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Professional Science Masters – Solar Energy Engineering & Commercialization Arizona State University

July 30, 2013

SUNSHINE ACRES CHILDREN’S HOME SOLAR ARRAY SYSTEM MONITORING AND MAINTENANCE

Tyler G. Pearce Professional Science Masters Student in Solar Energy Engineering & Commercialization

Sunshine Acres Children’s Home 3405 N. Higley Road

Mesa, AZ, USA

ABSTRACT

Sunshine Acres Children’s Home (SA) has installed nearly

500 kWdc of solar arrays across their campus over the past five

years. In the next five years, SA hopes to double that figure in

order to annually self-generate all the energy the property

requires. As their solar portfolio grows, monitoring and

maintenance of SA’s photovoltaic (PV) systems will become

increasingly important. For this reason, the purpose of this

applied project was to provide SA: (1) a proof-of-concept, real-

time monitoring system and educational display, or dashboard,

which tracks and assesses the performance of the Dining Hall’s

PV system and (2) a site wide maintenance program. This paper

discusses the development of the dashboard and the use therein

of the performance metrics Performance Ratio and Performance

Index. To obtain a Performance Index, three performance test

methods, including PVUSA, are applied to a set of collected

performance data, and the results of the regressions are

compared. The dashboard and the maintenance program that

was developed for SA are also described.

INTRODUCTION The “miracle in the desert” known as Sunshine Acres

Children’s Home (SA) was founded in 1954 when James and

Vera Dingman purchased 125 acres in rural Mesa for the

express purpose of taking in and caring for the community’s

displaced children. The family and friends of Mr. and Ms.

Dingman carry on that noble ministry today. Consequently, SA

continues to be a tremendous force for good in Mesa and a

beneficiary of the community’s generosity.

One testament to the liberality of the community is SA’s

success in advancing their solar power initiative. When SA was

developing their master plan in 2008, they decided that they

would self-generate as much energy as they consume by the

year 2018 through the use of photovoltaic (PV) panels. Since

that time many people have contributed time, money, expertise,

and materials (including PV panels) in order for SA to realize a

portion of that vision. Five years later, SA has nearly 500

kWdc of PV generating electricity for 18 of their 40 buildings,

and it is estimated that this amount of solar power is supplying

half of the property’s energy needs. This means that SA, in

order to fully realize their goal, will add at least 500 kWdc over

the next five years. When finished, the campus will be home to

over 1 MWdc of PV panels.

Self-generation, however, is just as much a responsibility

as it is a blessing for SA. While a 300 kWdc array is operated

and maintained by Green Choice Solar through a Power

Purchase Agreement, the upkeep of the remaining 200 kWdc

falls to the staff of SA – a crew that is not very familiar with the

technology. Naturally, SA’s Facilities Management Office and

Solar Committee are interested to know how well their PV

systems are performing and what they must do to maintain the

power production of the arrays. In addition, SA is anxious to

tell its solar story to the residents (many of whom are young

and curious), staff, and community supporters.

The challenge of this applied project was to address all

three of these needs – educational outreach, performance

evaluation, and maintenance planning. Consequently, this

project’s objectives were twofold: (1) implement a monitoring

system/educational display for one of SA’s many arrays (2) and

create a site-wide maintenance program that is applicable for all

of SA’s existing and future solar arrays. This paper details what

was done to accomplish these two aims.

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FIGURE 1: SUNSHINE ACRE DINING HALL ARRAY AND WEATHER STATION.

SYSTEM MONITORING Choosing a Solar Array

Sunshine Acres’ first PV array was installed in April of

2010. As part of Salt River Project’s (SRP) Earth Wise Solar

Energy Incentive Program, the utility designed, donated,

erected, and commissioned a 13 kWdc PV system for SA’s

Dining Hall as shown in Fig. 1. The array is installed on the

roof of the building, is inclined at the roof’s pitch – nearly 10°

from the horizon – and faces just 10° west of south. It is

comprised of 5 parallel strings of 12 panels in series. These 60

Sharp multi-crystalline silicon PV modules feed a Fronius

three-phase inverter. In addition to the array, SRP installed a

weather station – sited on the same roof – which consisted of

one ambient temperature sensor, an anemometer (wind speed

sensor), and a thermopile solar radiometer or pryranometer.

