8th PVPMC Workshop, May 9-10 2017

1. Characterizing PV modules
using Microinverter data
Nathan Charles

2. | © 2017 Enphase Energy, Inc.2
The Enphase Home Energy Solution with IQ
Enphase
Battery
Enphase
Envoy
Enphase
Installer Toolkit
Enphase
Enlighten
Enphase
IQ 6 / IQ 6+
Micro Enphase
Q Cable

3. | © 2017 Enphase Energy, Inc.3
• Enlighten displays detailed
data down to module level
• Enphase has produced ~15
million microinverters. Every
inverter reports time stamped
sensor data including:
• Internal temperature
• DC voltage
• DC current
• Over 500,000 systems in
Enlighten with ~ 4 million
inverter years of data
Microinverter data is reported to Enlighten

4. | © 2017 Enphase Energy, Inc.4
• Weather adjusted PR is commonly used for Commercial and Utility
Scale PV systems but requires:
• Instrumentation of module temperature
• Local POA Irradiance source, maintained and calibrated over time for accuracy
• Accurate tilt and azimuth of test unit(s), and data transposition for modules slightly
differently oriented within the same array or system
• This isn’t practical for small residential systems because:
• There is typically no local instrumentation
• As-built may not match design; and
• The time required to analyze a system is prohibitive
Problem: How to flag asset underperformance?

5. | © 2017 Enphase Energy, Inc.5
Infer Site/Orientation
Metadata
• Module Tilt
• Module Azimuth
• Module Latitude
• Module Longitude
Infer PV Module
Metadata
• Voltage reference
(Vmp)
• Current reference
(Imp)
• Temperature
coefficient
Signal analysis of
system behavior
• Series loss step
changes
• Shunt loss trends
Approach and process for data analysis
• Power != Energy - Micros report instantaneous values. DC current and DC voltage values gives us
significantly greater insight into environmental issues than just energy, but require different methods
of analysis.
• Bottom-up, start at the individual inverter level and work up to system and fleet level
• Analysis should not be dependent on external sensors, data sources, or meta data
• Simple is better than complicated – the dataset is too large for expensive processing
• Process should run automatically without intervention

6. | © 2017 Enphase Energy, Inc.6
1. Filter historical data for 24 hour interval with most Ah per month. This should be
correlated with clear sky irradiance.
2. With Kendall correlation coefficient between clear sky irradiance (W/m^2) and
dc current (A) as a cost function;
3. maximize correlation of current and clear sky irradiance by adjusting tilt and
azimuth.
• This process tests model and installer entered meta data.
• Methodology performance tested on a population in a small fleet
(n=190)
Tilt & Azimuth inference methodology
Infer Site/Orientation
Metadata

7. | © 2017 Enphase Energy, Inc.7
Azimuth analysis
• Azimuth is strongly
correlated with relatively
few outliers. RMSE 14.4°
with 5.8° bias west of
entered data.
• Why do we have outliers;
model error or incorrect
meta data?
Infer Site/Orientation
Metadata

8. | © 2017 Enphase Energy, Inc.8
Tilt analysis
• RMSE 5.7° but only a 1.4°
degree mean bias inferred
higher than entered.
• Installers may be making
assumptions e.g. 25° or 18°
instead of taking a
measurement
• Roof tilt is probably not a
standard distribution.
Infer Site/Orientation
Metadata

9. | © 2017 Enphase Energy, Inc.9
Example 1
• Installer entered: 180°
• Regression: 259°
• Perhaps install crew
confused job info?
Infer Site/Orientation
Metadata

10. | © 2017 Enphase Energy, Inc.10
Example 2
• Installer entered: 164°
• Regression: 140°
• Google maps shows
142 °
• Was the measurement
source calibrated?
Infer Site/Orientation
Metadata

11. | © 2017 Enphase Energy, Inc.11
Example 3
• Installer entered: 105°
• Regression didn’t lead
to meaningful data.
• Google maps shows a
system where dormers
would shade all
substrings of PV
module. A period
where only one diode
would latch is unlikely.
Infer Site/Orientation
Metadata

12. | © 2017 Enphase Energy, Inc.12
Example 4
• Installer entered: 116°
• Regression: 210°
• Array view looks
correct, however meta
data in Enlighten is
transposed. 8 modules
should be at 206°
instead of 116°
Infer Site/Orientation
Metadata

13. | © 2017 Enphase Energy, Inc.13
Regression model used to
model cell temperature from
micro temperature accounts for:
• Thermal latency of between
sensor and cell
• Heating from efficiency losses
• Temperature delta between cell
temperature and microinverter
Cell temperature
Infer Module
Metadata

14. | © 2017 Enphase Energy, Inc.14
Module Metadata: Voltage & Temperature
Slope of this line
is should be
correlated to
module
temperature
coefficient.
Value at 25
degrees C
should be
correlated with
Vmp
Infer Module
Metadata

15. | © 2017 Enphase Energy, Inc.15
Module Metadata: DC Current
DC Current vs Clearsky
Irradiance
RANSAC, a robust fit
shallow machine
learning algorithm,
used to find slope of
the ridge.
1000 W/m^2 value
should be similar to
Imp
Infer Module
Metadata

16. | © 2017 Enphase Energy, Inc.16
Irradiance Performance Ratio over time
Clear sky vs Actual
over time.
Step up in high
current steady
state is likely a rain
event.
Signal analysis of
system behavior

17. | © 2017 Enphase Energy, Inc.17
Temperature compensated voltage over time
PCU Temperature
used to adjust
voltage
Voltage step down
is attributed to
module sub-string
issue
Signal analysis of
system behavior

18. | © 2017 Enphase Energy, Inc.18
• Correlation maximization leads to meaningful inference of azimuth (7°
median deviation)
• Significant deviations between inferred and reported azimuth are typically
representational of incorrect meta data or sub-optimal design
• Inferred PV module characteristics allow visualization of module
performance over time
Microinverter data improves understanding of
fleet performance
Infer Site/Orientation
Metadata
• Module Tilt
• Module Azimuth
• Module Latitude
• Module Longitude
Infer PV Module
Metadata
• Voltage reference
(Vmp)
• Current reference
(Imp)
• Temperature
coefficient
Signal analysis of
system behavior
• Series loss step
changes
• Shunt loss trends

19. Thank you
Questions?