energies
Case Report
Performance Analysis of a Grid-Connected Upgraded
Metallurgical Grade Silicon Photovoltaic System
Chao Huang 1, *, Michael Edesess 2 , Alain Bensoussan 1,3 and Kwok L. Tsui 1
1
2
3
*
Department of Systems Engineering and Engineering Management, City University of Hong Kong,
Kowloon, Hong Kong, China; abensous@cityu.edu.hk (A.B.); kltsui@cityu.edu.hk (K.L.T.)
Centre for Systems Informatics Engineering, City University of Hong Kong, Kowloon, Hong Kong, China;
medesess@gmail.com
School of Management, University of Texas at Dallas, Richardson, TX 75080-3021, USA
Correspondence: chahuang3-c@my.cityu.edu.hk; Tel.: +852-3442-9321
Academic Editor: Tapas Mallick
Received: 2 March 2016; Accepted: 27 April 2016; Published: 5 May 2016
Abstract: Because of their low cost, photovoltaic (PV) cells made from upgraded metallurgical grade
silicon (UMG-Si) are a promising alternative to conventional solar grade silicon-based PV cells.
This study investigates the outdoor performance of a 1.26 kW grid-connected UMG-Si PV system
over five years, reporting the energy yields and performance ratio and estimating the long-term
performance degradation rate. To make this investigation more meaningful, the performance of
a mono-Si PV system installed at the same place and studied during the same period of time is
presented for reference. Furthermore, this study systematizes and rationalizes the necessity of a data
selection and filtering process to improve the accuracy of degradation rate estimation. The impact of
plane-of-array irradiation threshold for data filtering on performance ratio and degradation rate is
also studied. The UMG-Si PV system’s monthly performance ratio after data filtering ranged from
84% to 93% over the observation period. The annual degradation rate was 0.44% derived from time
series of monthly performance ratio using the classical decomposition method. A comparison of
performance ratio and degradation rate to conventional crystalline silicon-based PV systems suggests
that performance of the UMG-Si PV system is comparable to that of conventional systems.
Keywords: photovoltaic; upgraded metallurgical grade silicon; data filtering; performance ratio;
degradation rate
1. Introduction
During the last decade, the global photovoltaic (PV) market has grown at an average rate of
50% annually [1]. However, the high cost of PV cells made from conventional solar grade silicon is a
hindrance to the rapid growth of the PV industry. One promising alternative is to use PV cells made
from upgraded metallurgical grade silicon (UMG-Si) purified via the metallurgical process route [2–4].
The production of UMG-Si can be five times more energy efficiency than the conventional Siemens
process to produce solar grade silicon; hence, the cost of UMG-Si is much lower [5,6]. However,
UMG-Si contains more impurities, resulting in lower efficiency of crystalline silicon solar cells made
from such materials than from the conventional Siemens process. Thanks to the improvements in
the UMG purification process, Zheng et al., in 2016 [7] reported solar cells fabricated with 100%
UMG-Si with peak efficiency of 20.9%, and 21.9% for a control device made from electronic grade
silicon. Nevertheless, the potential advantage of low cost can be impaired if outdoor efficiency is
reduced by degradation of quality due to high concentration of metallic and non-metallic impurities [8].
Manufacturers have started producing PV cells made of UMG-Si, and demonstration projects have
been launched. PV modules made from UMG-Si manufactured by Canadian Solar and Silicor Materials
Energies 2016, 9, 342; doi:10.3390/en9050342
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Energies 2016, 9, 342
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are being tested at the National Renewable Energy Laboratory (NREL) [9]. In China, a UMG-Si PV
plant with a capacity of 330 kW has been built [10]. Measurement and reporting of the outdoor
performance of UMG-Si PV systems will help facilitate further improvements and better applications.
The outdoor performance of a PV system strongly depends on effective irradiation and module
temperature. The power output as well as the module efficiency of crystalline silicon modules
decreases with increasing cell temperature. In the field, however, there is often a difference between
the observed incident irradiation on the surface of PV modules and the effective irradiation that is
useful for PV production. This difference is explained by the influence of the solar spectrum and solar
angle-of-incidence (AOI). PV modules respond more strongly to a certain range of wavelength of light
contained in the solar spectrum [11]; the latter describes the intensity of irradiation at each wavelength
of the incoming light, and varies as the day progresses. The influence of AOI on PV performance
is due to two causes. One is the so called “cosine effect” which has already been removed from the
pyranometer’s irradiation measurement; optical loss, however, related to AOI still remains [12].
The performance of PV modules tends to degrade over time. Degradation rate, Rd , which
characterizes the changes of PV power delivery capacity over time, is of great importance in evaluating
the long-term reliability of a PV system. Jordan et al. [13] surveyed nearly 2000 reported degradation
rates, finding a median of 0.5% with a range from ´0.2% to 4.2%. Rd is usually derived from time
series of PV performance metrics such as performance ratio (PR). To obtain an accurate degradation
rate, it is desirable for the impacts of non-degradation factors to be isolated from PV performance
metrics. To improve the accuracy of degradation rate estimation, the research presented in a number
of previous articles [14–16] has filtered the observed data, discarding data points with low levels of
irradiation, eliminating data points on cloudy days with large fluctuations in irradiation, and removing
outliers. Analytical techniques also greatly influence the accuracy of degradation rate estimation.
In the literature, linear regression is the most widely used analytical technique to estimate Rd from
time series of performance metrics [17]. Other analytical techniques, classical decomposition and
autoregressive integrated moving average (ARIMA), were used in [18] to estimate Rd with stronger
robustness against data shift, outliers, and shorter observation time than linear regression.
The aim of this study is to investigate the long-term outdoor performance of a 1.26 kW
grid-connected UMG-Si PV system installed at the NREL in Golden, Colorado, USA. This investigation
consists of two parts: evaluation of system performance in converting solar energy to electricity; and
estimation of the long-term performance degradation rate. To make the investigation more meaningful,
the performance of another 1 kW PV system consisting of PV cells made from monocrystalline silicon
surrounded by ultra-thin amorphous silicon layers installed at the same place and studied during the
same period of time is presented for reference. In the following sections, these two PV systems are
identified as UMG-Si and mono-Si PV systems, respectively.
Furthermore, this study strives to systematize and rationalize the necessity of using a data
selection and filtering procedure to obtain more reliable long-term performance metrics for degradation
rate estimation. The impact of a plane-of-array (POA) irradiation threshold for data filtering on PR
and estimated degradation rate is also studied.
The remainder of the paper is organized as follows. Section 2 briefly introduces the PV systems
and database used in this study. In Section 3, the methods used to evaluate PV performance and to
estimate the degradation rate are developed. Results and discussion are in Section 4. A comparison of
PR and Rd for the UMG-Si PV system to those for conventional crystalline silicon-based PV modules is
made in Section 5. This is followed by conclusions in Section 6.
2. PV System and Database
The UMG-Si PV system and the mono-Si PV system are located in Golden, CO, USA (latitude
39.74˝ N, longitude 105.18˝ W). The 1.26 kW UMG-Si PV array consists of 6 polycrystalline silicon based
CS6P-210PE modules manufactured by Canadian Solar, and the 1 kW mono-Si PV array consists of
5 HIP-200BA3 modules manufactured by Sanyo (San Diego, CA, USA). The PV arrays are installed at a
Energies 2016, 9, 342
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fixed direction with a tilt angle of 40˝ to receive maximal average solar irradiation. The PV arrays are
connected to inverters manufactured by PV Powered for the UMG-Si PV system and by SMA for the
mono-Si PV system. The inverter performs maximal power point tracking, which controls voltage at
the optimum level to maximize power delivery. Thus, in this study the performance of PV is measured
at the maximal power point. Electrical properties of the two kinds of modules are presented in Table 1.