These instruments, as well as direct current (DC) current and

voltage transducers (located in the array’s DC combiner box)

and a pulse output from the solar production energy meter

(located on the alternating current (AC) side of the inverter),

were wired into a Campbell Scientific CR10X data logger. The

data logger was programmed to store 15 minute averages of the

weather, current, and voltage measurements and the total AC

energy produced over the same time interval.

At the time of the array’s installation, however, SA had not

yet established a site-wide computer network. As a result, the

data logger was isolated, and the only way one could obtain the

data was to download it through a direct connection to the

device. Such inaccessibility limited the usefulness of the logger

and data it recorded; the data was constantly being overwritten.

But in the years that have elapsed, SA has created a network

into which the CR10X could be integrated. For this reason, and

the reasons that follow, the Dining Hall array was chosen as the

subject of this project.

First, the software suite that Campbell Scientific sells with

their data loggers make it possible to create a graphical

interface and unique visual display. This dashboard could be

used to show valuable array performance information in real

time like DC and AC power. Performance parameters such as

Performance Index (PI) and Performance Ratio (PR) could also

be calculated and displayed alongside power and energy charts.

When employed in the dashboard, educational graphics such as

schematics and blurbs could be used to attract the attention of

people who visit SA and explain to them how solar energy

works. Furthermore, Campbell Scientific’s software enables

the user to publish the dashboard on the internet for easy access

and viewing by a broad audience.

Second, the data logger could be reprogrammed to produce

an expected power output curve for the array – one of the

dashboard’s desired features. This singular attribute would

allow the calculation of PI, which would enable SA to instantly

gauge the performance of the Dining Hall array. Many inverter

manufacturers, Fronius included, can provide the system owner

the ability to access his array’s performance data online.

Among the information typically included on such a site is the

average power output of the array and daily and monthly

energy yields. Some inverter websites import modeled monthly

energy yields from PV Watts or some other software tool and

compare the actual monthly yield to the modeled. But very few,

if any, inverter websites produce an expected power

performance curve for an array. This unique feature could help

the system owner catch and address performance issues when

they begin to appear.

Third, the data logger could easily accept the addition of

new weather instruments as spare inputs were available.

Global Horizontal Irradiance (GHI) data was already being

recorded, yet a Plane of Array (POA) pyranometer as well as a

module temperature sensor would be needed to associate the

power output of the array with the two variables of irradiance

and module temperature.

Last, the data logger would also make it possible to archive

historical data for later performance analysis and

troubleshooting. From the data, SA could generate monthly

and annual system reports that compare actual energy values to

modeled energy values. Performance metrics, PI and PR, could

WEATHER STATION

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also be trended over time to provide SA further insight into the

operation of the Dining Hall array.

For these reasons, the Dining Hall array, along with its

accompanying data logger, provided the possibility and

functionality necessary to meet the first project objective. A

Letter of Recommendation was written to SA’s Solar

Committee recommending the Dining Hall array as the prime

candidate for the project. The required hardware, software, and

support was also requested in the letter. SRP was contacted,

and the utility generously furnished two newly calibrated

pyranometers (LiCor LI200X), a module temperature sensor, a

network link for the data logger, the data logger software, and

wiring diagrams.

In May 2013, the new instruments and hardware were

installed and wired to the data logger per the provided

instructions and schematics. The module temperature sensor

was placed in the center of a cell on a PV panel at the front and

center of the array. One pyranometer was installed level to the

horizon to acquire GHI measurements while the other LI200X

was aligned to the tilt and orientation of the array – as near as

was possible – in order to obtain the POA irradiance. The data

logger was reprogrammed to accept the two new inputs and

provide 5 and 15 minute averages/totals. The GHI and ambient

temperature readings were then verified against measurements

being made by a local weather station, NREL’s Southwest Solar

Research Park, which is at Phoenix Sky Harbor Airport

approximately 20 miles away. These readings seemed to be in

general agreement, and the POA irradiance, given its small tilt

angle, and module temperature values seemed reasonable.

