the
meter expires. Figure 1 illustrates the concept of using
programmable thresholds to monitor a signal and wait
for an alert.
!
High Threshold
!
Low Threshold
Figure 1. Programmable Thresholds for Monitoring
Early detection of these types of faults is possible by
more closely monitoring the normal current
consumption profiles before they reach true fault
levels. In other words, the system can detect slight
changes in current consumption over time instead of
waiting for a known fault level thereby enabling earlier
detection of faults before they become critical and
perhaps allowing the system to simply be repaired
instead of replaced. Figure 2 illustrates the concept of
detecting a fault before it gets to critical levels.
Current Consumption
Overall, these trends are driving more electronics into
these smart meters while still maintaining >15 years of
lifetime without required regular maintenance. This is
leading to the industry trend of predictive maintenance
which allows utility companies to better understand
and predict when and where failures will occur
similarly to how this is being done in other industries
like factory automation. The goal is to maintain a
system with minimal interaction over a long period of
time with high reliability. Utility companies cannot
tolerate a failure in a smart meter because of the
financial damage or loss incurred, so a quick or
preemptive solution is required.
Autonomous Real-Time Monitoring
Fault Level
Time
Current Consumption
Residential smart meters are used for billing of
electricity, gas, water, and heat. The technological
advancements in these smart meters are being driven
by the demand from utility companies to create a
smarter and more efficient grid aimed at reducing nonrevenue losses. More sensors are being added to
collect more data about the status of the grid including
pressure, temperature, etc. There is also a growing
trend to incorporate higher precision measurements
with newer technologies, such as ultrasonic flow
measurements, which often end up adding to the
overall complexity, reliability, and power consumption.
These meters are also often part of radio-frequency
(RF) communication networks that can draw a variable
amount of power from the system depending on the
required output power of their signal.
Fault Level
Programmable Early-Detect Level
Time
Figure 2. Early Detection of Faults
SBAA291 – July 2018
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Predictive Maintenance in Smart Meters Using Real-Time Monitoring Peggy Liska
Copyright © 2018, Texas Instruments Incorporated
and Eric Djakam
1
www.ti.com
Low-power Real-Time Monitoring
When implementing a real-time monitoring function, it
is important to consider the impact this will have on the
lifetime of the battery due to the additional power
consumption. The typical current consumption of the
ADS7142 in autonomous mode is lower than the
leakage current of the primary lithium battery itself.
Therefore, this should have a negligible impact on
battery life over the lifetime of the system. This point is
illustrated by calculating the total used capacity of the
battery over 15 years, after deduction of the battery
capacity lost due to its self-leakage current.
For this example, assume a self-leakage current of 2
µA and an initial capacity of 3000 mAh. Table 1 shows
that 8.8% of the battery capacity is consumed by the
battery itself and about 2.2% of the remaining battery
capacity is used by the monitoring function. Thereby,
97.8% of the remaining capacity is still available to the
rest of the system after implementing the system
monitoring function.
Table 1. Battery capacity of ADS7142 system
monitor
Current Consumption
Annual Battery Capacity
Consumption
System Lifetime
Energy
AVDDMCU u IVDDMCU ¼º
» AVDDADC u IVDDADC IVREFADC
Sample Rate
(1)
Table 2. MCU versus ADC power consumption
ADS7142
Battery SelfLeakage
ADS7142
2 µA
0.45 µA
17.52 mAh/year
3.94 mAh/year
15 years
15 years
262.8 mAh
59.13 mAh
Total Available Battery
Capacity
3000 mAh
2737.2 mAh
Total Battery Capacity
Consumption %
8.8%
2.2%
Comparison between ADS7142 and MCU ADCs
The monitoring function can be implemented using the
integrated ADC available on most modern
microcontrollers (MCU). Two critical points should be
taken into account:
1. Operating the integrated ADCs requires running the
MCU core.
2. Operating the integrated ADCs requires the use of
a voltage reference.
1.8 V
1.8 V
IVDD
0.45 µA
1000 µA
IVREF
N/A (AVDD is VREF)
400 µA
600 SPS
1 MSPS
MCU Core - Standby
Mode
MCU Core - Run
Mode (8 MHz)
AVDD
1.8 V
1.8 V
IVDD
0.9 µA
2000 µA
4.05 nJ/sample
6.12 nJ/sample
Total Energy
By adding real-time monitoring of the battery current
consumption, smart meters can improve their reliability
as well as improve the intelligence of the overall smart
grid infrastructure. Implementing this real-time
monitoring feature with an external nanopower ADC
comes at the additional cost of the ADC itself but
lowers the overall system power consumption
compared to the internal ADC on the MCU thereby
increasing the overall lifetime of the smart meter. In
addition, it allows for gathering of real-time in-system
information and reduces the overall cost of ownership
by minimizing complete meter failures in the field.
Predictive Maintenance in Smart Meters Using Real-Time Monitoring Peggy Liska
and Eric Djakam
MCU ADC
AVDD
Sample Rate
Total Battery Capacity
Consumption
2
An external ADC like the ADS7142 can be used
instead of the integrated ADC on the MCU. The
energy consumed per sample can be calculated using
Equation 1, and the energy consumption comparison
between the external ADS7142 ADC and an internal
MCU ADC is summarized in Table 2. Despite the fact
that running the ADS7142 at a slower sample rate
effectively increases the energy consumed per
sample, the table shows that the both reduced current
consumption of the external ADC as well as the
reduced current consumption of running the MCU in a
lower power state helps reduce overall energy
consumption of the system.
Copyright © 2018, Texas Instruments Incorporated
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