Table 1. Electrical properties of photovoltaic (PV) modules.
Property
UMG-Si
mono-Si
Maximal power at STC (Pmax )
Maximal power voltage (V mp )
Maximal power current (Imp )
Open circuit voltage (V oc )
Short circuit current (Isc )
Temperature coefficient (Pmax )
210 W
29.0 V
7.25 A
36.4 V
7.89 A
´0.42%/˝ C
200 W
55.8 V
3.59 A
68.7 V
3.83 A
´0.29%/˝ C
The continuously observed data for the PV systems is measured using a Campbell Scientific data
logger by NREL [9]. DC performance is measured over a 50 mV current shunt and voltage divider.
Meteorological and electrical data are sampled every 5 s and averaged every minute. POA irradiation is
measured with a Kipp and Zonen CMP-11 broadband pyranometer. Module temperature is measured
with three T-type thermocouples. The mono-Si PV system was installed almost three years earlier
than the UMG-Si PV system; however, only the performance of both systems from 1 January 2011 to
31 December 2015 will be reported and used to analyze the annual performance degradation rate.
3. Methods
3.1. Evaluation of PV Performance
IEC standard 61724 [19] defines normalized parameters like PV array yield (Ya ) and reference
yield (Yr ) to quantify the energy production and solar resources over a period, and defines PR to
describe the overall energy transformation capacity of a PV system. These normalized quantities can
be updated daily, monthly, or yearly, and are often used to compare the performance of different PV
systems. In this study, DC output from the UMG-Si PV array is used to minimize the effect of the
inverter so as to focus on the performance of the PV array itself.
Ya is defined as energy E (integration of power over time) generated by the array during the
given period divided by the array nominal power P0 (the theoretical power output given the reference
irradiation and temperature):
E
phq
P0
Ya “
(1)
Yr is defined as the ratio of the measured POA irradiation G over the given period to the reference
irradiation G0 :
Yr “
G
phq
G0
(2)
PR is defined as Ya divided by Yr :
PR “ 100
Ya
p%q
Yr
(3)
In addition to the daily or monthly PR, we analyze its instantaneous counterpart, PRt , which is
given by:
PRt “ 100
Pt {P0
p%q
Gt {G0
(4)
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where Pt and Gt are instantaneous array power output and POA irradiation at time t, respectively.
PR over a period (daily or monthly) computed from instantaneous values is given by:
ř
Pt {P0
PR “ 100 ř
p%q
(5)
Gt {G0
Hence, PR is the ratio of the array’s output as a percentage of its nominal output, to actual
irradiation as a percentage of nominal irradiation, indicating the response of the PV array to
irradiation. PR normalizes power output for the most important driver of its variability, the first-order
irradiation; thus, it removes this source of variability from power output variability making it a suitable
performance metric to investigate long-term changes in PV performance.
This study will report the yields and PR of the UMG-Si PV system during the observation period
in different time scales: minutely, daily, and monthly. Moreover, this study strives to investigate factors
which make certain PR data points substantially less reliable in reflecting PV response to irradiation
than others, thereby systematizing and providing the rationale for data filtering to obtain more reliable
performance metrics for degradation rate estimation.
3.2. Estimation of Degradation Rate
Degradation rate is usually estimated from time series of PV performance metrics. In this
study, the statistical technique of classical decomposition is used to derive the degradation rate from
monthly PR.
Classical decomposition, in which the signal is partitioned into three components, the trend, the
seasonality, and the irregular component, is mathematically expressed as:
Yt “ Tt ` St ` et , for t “ 1, . . . , n
(6)
where Yt is the signal, Tt is the component of trend, St the component of seasonality, and et the
remaining irregular component assumed to be random variables with zero mean and constant variance
at time t [20,21]. In our study, Yt refers to the time series of monthly PR over the observed period of
60 months; hence, t ranges from 1 to 60 (n equals to 60). The component of trend is obtained from the
original data by employing a two-step centered twelve-month moving average. For a 2ˆ m moving
average, (here m = 12, the number of months in a year), the centered average at time t is arrived at by:
¨
Tt “
1 ˝
2m
t`m
{2´1
ÿ
t`ÿ
m{2
Yi `
i“t´m{2
˛
Yi ‚
(7)
i“t´m{2`1
where Tt is the trend for m/2 < t < n-m/2.
The degradation rate is obtained by applying a linear regression on the extracted trend Tt :
y “ ax ` b
(8)
where a is the slope of the line fitted to the series of Tt obtained in Equation (7) and b its corresponding
intercept. b represents the estimated initial PR, and a represents the estimated monthly performance
loss; thus, the annual degradation rate in percentage is expressed as:
Rd “ 100
12a
p%q
b
(9)
With the classical decomposition method, the impact of seasonal variation on Yt is expected to be
reduced. The component of seasonality in each month throughout the observation period is derived
by subtracting the extracted trend from the original signal. To eliminate the variation of seasonality
in a given month of different years, average the values of each respective month throughout the
observation period under the assumption that the seasonality in each month does not vary from year
to year. After the seasonality is known, and using the trend, the remaining irregular component is
calculated with Equation (6).
derived by subtracting the extracted trend from the original signal. To eliminate the variation of
seasonality in a given month of different years, average the values of each respective month
throughout the observation period under the assumption that the seasonality in each month does
not vary from year to year. After the seasonality is known, and using the trend, the remaining
Energies 2016, 9, 342
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irregular component is calculated with Equation (6).
Results and
and Discussion
Discussion
4.4. Results
4.1.
4.1.PV
PVPerformance
Performance
4.1.1.
4.1.1.Instantaneous
InstantaneousPerformance
Performance
Instantaneous
Instantaneousperformance
performanceisisanalyzed
analyzedon
ononly
onlyfive
fiveconsecutive
consecutivedays
daysfrom
from2828March
Marchtoto1 1April
April
2015.
2015.These
Thesedays
daysare
areselected
selecteddue
duetototheir
theirvariety
varietyofofweather
weatherconditions.
conditions.Figure
Figure11depicts
depictsthe
thePOA
POA
irradiation,
power
output,
ambient
temperature,
and
module
temperature
at
time
intervals
of
1
min.
irradiation, power output, ambient temperature, and module temperature at time intervals of 1
28minute.
March 28
andMarch
29 March
sunnywere
dayssunny
on which
were no
large
fluctuations
in irradiationin
and were
29 March
daysthere
on which
there
were
no large fluctuations
over
short
time
intervals.
30
March
was
a
cloudy-sunny
day,
while
31
March
was
a
sunny-cloudy
irradiation over short time intervals. 30 March was a cloudy-sunny day, while 31 March was a
day.
1 April wasday.
a cloudy
day
on awhich
theday
fluctuation
irradiation
wasinsignificant.
was
sunny-cloudy
1 April
was
cloudy
on whichinthe
fluctuation
irradiationThe
waspower
significant.
roughly
proportional
to theproportional
POA irradiation,
and
thisirradiation,
relationshipand
wasthis
tighter
on sunnywas
daystighter
than on
The power
was roughly
to the
POA
relationship
on
cloudy
days.
Module
temperature
is
related
principally
to
ambient
temperature,
irradiation,
and
wind
sunny days than on cloudy days. Module temperature is related principally to ambient temperature,
speed.