With the weather station fully functional, array performance

data could be collected and analyzed to determine the

relationship between the AC power output of the array and at

least two of the following independent variables: irradiance,

ambient temperature, wind speed, and/or module temperature.

TABLE 1: DATA LOGGER INSTRUMENTS & READINGS

Instrument Measurement

LI200X (1) Global Horizontal Irradiance (W/m2)

LI200X (2) Plane of Array Irradiance (W/m2)

108 L Ambient Temperature (°C)

110 PV PV Module Temperature (°C)

03101 L Wind Speed (m/s)

VT7-010B DC Voltage Transducer (V)

CTL-100/101 DC Current Transducer (A)

KYZ Pulse AC Energy – from Inverter (kWh)

Determining the Expected Power Output Curve

Background – Several methods currently exist to relate

two or more of the above independent variables to the AC

power output of a PV array for the purpose of determining a

power rating at a certain reporting condition. Only three

approaches will be considered here. For each of these methods,

data for each of the independent variables must be collected

over a certain period of time using a data acquisition system

(DAS) such as the CR10X data logger.

The simplest method, employed by Senior Engineer Jim

Hansen of Arizona Public Service for rating their PV power

plants [1], states that the relationship between the temperature-

corrected AC power, PTC is proportional to the POA irradiance

as signified by the following equation:

𝑃𝑇𝐶 = 𝐴 ∙ (𝑃𝑂𝐴) (1)

Where A is a constant of the regression. PTC is found using the

module temperature, Tm, and the PV panel manufacturer-

provided temperature coefficient for power, CT (%/°C), in this

expression:

𝑃𝑇𝐶 = 𝑃𝑚𝑒𝑎𝑠/{1 + 𝐶𝑇 ∙ (𝑇𝑚 − 𝑇0)} (2)

Where:

Pmeas = Measured AC power (W)

T0 = Reporting condition temperature or 25°C

The expected or estimated power output of the array, Pest, can

then be calculated by:

𝑃𝑒𝑠𝑡 = 𝐴 ∙ 𝑃𝑂𝐴 ∙ {1 + 𝐶𝑇 ∙ (𝑇𝑚 − 𝑇0)} (3)

In plain terms, Eq. (3) implies that the power output of the

array is most significantly influenced by the irradiance lighting

upon it and is slightly affected by the module temperature but

adjusted accordingly. Any power losses resulting from voltage

drops or wire resistance or inverter inefficiency are implicitly

compensated for by considering only the array’s AC power

output.

Another popular system for rating the DC or AC power

output of a PV array is the PVUSA method [2, 3]. This method

has been extensively tested and widely used by the PV industry.

It was developed as part of the PVUSA project sponsored by

Pacific Gas & Electric in the late 1980s and early 1990s.

ASTM standard E2848-11 recommends its use for “acceptance

testing of newly installed photovoltaic systems, reporting of

DC or AC system performance, and for monitoring

photovoltaic system performance” [4]. The PVUSA equation is

as follows:

𝑃 = 𝐼 ∙ (𝐴 + 𝐵 ∙ 𝐼 + 𝐶 ∙ 𝑇𝑎 + 𝐷 ∙ 𝑊𝑆) (4)

Where:

P = AC power (W)

I = Plane of array irradiance (W/m2)

𝑇𝑎= Ambient temperature (C)

WS = Wind speed (m/s)

A-D = Regression constants derived from collected data

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The PVUSA equation makes use of the fact that real AC

electrical power is the product of current, voltage, and power

factor. Array current is directly proportional to POA irradiance

whereas array voltage is a function of the module temperature.

Module temperature, when it is not measured, can be estimated

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FIGURE 2: POA IRRADIANCE & AC POWER, JUNE 16-28 – FILTERED DAYS CIRLCLED.

when irradiance, ambient temperature, and wind speed are

known. Therefore, the PVUSA employs these variables to

represent the affect each of them may have on system voltage.

Power factor, for most grid-tied inverters, is unity. Like the

Hansen method, power losses of the system are imbedded in

the expression.