The module
temperature
fluctuated
with fluctuating
incident POA
irradiation,
and wind
speed. The
modulesynchronously
temperature fluctuated
synchronously
withirradiation,
fluctuating
which
wasPOA
obviously
observed
on was
cloudy
days. observed on cloudy days.
incident
irradiation,
which
obviously
(a)
(b)
Figure1.1.Measurements
Measurements
of meteorological
PV output
from
28 March
to 12015:
April
(a)
Figure
of meteorological
datadata
and and
PV output
from 28
March
to 1 April
(a)2015:
power
power
output
and
plane-of-array
(POA)
irradiation;
(b)
ambient
and
module
temperature.
output and plane-of-array (POA) irradiation; (b) ambient and module temperature.
Theinstantaneous
instantaneousPR
PRduring
duringthe
thefive
fiveconsecutive
consecutivedays
daysisispresented
presentedininFigure
Figure2.2.InInthe
theearly
early
The
morning,
instantaneous
PR
increased
quickly
with
increasing
POA
irradiation.
This
is
explained
morning, instantaneous PR increased quickly with increasing POA irradiation. This is explained partly
partly
by the decreasing
solar angle-of-incidence,
and
by the variation
ofspectrum.
the solar spectrum.
by
the decreasing
solar angle-of-incidence,
and partly
bypartly
the variation
of the solar
Oblique,
Oblique,
that
is,
high,
angles-of-incidence
in
early
mornings
result
in
small
effective
irradiation
due
that is, high, angles-of-incidence in early mornings result in small effective irradiation due to increased
to
increased
optical
loss.
The
variation
of
the
solar
spectrum
is
primarily
due
to
the
variation
in
the
optical loss. The variation of the solar spectrum is primarily due to the variation in the air mass
air
mass
through
which
the
irradiation
travels
in
mornings,
mid-days,
and
evenings.
The
air
mass
through which the irradiation travels in mornings, mid-days, and evenings. The air mass is greatest inis
greatest in
mornings
and
evenings
when
the sun’s
radiation
atangle,
an oblique
angle, thus
passing
mornings
and
evenings
when
the sun’s
radiation
passes
at anpasses
oblique
thus passing
through
a
through
a
large
mass
of
air,
and
is
lowest
at
mid-day.
Instantaneous
PR
tended
to
be
steady
large mass of air, and is lowest at mid-day. Instantaneous PR tended to be steady at around 88% atat
around
88% at
highofmid-day
levels
of irradiation,
while dropping
slightly
prior
hours
due to
high
mid-day
levels
irradiation,
while
dropping slightly
from prior
hoursfrom
due to
higher
module
higher
module
temperature.
In
the
late
afternoon,
instantaneous
PR
fell
in
pace
with
decreasing
temperature. In the late afternoon, instantaneous PR fell in pace with decreasing POA irradiation due to
POA
irradiation
due to the increasing
angle-of-incidence
asspectrum.
well as the
change near
in solar
spectrum.
the
increasing
angle-of-incidence
as well as
the change in solar
However,
sunset
when
However,
near
sunset
when
the
POA
irradiation
was
close
to
zero,
suspiciously
high
values
the POA irradiation was close to zero, suspiciously high values of instantaneous PR, greater than 100%,of
instantaneous
PR, greater
than
100%, from
werethe
observed.
Thisofmay
have
resultedorfrom
themeasurement
inaccuracy of
were
observed. This
may have
resulted
inaccuracy
POA
irradiation
power
POA
irradiation
or
power
measurement
at
very
low
levels
of
irradiation,
since
a
small
error
the
at very low levels of irradiation, since a small error on the power or irradiation measurement
canon
lead
or irradiation
measurement
canwas
lead
to large
in PR
when
the that
truePR
value
very
small.
topower
large error
in PR when
the true value
very
small.error
Figure
2 also
shows
waswas
more
variable
Figure
2
also
shows
that
PR
was
more
variable
under
cloudy
conditions
where
solar
irradiation
under cloudy conditions where solar irradiation largely fluctuated. This confirms that wide fluctuation
in irradiation can considerably reduce the precision of PR [15]. Thus, it is necessary to discard data
points with large fluctuation in irradiation to obtain more reliable PV performance metrics.
Energies 2016, 9, 342
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6 of 15
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largely fluctuated. This confirms that wide fluctuation in irradiation can considerably reduce the
largely fluctuated. This confirms that wide fluctuation in irradiation can considerably reduce the
precision
Energies
2016,of9,PR
342[15]. Thus, it is necessary to discard data points with large fluctuation in irradiation
6 of 15
precision of PR [15]. Thus, it is necessary to discard data points with large fluctuation in irradiation
to obtain more reliable PV performance metrics.
to obtain more reliable PV performance metrics.
Figure 2. Instantaneous PR from 28 March to 1 April 2015.
Figure 2.
2. Instantaneous
Instantaneous PR
PR from
from 28
28 March
March to
Figure
to 11 April
April 2015.
2015.
Figure 3 depicts the variation of PR as a function of POA irradiation. The impact of temperature
Figure 3 depicts the variation of PR as a function of POA irradiation. The impact of temperature
is removed by converting PR to 25 ˝°C using the maximal power temperature coefficient given in
C using the maximal power temperature coefficient given in
is removed by converting PR to 25 °C
Table 1 with the equation PR25°C = PR/[1 + α(Tmod − 25)] where α is the maximal power temperature
˝
Table
=
PR/[1
α(Tmod
25)]where
whereααisisthe
themaximal
maximal power
power temperature
Table 11with
withthe
theequation
equationPR
PR
25°C
=
PR/[1
++ α(T
25)]
mod− ´
25 C
coefficient and Tmod is the module temperature. In Figure 3, data points where the POA irradiation is
coefficient and Tmod
is
the
module
temperature.
In
Figure
3,
data
points
where
the
POA
irradiation
irradiation
is
modis the module temperature. In Figure 3, data points where the POA
smaller than 50 W/m2 are
dropped to eliminate misleading measurements at very low levels of
2
2 areare
is smaller
than
W/m
dropped
eliminate
misleading
measurements
at very
levels
smaller
than
50 50
W/m
dropped
to to
eliminate
misleading
measurements
at very
lowlow
levels
of
irradiation.
of irradiation.
irradiation.
Figure 3. Temperature-corrected PR as a function of POA irradiation
Figure 3. Temperature-corrected PR as a function of POA irradiation
Figure 3. Temperature-corrected PR as a function of POA irradiation.
As can be seen from Figure 3, irradiation intensity-dependent instantaneous PR varied little
As can be seen from Figure 3, irradiation intensity-dependent
instantaneous PR varied little
can
be seenPOA
from irradiation
Figure 3, irradiation
intensity-dependent
instantaneous
varied
little when
whenAsthe
incident
was greater
than 600 W/m2; at
this level of PR
POA
irradiation
the
2; at this level of POA irradiation the
when the incident POA irradiation was greater than 600
W/m
2 ; at this level of POA
the
incidentcaused
POA irradiation
greater than
W/m
the influences
influences
by solarwas
spectrum
and 600
solar
angle-of-incidence
wereirradiation
small. The
effects of
influences caused by solar spectrum and solar angle-of-incidence were small. The effects of
caused
by solar spectrum
and
solar angle-of-incidence
were small.
The effects of angle-of-incidence
angle-of-incidence
and solar
spectrum
explain why on cloudy
days instantaneous
PR at low levels of
angle-of-incidence and solar spectrum
explain why on cloudy days instantaneous PR at low levels of
2
and
solar spectrum
why
on cloudy
PR at
low levels
irradiation,
around
irradiation,
around explain
200 W/m
, was
greaterdays
thaninstantaneous
on sunny days.
Suppose
thatofthe
POA irradiation
irradiation,
around 200 W/m2, was greater than on sunny days. Suppose that the POA irradiation
2 , was
200
greater
than on
sunny
Suppose
that the
POA
irradiation
level on
levelW/m
on cloudy
days
at noon
is close
to days.
the POA
irradiation
level
in the
early morning
oncloudy
sunny days
days.
level on cloudy days at noon is close to the POA irradiation level in the early morning on sunny days.
at
noon
is close to
POA days
irradiation
level
in the
morning
on sunny
POA
POA
irradiation
onthe
cloudy
consists
mainly
of early
diffuse
irradiation
whichdays.
reaches
theirradiation
surface of
POA irradiation on cloudy days consists mainly of diffuse irradiation which reaches the surface of
on
cloudyfrom
daysall
consists
mainly
of diffuse
irradiation
which
reachesto
the
surface
modules from
all
modules
directions
resulting
in less
optical loss
compared
the
beam of
component
of POA
modules from all directions resulting in less optical loss compared to the beam component of POA
directions
less optical
to the beam
of The
POAdifference
irradiationinat solar
high
irradiationresulting
at high in
oblique
angleloss
in compared
the early morning
on component
sunny days.
irradiation at high oblique angle in the early morning on sunny days. The difference in solar
oblique
angle
in the
early
morning at
onnoon
sunny
The
difference
in solar
between
spectrum
between
POA
irradiation
ondays.
cloudy
days
and in the
early spectrum
morning on
sunny POA
days
spectrum between POA irradiation at noon on cloudy days and in the early morning on sunny days
irradiation
noon
on cloudy
days and in the early morning on sunny days complicates the deviation
complicatesatthe
deviation
of PR.
complicates the deviation of PR.
of PR.Analysis of the relationship between PR and POA irradiation strongly supports that, to obtain a
Analysis of the relationship between PR and POA irradiation strongly supports that, to obtain a
moreAnalysis
reliable PR
series,
it is necessary
to discard
at lowstrongly
levels ofsupports
irradiation
to to
reduce
the
of the
relationship
between
PR anddata
POApoints
irradiation
that,
obtain
a
more reliable PR series, it is necessary to discard data points at low levels of irradiation to reduce the
impact
of solar
andto
solar
spectrum.
more
reliable
PRangle-of-incidence
series, it is necessary
discard
data points at low levels of irradiation to reduce the
impact of solar angle-of-incidence and solar spectrum.
impact of solar angle-of-incidence and solar spectrum.
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4.1.2.
Daily
Performance
4.1.2. Daily Performance
Figure
4 Performance
presents the daily array yield and daily reference yield from 1 January 2011 to
4.1.2.
Daily
Figure2015,
4 presents
dailytwo
array
yieldinand
daily
reference
yieldoccurred
from 1 January
2011
to 31
31 December
whichthe
shows
peaks
each
year.
The peaks
on those
dates
when
Figure
4
presents
the
daily
array
yield
and
daily
reference
yield
from
1
January
2011
to
31
December
2015,
which
shows
two
peaks
in
each
year.
The
peaks
occurred
on
those
dates
when
the
the sun was positioned so that the PV module received the most energy radiation. The observed
December
2015, which
twoPV
peaks
in each
year. The
on those dates
the
sun was positioned
soshows
that the
module
received
thepeaks
mostoccurred
energy radiation.
The when
observed
maximal
daily
array yield
7.564the
h (corresponding
reference
yield
8.581
h)radiation.
occurred The
on 14observed
April 2013,
sun
was
positioned
so
that
PV
module
received
the
most
energy
maximal daily array yield 7.564 h (corresponding reference yield 8.581 h) occurred on 14 April 2013,
while
the observed
maximal
dailyhreference
yield 9.207
h (corresponding
array yield
6.808
h) occurred
maximal
array
yield
7.564
(corresponding
reference
8.581 h) occurred
onyield
14 April
2013,
while thedaily
observed
maximal
daily
reference yield
9.207 yield
h (corresponding
array
6.808
h)
on 25
March
2013.
Themaximal
observeddaily
POAreference
irradiation
and
PV hpower
output on array
25 March
2013
and
while
the
observed
yield
9.207
(corresponding
yield
6.808
h) on
occurred on 25 March 2013. The observed POA irradiation and PV power output on 25 March 2013
14 April
2013 shown
in Figure
5 in
explain
why
the irradiation
day
with maximal
array
yieldarray
did
coincide
with
occurred
25 March
The
observed
power
output
on not
25
March
and on 14onApril
2013 2013.
shown
Figure 5 POA
explain
why theand
dayPV
with
maximal
yield
did2013
not
the and
observed
maximal
daily
reference
yield.
It
is
clearly
illustrated
that
in
the
morning
from
7
on 14
April
shown
in Figure
explain why
the
day
with illustrated
maximal array
did notto 9
coincide
with
the 2013
observed
maximal
daily5 reference
yield.
It is
clearly
that inyield
the morning
coincide
with
the observed
maximal
daily
reference
yield.
Itfully
is clearly
illustrated
that
in the
morning
o’clock
March
2013,
the
PV2013,
system
didn’t
fully
respond
to the
incident
irradiation,
perhaps
fromon
7 to25
9 o’clock
on
25 March
the PV
system
didn’t
respond
to the incident
irradiation,
from
7
to
9
o’clock
on
25
March
2013,
the
PV
system
didn’t
fully
respond
to
the
incident
irradiation,
because
of
shading,
resulting
in
less
power
generated
than
expected.
perhaps because of shading, resulting in less power generated than expected.
perhaps because of shading, resulting in less power generated than expected.
(a)
(a)
(b)
(b)
Figure 4. Daily yields from 1 January 2011 to 31 December 2015: (a) array yield; (b) reference yield.
Figure
4. Daily
yields
December2015:
2015:(a)(a)array
array
yield;
reference
yield.
Figure
4. Daily
yieldsfrom
from1 1January
January2011
2011 to
to 31
31 December
yield;
(b)(b)
reference
yield.
(a)
(b)
(a)
(b)
Figure 5. Observed power and POA irradiation: (a) 25 March 2013; (b) 14 April 2013.
Figure 5. Observed power and POA irradiation: (a) 25 March 2013; (b) 14 April 2013.
Figure 5. Observed power and POA irradiation: (a) 25 March 2013; (b) 14 April 2013.
Energies 2016, 9, 342
Energies 2016, 9, 342
8 of 15
8 of 15
Figure
66 depicts
the daily
dailyPR
PRduring
duringthe
the
period
of observation.
values
varied
Figure2016,
depicts
period
of observation.
MostMost
values
varied
between
Energies
9, 342 the
8 between
of 1584%
84%
Not much
difference
was observed
fromtoday
oryear
fromtoyear
year except
for
and and
92%.92%.
Not much
difference
was observed
from day
daytoorday
from
yeartoexcept
for some
Figure
6
depicts
the
daily
PR
during
the
period
of
observation.
Most
values
varied
between
84%
some
irregular
points.
Those with
points
with
lowresulted
values from
resulted
from snowing,
of the
irregular
points.
Those points
very
lowvery
values
snowing,
shading ofshading
the PV array,
anddown-time,
92%.