The third regression analysis system considered here is

known as the King 3-Part method [3]. This approach was

developed by David King of Sandia National Laboratories as

an integrated product of the array performance and inverter

models that were also developed at Sandia. In truth, it is a

three-part method which consists of (1) determining the

effective solar irradiance; (2) defining the operating

temperature of the module using an explicit expression that

accounts for irradiance, temperature, and wind speed; and (3)

employing the results of (1) and (2) to assess the electrical

performance of the array and the inverter in the equation that

follows:

𝑃 = 𝐴1 ∙ 𝐼𝑒 + 𝐴2 ∙ 𝐼𝑒 ∙ (𝑇𝑚 + 273) ∙ ln(𝐼𝑒) +

𝐴3 ∙ 𝐼𝑒 ∙ [(𝑇𝑚 + 273) ∙ ln(𝐼𝑒)]2 +

𝐴4 ∙ 𝐼𝑒 ∙ (𝑇𝑚 − 𝑇0) (5)

Where:

P = AC power (W)

𝐼𝑒 = Effective irradiance (dimensionless)

Tm = Module temperature (C)

A1-A4 = Regression coefficients

According to King, there are several ways to calculate the

effective irradiance; the method being dependent on the type of

device being used to measure the irradiance, e.g., reference cell

or thermopile pyranometer or photodiode [5]. When using a

photodiode such as the LI200X, the effective irradiance is

defined as the quotient of the measured irradiance divided by

the reference irradiance or 1000 W/m2. If soiling is present, the

result may be adjusted using a soiling factor. The module

temperature, if it is not being measured, may be determined

using an empirical expression King defines elsewhere [3,5].

Methodology – On the morning of June 10, 2013, the

Dining Hall array was washed, and data collection commenced.

Data was gathered over the next eighteen days – from June 10

through June 28. Using the on-site weather station and other

sensors, global horizontal and POA irradiances, ambient and

module temperatures, wind speed, and DC current and voltage

measurements were collected every 10 seconds and averaged

over both 5 & 15 minute intervals. The AC energy, measured

by a pulse output from the solar production meter, was totalized

over the same time interval. Once the data was obtained, the

average AC power was calculated and plotted along with the

POA irradiance as shown in Figure 2. Figure 2 depicts only the

last twelve days of the data set since the first six days were

cloudy days.

ASTM E2848-11 guidelines were followed in filtering the

data set for the analysis. For example, the standard and another

publication by Kimber et al. stated that the regression model

error would be minimized by only analyzing clear days, i.e.,

days where the AC power and irradiance trace an almost perfect

bell curve. Thus, data from June 21, 22, & 24 were eliminated.

The data was also screened to remove data points associated

with POA irradiance conditions of less than 500 W/m2. As a

result of both filters, the data set was reduced to 278 data points

(15 minute averages) or nearly 70 hours of excellent data – a

very robust data set as defined by the standard.

Results & Discussion – The regression analyses were

carried out on the filtered data set in the following way. For the

Hansen method, the measured power output of the array was

temperature-corrected using the measured module temperature

and CT = -0.485 %/°C in Eq. (2). The temperature corrected

power values were then plotted against corresponding POA

irradiance values as shown in Fig. 3. Excel’s linear regression

analysis calculated the equation displayed on the chart.

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FIGURE 3: TEMPERATURE-CORRECTED AC POWER VS. POA IRRADIANCE

The multivariable regression analysis for the PVUSA and King

methods was carried out using Excel. It should be noted that

for the King regression, the measured module temperature was

considered in the analysis in lieu of a calculated temperature,

and the effective irradiance was calculated as previously

described.

Each regression analysis performed well. The goodness-

of-fit or R-squared values for all regressions exceeded 0.99.

And the p-value, which indicates the goodness or significance

of the coefficient, is less than 0.05 for each associated variable

in each regression model – a requirement of the ASTM

standard.

Another way of summarizing the results is shown in Table

2 below. The displayed values were produced by calculating

the power output of the PV array for each data point of the data

set with the original inputs (POA, ambient temperature, module

temperature, wind speed) using the three regression equations.

The measured power values were then divided by the calculated

values, which yielded a range of results centered on unity. The

results suggest that the PVUSA regression performed the best

(less spread in the range, lower standard deviation), followed

by the King model. But the results for the simpler Hansen

model are comparable as well.