Not down-time,
much
difference
was observed
from
day
to day
from data
year Shading
to
year except
for
someof the
PV
array,
system
component
errors
in or
power
collection.
Shading
system
component
failure,
or failure,
errors
inorpower
data
collection.
of the
irradiation
irregular
points.
Those
withcollection
verydata
low values
resulted
from in
snowing,
shading
thedaily
PVof
array,
sensor
or errors
inorirradiation
data
resulted
inresulted
extremely
high values
ofofthe
PR.
The
irradiation
sensor
errorspoints
in irradiation
collection
extremely
high
values
the daily
system down-time, component failure, or errors in power data collection. Shading of the irradiation
daily
PRdaily
can be
to used
identify
operational
problems.
PR.
The
PRused
can be
to identify
operational
problems.
sensor or errors in irradiation data collection resulted in extremely high values of the daily PR. The
daily PR can be used to identify operational problems.
Figure 6.
6. Daily
Daily PR from 1 January 2011 to
Figure
to 31
31 December
December 2015.
2015.
Figure 6. Daily PR from 1 January 2011 to 31 December 2015.
There
aresome
some
gaps
Figures
4a,b
and
6.
is due
due
tosystem
system
data
unavailability
from
19
There
are
gaps
ininFigures
4a,b
and
6. This
is due
to to
system
data
unavailability
from
19
There
are
some
gaps
in
Figures
4a,b
and
6. This
This
is
data
unavailability
from
19 May
to 82011,
June
2011,
and
totoirradiation
measurement
error
from
June
23
July
2013.
toMay
8 June
and
due
todue
irradiation
measurement
errorerror
fromfrom
26 June
to 23
July
2013.
May
to
8 June
2011,
and
due
irradiation
measurement
2626June
to to
23
July
2013.
4.1.3.4.1.3.
Monthly
Performance
Monthly
Performance
4.1.3.
Monthly
Performance
Figure
7 presents
monthlyarray
arrayyield,
yield, reference
reference yield,
PR
during
thethe
observed
period;
Figure
presents
thethe
monthly
array
yield,
and
PR
during
observed
period;
Figure
77presents
the
monthly
reference
yield,and
and
PR
during
the
observed
period;
it shows
array
yield
and
referenceyield
yieldin
in summer
summer were
than
in in
winter.
TheThe
observed
shows
thatthat
array
yield
and
reference
yield
weregreater
greater
than
winter.
observed
ititshows
that
array
yield
and
reference
greater
than
in
winter.
The
observed
maximal
monthly
array
yield
andreference
referenceyield
yield were
were 169.7
203.2
h respectively,
both
in April
maximal
monthly
array
yield
and
169.7
and
203.2
hhrespectively,
both
in
maximal
monthly
array
yield
and
169.7hhhand
and
203.2
respectively,
both
inApril
April
2012. Reference yield 90.6 h and array yield 76.9 h in May 2011 were abnormal compared to yields in
2012.Reference
Referenceyield
yield90.6
90.6h hand
andarray
arrayyield
yield
76.9
May
2011
were
abnormal
compared
yields
in
2012.
76.9
hh
inin
May
2011
were
abnormal
compared
to to
yields
in the
the adjacent months as well as the yields in May of other years. This was a result of system data
the adjacent
months
asaswell
as theinyields
inother
Mayyears.
of other
years.
This
was
a result
of unavailability
system data
adjacent
months
as
well
the
yields
May
of
This
was
a
result
of
system
data
unavailability from 19 May to 8 June 2011 as shown in Figures 4a,b and 6. From 26 June to 23 July
unavailability
19
May
to 8 June
2011 as4a,b
shown
Figures
and
6.July
From
26due
June to
23 July
from
19
May
to to
8from
June
2011
asmeasurement
shown
in Figures
andinin
6.Figures
From
264b4a,b
June
2013,
irradiation
2013,
due
irradiation
error shown
andto
6, 23
array
yields
weretogreater
2013,than
duereference
to error
irradiation
error
in Figures
4b andthan
6, array
yields
were greater
measurement
shownmeasurement
in Figures 4b and
6, shown
array yields
were greater
reference
yields.
yields.
than reference yields.
Figure 7. Monthly reference yield, array yield, and PR from 1 January 2011 to 31 December 2015.
Figure 7. Monthly reference yield, array yield, and PR from 1 January 2011 to 31 December 2015.
Figure 7.7,Monthly
reference
array the
yield,
andin
PRJuly
from2013
1 January
2011extremely
to 31 December
In Figure
the monthly
PRyield,
excludes
data
to avoid
high2015.
values due
to measurement errors of irradiation. The monthly PR varied from 76.4% to 91.6%, and no obvious
seasonal variation was observed.
Energies
2016,
342
Energies
2016,
9, 9,
342
9 of915
of 15
In Figure 7, the monthly PR excludes the data in July 2013 to avoid extremely high values due to
measurement
errors
of irradiation.
The monthly
PR varied from
76.4%
91.6%, and
seasonal
Investigation
of daily
and monthly
PV performance
shows
thattooutliers
dueno
to obvious
snowing,
shading,
variation
was observed.
system
down-time,
data availability, and measurement errors complicate performance metrics, making
Investigation
ofthe
daily
and monthly
PVarray
performance
shows that outliers due to snowing,
PR inefficiently
reflect
response
of the PV
to irradiation.
shading,
system down-time,
data itavailability,
andclear
measurement
errors complicate
performance
From analysis
in this section,
will become
that performance
data points
in the early
metrics,and
making
PR inefficiently
reflect
response is
oflow,
the PV
irradiation.
morning
late evening
when the
POAthe
irradiation
andarray
datato
points
on cloudy days with large
Frominanalysis
in thisas
section,
it will
become
clear are
thatfar
performance
data points
the early
morningon
fluctuation
irradiation,
well as
certain
outliers,
more unreliable
thaninthose
at mid-day
and
late
evening
when
the
POA
irradiation
is
low,
and
data
points
on
cloudy
days
with
large
fluctuation
sunny days and need to be filtered out in order to obtain more reliable long-term performance metrics.
in irradiation, as well as certain outliers, are far more unreliable than those at mid-day on sunny days
andPerformance
need to be filtered
in order
to obtain more reliable long-term performance metrics.
4.1.4.
afterout
Data
Filtering
To exclude
influences
fromFiltering
the aforementioned factors, the following steps were adopted to select
4.1.4.
Performance
after Data
appropriate data points:
To exclude influences from the aforementioned factors, the following steps were adopted to
select
appropriate
points:
‚
Discarding
datadata
points
with POA irradiation less than 600 W/m2 to reduce the impact of solar
and solar
• angle-of-incidence
Discarding data points
withspectrum.
POA irradiation less than 600 W/m2 to reduce the impact of solar
‚
Eliminating
data points
change of POA irradiation is more than 20 W/m2 /min to reduce
angle-of-incidence
andwhen
solar the
spectrum.
• theEliminating
data fluctuations
points wheninthe
change ofonPOA
irradiationofisperformance
more than 20
W/m2/min to
impact of large
irradiation
the precision
metrics.
reduce the
impactbased
of large
in irradiation
on theexceeding
precision of
performance
metrics.
‚
Removing
outliers
onfluctuations
instantaneous
PR. Data points
the
lower or upper
limit of
• PR,
Removing
outliers
based
on
instantaneous
PR.