Table 2: ANALYSIS RESULTS

POA > 500 W/m

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Shaded Unshaded

Indicator Hansen PVUSA King Hansen

Avg. 1.00 1.00 1.00 1.01

Min 0.83 0.86 0.88 0.95

Max 1.06 1.05 1.13 1.06

Std. Dev. 0.037 0.029 0.031 0.017

It is worth noting here that the scatter of data at irradiance

levels less than 700 W/m2 in Fig. 3 indicates that the Dining

Hall array was being shaded for a certain period of the day

while data was being collected. A physical inspection of the

system and a sweep of the data revealed a tree was shading the

northwest corner of the array during the late afternoon. From

the position of the tree and the fact that the data set included the

summer solstice, it was concluded that the tree will not shade

the array every day of the year. Because of this deduction the

data set was again filtered to remove the “shaded” data points

and reanalyzed using the same method described in the

preceding paragraph for the Hansen equation alone. These

results, which are also shown in Table 2, indicate that the

Hansen model’s estimated array power will better represent the

power output of the array when it is not shaded. Although this

exercise was only conducted using the Hansen method, similar

conclusions could be hypothesized for the other two methods.

In the end, the Hansen equation, Eq. (3), was used to

produce the expected power output curve in the dashboard

because the function was dependent on only two variables

(POA and Tm) and did not behave erratically at low irradiance

levels. The equation which resulted from the King regression

analysis, on the other hand, yielded negative values at low

irradiance levels. The equation programmed into the data

logger is:

𝑃𝑒𝑠𝑡 = 11.818 ∙ 𝑃𝑂𝐴 ∙ {1 − 0.00485 ∙ (𝑇𝑚 − 25)} (6)

Concluding Thoughts on the Analysis – The

primary purpose for carrying out the foregoing analysis was to

determine the relationship between the AC power output of the

array and certain weather variables so that the power could be

reasonably estimated on a real-time basis. The array’s

performance could then be assessed by comparing the predicted

and actual power outputs. Of most concern is the ability to

evaluate performance when the array is producing the most

power, i.e., when irradiance levels are greater than 500 W/m2.

The results show that Eq. (6), when calculated, will accurately

estimate the power output at these times. An error analysis of

the results based on the random and bias errors of the

instruments was not part of the scope of this project but may be

of academic interest to some and a topic of future work.

However, it may be reasonably concluded that whatever error

exists is an intrinsic part of the formula. One benefit of this

type of analysis is that one could perform this evaluation

several times throughout the lifetime of the array to ensure that

the model is always accurate.

The Dining Hall Array Dashboard Like other PV system dashboards available from many

inverter manufacturers and third-party companies such as Deck

Monitoring and Also Energy, the intent of the Dining Hall array

dashboard is to display system measurements in real time and

educate viewers – especially children – about how PV solar

power works. Unique to SA’s dashboard, however, is the

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display of an expected power output curve, evaluation of a PI,

and calculation of PR over different time intervals. These

performance parameters are calculated in real-time on the

dashboard and will enable SA to gauge the health of their array

on the same time scale. In addition to the real-time display, raw

data from the DAS is continually collected and archived in a

manner that SA can easily generate monthly and annual system

reports. The combination of all these features will give SA

profound insight into the performance of the array over both

short and long time intervals.

The Dining Hall array dashboard consists of 11 pages

which were created in Campbell Scientific’s Real-Time

Monitoring and Control or RTMC Pro software. This software

is a feature-enhanced version of the basic RTMC program that

ships with LoggerNet – the software that one needs to interface

with Campbell Scientific data loggers like the CR10X. RTMC

Pro enables one to easily manipulate and display data that is

collected by the data logger. The display’s refresh rate is

governed by how often new data is collected and written to an

output file. In the case of SA’s dashboard, 5 minute-averaged

and totaled data is collected every 5 minutes. Thus, the display

is updated every 5 minutes with new data. In addition, RTMC

has been set to automatically tab through each of the dashboard

pages every 30 seconds unless interrupted by the viewer.

Consequently, new data is displayed every time the dashboard

automatically cycles through its presentation. Each of the

dashboard’s pages is briefly described below. The reader can

find accompanying screenshots in Annex A.

Welcome – This page simply introduces the viewer to

Sunshine Acres, “A Solar Powered Community.” The date,

time, and the current temperature, as measured by the Dining

Hall weather station are displayed below pictures of the SA

Administration Office and welcome sign.

Solar Power – The operation of a typical grid-tied PV

system is described in a manner that children can understand.

More importantly, it uses the Dining Hall’s PV array as an

example of how PV works. The diagram shows just how power

from the sun is ultimately converted into power that appliances

in the Dining Hall use. It also explains how the utility will

supply power to the Dining Hall when the solar panels are not

producing electricity. The contents of this page also represent

an outline of the remaining presentation.

Sun – Earth – This is another informative slide whose

aim is to capture the imagination of SA’s children. The main

message of the slide is that the Sun imparts a huge amount of

energy to the Earth each day. This slide, as well as many of the

remaining pages, uses a factoid and question to engage viewers

and encourage them to think.

Weather Station – The Weather Station page answers

the question of the preceding screen through the graphical

display of irradiance data from both pyranometers. The name

and use of each instrument is given and their respective

measurements are displayed.

AC Power – The AC power and POA irradiance are

overlaid on the same graph in this page for the purpose of

showing viewers that the power output of the array is wholly

dependent on sun power. AC power, in this and all other pages

where it is displayed, is found by dividing the array’s AC

energy output by the time interval between readings, which is 5

minutes in this case. A pulse counter in the data logger counts

the number of pulses sent to it by the solar production meter.

Each pulse equals 43.2 Wh. Consequently, the resolution of the

power measurement is 518 W. This makes the AC power

measurement unreliable at low irradiance levels. At higher

irradiance levels, the power output of the array is higher, the

pulse output is quicker, and the resulting power calculation

tends towards the actual power output.

A key feature of the AC Power page is the display of the

performance parameter PR on varying time intervals. PR is

calculated here as recommended by IEC standard 61724 [6].

The equation is:

𝑃𝑅 = 𝐹𝑖𝑛𝑎𝑙 𝑌𝑖𝑒𝑙𝑑 (𝑌 )

𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑌𝑖𝑒𝑙𝑑 (𝑌 )=

𝐸𝑃 ⁄

𝐻𝐼 ⁄

(7)

Where:

PR = Performance Ratio (dimensionless) E = Hourly, daily, monthly, or annual AC energy output of the

array (kWh)

PARRAY = Nameplate power rating at STC (kWDC)

H = Hourly, daily, monthly or annual solar insolation (kWh/m2)

ISTC = STC test irradiance or 1 kW/m2

Simply stated, PR is a common performance metric that

normalizes the AC energy output of a PV system to the

available solar resource. It is, in fact, a ratio of actual

performance to ideal performance. Typical PR values for a

single PV system can range from 0.65 to 0.80. This fluctuation

in PR is due in part to the seasonal performance of PV systems;

PV systems, especially in Arizona, tend to perform better in

spring and fall because these seasons are characterized by an

abundance of sunshine at mild temperatures. PR is normally

calculated on a monthly or annual basis, but more frequent

calculations, e.g. daily, can help the system owner to better

trend the system’s performance and may warn him of

component failures.

In the case of SA’s dashboard, PR is calculated over four

different time intervals: yesterday, hourly, daily or running, and

monthly. Hourly, Running, and Monthly PRs are calculated

as the hourly, daily, and monthly AC energy and solar

insolation totals accumulate over these respective time periods.

In contrast, Yesterday’s PR value is the result of evaluating the

expression, Eq. 7, with yesterday’s total AC energy produced

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and total solar insolation received. This quantity is more useful

to the system owner as hourly and running PR can fluctuate

over the course of the day. The running monthly PR may also

be a valuable performance parameter if the owner notices that it

changes greatly from day to day. A true monthly PR is

calculated elsewhere (outside the dashboard) with monthly

energy totals.