Data
points
exceeding
the
lower
or
upper
limit
and data points with large deviation from the mean PR over a given period are considered
of
PR,
and
data
points
with
large
deviation
from
the
mean
PR
over
a
given
period
are
outliers. In this study, the lower limit and upper limit of instantaneous PR are 75% and 100%,
considered
outliers.
In
this
study,
the
lower
limit
and
upper
limit
of
instantaneous
PR
are
75%
respectively, and the limit of deviation is ˘5%.
and 100%, respectively, and the limit of deviation is ±5%.
Figure
8 depicts
and the
themonthly
monthlyaverage
averageambient
ambient
temperature
Figure
8 depictsthe
themonthly
monthlyPR
PRafter
afterdata
data filtering
filtering and
temperature
of of
selected
seasonalityand
andranged
rangedfrom
from84%
84%toto93%
93%
over
selecteddata
datapoints.
points. PR
PR showed
showed a
a strong
strong seasonality
over
thethe
observation
period.
This
seasonality
resulted
from
the
seasonal
variation
of
ambient
temperature,
observation period. This seasonality resulted from the seasonal variation of ambient temperature,
wind
speed,
and
remaining
andsolar
solarspectrum.
spectrum.
wind
speed,
and
remainingimpacts
impactsof
ofsolar
solarangle-of
angle-of incidence
incidence and
Figure8.8.Monthly
MonthlyPR
PR and
and ambient
ambient temperature
Figure
temperatureafter
afterdata
datafiltering.
filtering.
Annual
DegradationRate
Rate
4.2.4.2.
Annual
Degradation
The
three
components
classical
decomposition,
the the
trend,
the seasonality,
and the
The
three
components
afterafter
classical
decomposition,
the trend,
seasonality,
and the irregularity
irregularity are shown in Figure 9 accompanied by the original monthly PR. The trend shown in
are shown in Figure 9 accompanied by the original monthly PR. The trend shown in Figure 9b is
Figure 9b is obtained by applying a two-step centered twelve-month moving average expressed in
obtained by applying a two-step centered twelve-month moving average expressed in Equation (7)
Equation (7) to the original monthly PR in Figure 9a. A linear regression is applied to the trend to
to the original monthly PR in Figure 9a. A linear regression is applied to the trend to obtain the
obtain the degradation rate. The fitted line using least square algorithm and its coefficient of 2
degradation rate. The
fitted line using least square algorithm and its coefficient of determination R
determination R2 are also shown in Figure 9b. The coefficient of determination is a regression
arestatistic
also shown
in
Figure
coefficient
determination
a coefficient
regressionof
statistic
which measures
which measures9b.
theThe
goodness
of fit.ofHigher
values of is
the
determination
imply
thebetter
goodness
of
fit.
Higher
values
of
the
coefficient
of
determination
imply
better
agreement
between
agreement between the fitted line and the observed data points [11]. The steps to obtain
the fitted line and the observed data points [11]. The steps to obtain seasonal component and the
remaining irregular component are well explained in Section 3.2. The estimated annual degradation
rate was 0.44% using Equation (9).
Energies 2016, 9, 342
10 of 15
seasonal
component
and the remaining irregular component are well explained in Section 3.2.
Energies 2016,
9, 342
10 ofThe
15
10 of 15
estimated annual degradation rate was 0.44% using Equation (9).
seasonal component and the remaining irregular component are well explained in Section 3.2. The
estimated annual degradation rate was 0.44% using Equation (9).
Energies 2016, 9, 342
(a)
(a)
(b)
(b)
(c)
(d)
(c)
(d)
Figure 9. Classical times series decomposition of the monthly PR: (a) original signal; (b) component
Figure 9. Classical times series decomposition of the monthly PR: (a) original signal; (b) component of
ofFigure
trend and
its linear
regression
line; (c) component
of
seasonality;
(d)original
component
of(b)
irregularity.
9. Classical
times
series
decomposition
of the
monthly
PR:(d)
(a)
signal;
component
trend
and its
linear regression
line;
(c) component
of seasonality;
component
of irregularity.
of trend and its linear regression line; (c) component of seasonality; (d) component of irregularity.
Performanceofofthe
theMono-Si
Mono-SiPV
PVSystem
System
4.3.4.3.
Performance
4.3. Performance of the Mono-Si PV System
The monthly PR of the mono-Si PV system installed at the same place and studied during the
The monthly PR of the mono-Si PV system installed at the same place and studied during the
The monthly
of the mono-Si
installed
at the
same process
place and
studied
during
the
same period
of timePR
is shown
in FigurePV
10 system
after a similar
data
filtering
applied
to the
UMG-Si
same period of time is shown in Figure 10 after a similar data filtering process applied to the UMG-Si
same
period
of time decomposition
is shown in Figure
10 after
a similar
datathe
filtering
applieddegradation
to the UMG-Si
PV
system.
Classical
is used
again
to obtain
annualprocess
performance
rate
PV system.
Classical
decomposition
isisused
again
to
obtainthe
theannual
annualperformance
performance
degradation
rate
PV
system.
Classical
decomposition
used
again
to
obtain
degradation
rate
of the mono-Si PV system. The trend component and its linear regression are shown in Figure 11. The
of the
mono-Si
PV
system.
The
trend
component
and
its
linear
regression
are
shown
in
Figure
of the mono-Si
PV system. The
and its linear regression are shown in Figure 11. The 11.
derived
annual degradation
ratetrend
was component
0.71%.
The derived
derivedannual
annualdegradation
degradation
rate
0.71%.
rate
waswas
0.71%.
Figure 10. Monthly PR and ambient temperature for mono-Si PV.
Figure 10. Monthly PR and ambient temperature for mono-Si PV.
Figure 10. Monthly PR and ambient temperature for mono-Si PV.
These
over aa year’s
year’s time,
time,with
withlarger
largerPR
PRinincooler
cooler
Thesetwo
twoPV
PVsystems
systemsshow
showaasimilar
similar pattern
pattern of
of PR
PR over
These
two
PV
systems
show
a
similar
pattern
of
PR
over
a
year’s
time,
with
larger
PR
in
cooler
winter
PV system
system was
wasinstalled
installedalmost
almostthree
three
wintermonths
monthsthan
thanininhotter
hottersummer
summer months.
months. The
The mono-Si
mono-Si PV
years
earlier
UMG-Si
PV
At
beginning
ofPV
thesystem
common
observation
periodthe
the
winter
months
than
in
summer
months.
The
mono-Si
was
installedperiod
almost
three
years
earlierthan
thanthe
thehotter
UMG-Si
PV system.
system.
At the
the
beginning
of
the
common
observation
values
of
PR
for
two
systems
were
almost
same
(Figure
8
vs.
Figure
10)
and
the
degradation
years
earlier
than
the
UMG-Si
PV
system.
At
the
beginning
of
the
common
observation
period
values of PR for the two systems were almost the same (Figure 8 vs. Figure 10) and the degradation the
rate
PV
higher
than
that
of
the
UMG-Si
PVFigure
system;
hence,
itisisdegradation
expected
values
ofofthe
PR
for
the two
systems
were
almost
the
same
(Figure
8 vs.
10)
and it
the
rateof
themono-Si
mono-Si
PVsystem
systemwas
was
higher
than
that
of the
UMG-Si
PV
system;
hence,
expected
rate of the mono-Si PV system was higher than that of the UMG-Si PV system; hence, it is expected
that the PR of the mono-Si PV system at the beginning of its life would have been higher than that of
the UMG-Si PV system.