Expected Power – This page tells viewers that the power

output of the PV array can be estimated when the values of

solar irradiance and module temperature are known. The actual

AC power and expected AC power curves are overlaid on the

chart as a visual representation of how well the two quantities

agree. Digital readouts of these values are also shown near the

graph’s legend. The performance parameter, PI, is defined as

the ratio of actual power to expected power [7]:

𝑃𝐼 = (𝐴𝑐𝑡𝑢𝑎𝑙 𝑃𝑜𝑤𝑒𝑟)/(𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑃𝑜𝑤𝑒𝑟) (8)

Because it is a power ratio and not an energy ratio, PI is a real-

time assessment of the array’s performance, and it is used here

to warn the owner of serious system operational issues. In the

case of this dashboard, the usefulness of PI is limited by the

accuracy of Eq. (6) and the resolution of the power

measurement or calculation. The expected range of PI values

over the course of the day (irradiance greater than 500 W/m2) is

from 0.90-1.10 as shown in the display page. However, for PI

calculations made at higher irradiance levels, the PI range

narrows. Thus, the owner will have a better opportunity to

catch more minor operational anomalies if he takes notice of PI

within an hour or two of noon. Average daily PI values are

calculated and stored outside the dashboard as well.

DC to AC – DC voltage and current measurements are

displayed with gauges and digital readouts on this page. DC

power (voltage x current) is calculated and shown in its own

gauge as well. The AC power is calculated and likewise

displayed. Typical values of all above quantities are provided

with the note that such values will occur near noon on a clear

sunny day. The inverter efficiency in percent is displayed as a

digital read-out and is calculated as the ratio of outgoing AC

power to incoming DC power. From an analysis conducted on

historical data, the inverter efficiency should fall within the

range of 85-95% and averages 90%. This single parameter can

help the system owner to determine if any changes in the power

output of the array are related to inverter performance issues.

Energy/Power – The Energy/Power slide is used to

transition the viewer from the previous “power” pages to the

pending “energy” pages of the dashboard. Both energy and

power are generally defined here in terms children can

understand. The viewer can read that electricity is just one form

of energy, and it is delivered to all kinds of electrical gadgets by

power. Customary units of energy and power are also given so

the viewer can better understand information contained in the

other pages of the dashboard.

Daily Energy – In this page, the energy production of the

Dining Hall array over the past week is displayed in a bar graph

as daily totals – the newest record being yesterday’s total. A

digital readout at the top of the screen shows the current day’s

running energy total. In order for the viewer to comprehend

just how much energy has been produced, he is shown just how

many hours of TV he could watch or how many 50 W light

bulbs he could turn on for an hour or how many hours he could

surf the internet on his laptop. These are devices and activities

to which each viewer can readily relate.

Monthly Energy – The Monthly Energy depicts the total

amount of energy the array has produced over the past three

months in another bar graph. This graph, however, displays the

totals differently than the Daily Energy total bar graph; each bar

represents the aggregate of all daily energy totals for all the

previous days of the month. Thus, the height of the last bar

signifies the current month’s running energy total through

yesterday. The most current running total is displayed as a

digital read-out at the top of the screen. The Dining Hall uses,

on average, nearly 14,000 kWh per month. The newest

monthly total is divided by this quantity to show how much of

the Dining Hall’s monthly energy requirement has been

provided by the array.

Plant Profile – This last page provides some detail about

the Dining hall array, that it was donated to SA by SRP and that

it is only one of several PV arrays on the property providing

power to the site.

SYSTEM MAINTENANCE The need for a maintenance program for SA’s solar arrays

is clear. Like so many other individuals and groups, SA has

made a significant investment in PV power systems because it

reduces their operating expenses which in turn increases their

financial ability to develop other important areas of their

enterprise. For SA their most important mission is helping

children in need. As a result, an appropriate maintenance

program will allow SA’s to protect their investment and enlarge

their ministry. The goal here was to create a maintenance

program that is thoughtful of the time commitment required of

SA staff and fitting to their level of expertise.

The first step in creating SA’s solar maintenance program

was to take an inventory of their current solar assets. Each

array was located and key information was collected. Such

information included the PV panel manufacturer; PV panel

STC power rating; the number of modules in the array; the

number of inverters per array, and the inverter manufacturer,

model, and size. Electrical 3-line diagrams for most of the

arrays were located, and technical manuals for each inverter

were downloaded from the respective manufacturer’s website.