Energies 2016, 9, 342
11 of 15
Energies
11 of 15
that the2016,
PR 9,of342
the mono-Si PV system at the beginning of its life would have been higher than that
of
Energies 2016, 9, 342
11 of 15
the UMG-Si PV system.
that the PR of the mono-Si PV system at the beginning of its life would have been higher than that of
the UMG-Si PV system.
Figure11.
11.Trend
Trendcomponent
component and
and its
its linear
linear regression
Figure
regressionfor
formono-Si
mono-SiPV.
PV.
Figure 11. Trend
and its linear
regression
for mono-Si
PV. of the UMG-Si PV
The derived degradation
ratecomponent
of the mono-Si
is higher
than that
The
derived degradation rate
of the mono-SiPV
PVsystem
system
is higher
than that
of the UMG-Si
system showing that PV modules made of UMG-Si have the potential to retain high quality in
PV system
showing
that
PV
modules
made
of
UMG-Si
have
the
potential
to
retain
quality
The derived degradation rate of the mono-Si PV system is higher than that of thehigh
UMG-Si
PV in
long-term performance. But it is difficult to conclude that PV modules made from UMG-Si have
long-term
performance.
But
it
is
difficult
to
conclude
that
PV
modules
made
from
UMG-Si
system showing that PV modules made of UMG-Si have the potential to retain high quality have
in
smaller degradation rates than modules made from mono-Si since other factors, including effects of
smaller
degradation
rates than
made
from mono-Si
since
other made
factors,
including
effects
long-term
performance.
But itmodules
is difficult
to conclude
that PV
modules
from
UMG-Si
have of
encapsulant and back sheet of PV modules, influences of dirtiness on the surface, and impacts of
encapsulant
and back sheet
PVmodules
modules,
influences
of dirtiness
the surface,
and impacts
of other
smaller degradation
rates of
than
made
from mono-Si
sinceon
other
factors, including
effects
of
other components like inverter of PV systems, are not taken into account.
encapsulant
and
back
sheet
of
PV
modules,
influences
of
dirtiness
on
the
surface,
and
impacts
of
components like inverter of PV systems, are not taken into account.
other
components
likePerformance
inverter of with
PV systems,
not
taken into
account.
4.4. Comparison
of PV
Different are
Data
Filtering
Criteria
4.4. Comparison of PV Performance with Different Data Filtering Criteria
We have shown
PR as a function
of POAData
irradiation
Figure 3, and we filtered out the data
4.4. Comparison
of PV Performance
with Different
FilteringinCriteria
We have
PR as a function
of POA
irradiation
in Figure
3, andrate
we estimation.
filtered outIn
thethis
data
2 for PV
points
withshown
POA irradiation
less than
600 W/m
degradation
2
We
have
shown
PR
as
a
function
of
POA
irradiation
in
Figure
3,
and
we
filtered
out
the
data
points
with POA
lessthreshold
than 600 of
W/m
PV degradation
estimation.
In this
subsection,
2 to 800
subsection,
weirradiation
will vary the
POAfor
irradiation
for datarate
filtering
from 200
W/m
2 for PV degradation rate
2 to
2 inthis
with
POA
irradiation
less
than 600for
W/m
estimation.
In
wepoints
will
vary
the
threshold
of
POA
irradiation
data
filtering
from
200
W/m
800
W/m
steps
2
2
W/m in steps of 200 W/m to observe the impact on monthly PR and degradation rate.
2
2
subsection,
weobserve
will vary
the
threshold
of POAPR
irradiation
for data filtering
from 200 W/m to 800
of 200
W/m
to
the
impact
on
monthly
and
degradation
rate.
Figure 12 depicts monthly PR for both PV systems with various data filtering thresholds of POA
2 in steps of 200 W/m2 to observe the impact on monthly PR and degradation rate.
W/m
Figure
12The
depicts
monthly
PR forhigher
both PV
systems
with
various remained
data filtering
thresholds
POA
irradiation.
pattern
of PR being
in winter
than
in summer
almost
the sameof
with
FigureThe
12 depicts
monthly
PR forhigher
both PV systems
with
various
data filtering
thresholds
of POA
irradiation.
pattern
of PR
being
winter
thanincreasing
in summer
remained
same
various thresholds
for the
whole
observationin
period.
With
thresholds,
PR almost
in eachthe
month
irradiation. The pattern of PR being higher in winter than in summer remained almost the same with
with
various However,
thresholds
themonths,
whole observation
period.
With
increasing
thresholds,
PR in
each
decreased.
infor
some
especially May
2015, PR
was
unusual with
suspiciously
high
various thresholds for the whole observation
period. With
increasing thresholds, PR in each month
2
2
values
with lower
thresholds
(200 W/m
andespecially
400 W/mMay
) of 2015,
POA PR
irradiation
for data
filtering.
The
month
decreased.
However,
in some
months,
was unusual
with
suspiciously
decreased. However, in some months, especially
May 2015, PR
unusual with suspiciously high
2 and
2 ) was
unusually
high values
of PR with lower
thresholds
were
explained
by
theirradiation
fact that in for
Maydata
2015filtering.
there
high
values with
lower thresholds
(200 W/m
400
W/m
of
POA
values with lower thresholds (200 W/m2 and 400 W/m2) of POA irradiation for data filtering. The
more cloudy
than
in adjacentwere
months
and in May
of other
years.
Thewere
unusually
high (rainy
valuesorofovercast)
PR withdays
lower
thresholds
explained
by the
fact that
in This
Mayfact
2015
unusually high values of PR with lower thresholds were explained by the fact that in May 2015 there
was
demonstrated
by
the
observed
monthly
reference
yield
in
May
2015
which
was
less
than
in
Aprilyears.
or
there were more cloudy (rainy or overcast) days than in adjacent months and in May of other
were more cloudy (rainy or overcast) days than in adjacent months and in May of other years. This fact
June
2015.
is shown in Figure
3 that
on cloudy
days instantaneous
PR was
greater
than
on sunny
days
This
fact
wasItdemonstrated
by the
observed
monthly
reference
yield
May
2015less
which
less
than
was
demonstrated
by the observed
monthly reference
yield
in May
2015inwhich
was
thanwas
in April
or
at
low
levels
of
POA
irradiation,
leading
to
the
unusually
high
values
of
monthly
PR
with
lower
data
in April
or June
2015. ItinisFigure
shown
in Figure
3 that
oninstantaneous
cloudy days PR
instantaneous
PR was
greater
than
June 2015.
It is shown
3 that
on cloudy
days
was greater than
on sunny
days
filtering
thresholds
POA of
irradiation.
The unusual
PR intoMay
2015
was reduced
for theofUMG-Si
PV
onat
sunny
days
at POA
lowoflevels
POA
irradiation,
leading
thevalues
unusually
high values
monthly
low levels
of
irradiation,
leading
to the unusually
high
of monthly
PR with
lower
dataPR
system and eliminated for the mono-Si PV system by increasing the threshold.
with
lowerthresholds
data filtering
thresholds
of POA
The
unusual
PR reduced
in May 2015
was
reduced
filtering
of POA
irradiation.
The irradiation.
unusual PR in
May
2015 was
for the
UMG-Si
PVfor
thesystem
UMG-Si
system and
eliminated
forsystem
the mono-Si
PV system
by increasing the threshold.
andPV
eliminated
for the
mono-Si PV
by increasing
the threshold.
(a)
(a)
Figure 12. Cont.
Energies
2016,
9, 342
Energies
2016,
9, 342
Energies 2016, 9, 342
1215
of 15
12 of
12 of 15
(b)
(b)
Figure 12. Monthly PR for various data filtering thresholds of POA irradiation: (a) UMG-Si PV;
Figure
Monthly PR
PR for
for various
various data
data filtering
(a) UMG-Si
UMG-Si PV;
PV;
Figure 12.
12. Monthly
filtering thresholds
thresholds of
of POA
POA irradiation:
irradiation: (a)
(b) mono-Si PV.
(b)
(b) mono-Si
mono-Si PV.
PV.
Figure 13 presents the estimated annual degradation rates with different data filtering thresholds
Figure 13 presents the estimated annual degradation rates with different data filtering thresholds
of POA irradiation for two PV systems using the classical decomposition method. Degradation rate
of increased
POA
for
systems
using
the
classical
decomposition
method.
Degradation rate
POA irradiation
irradiation
for two
two PV
PV
systemsfrom
using
theW/m
classical
decomposition
method.
2 to 600
with increasing
thresholds
200
W/m2, and tended
to be steady above
22 to 600 W/m22 , and tended to
increased
with
increasing
thresholds
from
200
W/m
with
increasing
thresholds
from
200
W/m
600
W/m
,
and
tended
to
be steady
above
2
600 W/m which was obvious for the mono-Si PV system while the tendency was weaker
for above
the
2 which was obvious for the mono-Si PV system while the tendency was weaker for the
2
600UMG-Si
W/m
W/m which
was based
obvious
for the we
mono-Si
PVvalues
systemwith data filtering threshold of 600 W/m2the
PV system;
on which,
select the
2 as2
UMG-Si
PV
based
on
the
values
with
data
filtering
threshold
of 600
W/m
UMG-Si
PVsystem;
system;
based
onwhich,
which,we
weselect
select
the
values
with
data
filtering
threshold
of 600
W/m
as the final
estimated
annual
degradation
rates
for
the
two
PV
systems.
the
final
estimated
annual
degradation
rates
for for
thethe
twotwo
PV PV
systems.
as the
final
estimated
annual
degradation
rates
systems.
Figure 13. Degradation rate with different thresholds for data filtering
5. Performance Comparison
Figure 13.
13. Degradation
Degradation rate
rate with
with different
different thresholds
thresholdsfor
fordata
datafiltering.
filtering
Figure
To better characterize the long-term field performance of the investigated UMG-Si PV system
5. Performance
Comparison
5.
Performance
Comparison
and
the mono-Si
PV system at the same place, we compare PR and Rd with conventional crystalline
silicon-based
PV systems shown
in Table field
2.
To better
better characterize
characterize
the long-term
long-term
field performance
performance of
of the
the investigated
investigated UMG-Si
UMG-Si PV
PV system
system
To
the
and the
the mono-Si
mono-Si PV
PV system
system at
at the
the same
same place,
place, we
we compare
compare PR
PR and
and RRdd with
with conventional
conventional crystalline
crystalline
and
silicon-based
PV
systems
shown
in
Table
2.
silicon-based PV systems shown in Table 2.
Energies 2016, 9, 342
13 of 15
Table 2. Performance comparison with conventional crystalline silicon modules.
Technology
PR (%)
Rd (%)
Reference
poly-Si
90–110
0.24–0.46
[15]
mono-Si
90–110
0.64–0.92
[15]
poly-Si
mono-Si
poly-Si
mono-Si
poly-Si
75–98
65–98
92.9
60–62
76–95
0.78–1.30
0.77–1.37
[14]
[14]
[22]
[23]
[24]
mono-Si
0.25
[25]
poly-Si
0.22
[26]
mono-Si
0.71
UMG-Si
84-93
0.44
Present
study
Present
study
Comments
Data filtering: G > 750 W/m2 , stability, and outliers
Analytical: linear regression on monthly PR (3 years)
Data filtering: G > 750 W/m2 , stability and outliers
Analytical: linear regression on monthly PR (3 years)
Analytical: classical decomposition on monthly PR (5 years)
Analytical: classical decomposition on monthly PR (5 years)
Annual average daily performance ratio of 1 year
Annual average monthly performance ratio of 3 years
Monthly performance ratio of 1 year
Analytical: linear regression on monthly maximal power
using PVUSA (8 years)
Average decay of maximal power (I-V curves) of 70
modules over 20 years
Data filtering: G > 600 W/m2 , stability, and outlier
Analytical: classical decomposition on monthly PR (5 years)
Data filtering: G > 600 W/m2 , stability, and outlier
Analytical: classical decomposition on monthly PR (5 years)
It is indicated by Table 2 that the performance of the UMG-Si modules and the performance
of the mono-Si PV system fall in the performance range of crystalline silicon modules, though
the performance varies from one system to another within a broad range. The wide variation of
performance could result from factors such as difference in design of modules, local climate, and
operating conditions of each system. Therefore, the performance of the UMG-Si PV system is acceptable
compared to conventional crystalline silicon modules.
6. Conclusions
In this study, long-term performance of a 1.26 kW UMG-Si PV system installed at NREL is
investigated by analyzing continuously monitored data from 1 January 2011 to 31 December 2015.
To make the investigation more meaningful, the performance of a mono-Si PV system installed at the
same place and studied during the same period of time is presented for reference.
Analysis of the instantaneous, daily, and monthly performance shows that:
‚
‚
‚
PR tends to be high and steady at high levels of POA irradiation where the impacts of solar
angle-of-incidence and solar spectrum are weak.
PR is more variable and unreliable under cloudy conditions where irradiation largely fluctuates.
Outliers due to snowing, shading, system down-time, data availability, measurement errors, etc.
make PR inefficiently reflect the response of the PV array to irradiation.
Hence, it is necessary to discard data points at low levels of irradiation and data points with
large fluctuations in irradiation, as well as outliers, to obtain a more reliable PR. After data filtering,
the monthly PR ranged from 84% to 93% over the observation period and showed strong seasonality.
The impact of POA irradiation threshold for data filtering on PR and estimated degradation rate is also
studied, showing that the estimated value of degradation rate increases with increasing thresholds
from 200 W/m2 to 600 W/m2 and tends to be steady above 600 W/m2 .
Long-term performance annual degradation rate, Rd , was 0.44% for the UMG-Si PV system and
0.71% for the mono-Si PV system with a data filtering threshold of 600 W/m2 . A comparison of PR and
Rd to conventional crystalline silicon-based PV systems shows that the performance of the UMG-Si PV
system is acceptable.
These results are of value for further performance analysis of the UMG-Si PV system, such as
short-term and long-term power output prediction. More importantly, this long-term performance
evaluation provides a preliminary understanding of UMG-Si PV outdoor performance, and offers
Energies 2016, 9, 342
14 of 15
reference information for better and wider use of UMG-Si PV modules. Furthermore, the rationalization
for the importance of data filtering will benefit analysis of long-term performance of other PV systems.
Acknowledgments: The first author would like to thank City University of Hong Kong for financial support on
his Ph.D. research.
Author Contributions: Chao Huang developed the main part of this work. Michael Edesess contributed in
writing the parts and editing the document. Alain Bensoussan and Kwok L. Tsui critically reviewed the paper and
contributed in finalizing the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AM
AOI
ARIMA
NREL
POA
PR
PV
UMG-Si
Air Mass
Angle of Incidence
Autoregressive Integrated Moving Average
National Renewable Energy Laboratory
Plane of Array
Performance Ratio
Photovoltaic
Upgraded Metallurgical Grade Silicon
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