A site-plan for the whole of the SA campus was obtained and

updated with the location of each of the arrays inventoried. All

of the above information was placed in, or linked to, a single

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spreadsheet (SA Solar O&M Spreadsheet) that will be given to

SA maintenance staff. Manuals and data sheets for the Dining

Hall’s weather station and DAS were also included the file.

The inventory revealed that there are just over 80 central

inverters and 50 micro-inverters installed on the 500 kWdc of

PV arrays at SA. Of this total, SA is responsible for the upkeep

and maintenance of 38 central inverters, all of the micro

inverters, and nearly 1300 PV panels. Each central inverter, on

average, accepts power from 40 PV panels making SA’s solar

portfolio inverter intensive. It is well known that inverters are

the most frequently the cause of PV system failures and lost

energy production. For these reasons, it seemed justifiable to

focus SA’s monitoring and maintenance efforts at the inverter

level.

The inverter technical manuals were reviewed in order to

discover the manufacturer-recommended maintenance

procedures and the frequency of such activities. The Sunny

Boy manual indicated that regular inverter maintenance ensures

long operating life but did not specifically define “regular” as

monthly, semi-annual, or annual. On the other hand, Power

One recommended an annual inspection of enclosures and wire

terminations and annual cleaning of the inverter. Other

literature on PV systems operation and maintenance and the

Dining Hall array operating manual provided by SRP suggested

that inverters should be inspected twice a year [8].

Consequently, a semi-annual preventative maintenance

schedule was developed in the spreadsheet containing the

inventory and site plan for each of SA’s solar assets. The

schedule recommends which month each array should be

formally inspected, and it contains spaces for linking the

completed inspection report, recording recommended inverter

readings, and making annotations of site observations and

action items. The Dining Hall weather station is also included

on the schedule, and monthly inspection of the system is

suggested mainly to ensure that irradiance readings are not

affected by soiling.

An “Array Inspection Checklist Form” was also created to

help SA staff know what to look for when inspecting the

weather station and solar array. A copy of the form is included

in Annex B for the reader’s reference. The checklist requires

the inspector to note the system inspected, date and time, and

current weather conditions. He is to check the weather station

instruments and clean both pyranometers. The solar array site

is to be inspected for overgrown weeds, destructive erosion,

and wildlife. Array PV panels, wiring and wire management,

support structure, and labeling are also to be checked. It is

suggested in the checklist that the inverters be checked for

proper operation and certain readings taken so array

performance can be trended over time. At least once a year, the

combiner boxes, disconnects, and inverter should be opened

and the condition of the wiring and wiring terminals examined

for loose connections and arcing. Lastly, a place for notes is

also provided. Instructions at the top of the form direct the

inspector to complete and archive the form so a record of

maintenance activity may be kept. Aside from opening

disconnects, electrical boxes, and inverters, the site inspection

does not require an in-depth knowledge of electricity or PV

power systems to be helpful. SA’s staff, if proficient in other

areas of maintenance, should be able to provide discerning eyes

that will prevent system failures and hazards from occurring.

In addition to formal semi-annual inspections and reports,

it is recommended that SA staff make quick informal monthly

walk-throughs of the array sites to check for weeds, dust on the

panels, and the inverter’s operational status. SA should also

make note of the monthly energy yield of each array as reported

by SRP in their electric bills as a bare minimum check of

system performance. A tab in the spreadsheet was created as a

place to record these numbers, and expected energy yield, as

calculated by PV Watts or the like, will be added in the future

for comparison. Doing this very small thing will help SA to

assess the performance of each array on a cursory level. It will

also assure them the PV system is operating properly or alert

them of malfunctioning equipment.

A more ideal system of monthly reporting would include

calculating PR and comparing it with an expected value as

recommended by PV maintenance experts [8, 9]. With the

DAS in place, this can easily be done for the Dining Hall array.

The data logger is programmed to output a data file containing

15 minute averages and totals of the measurements and

calculations it performs. Another spreadsheet was created

(Dining Hall Data – Performance Reports) and connected to

this constantly updating data file for the purpose of calculating

key performance metrics for the PV system. When the

spreadsheet is opened, it automatically imports the newest data,

calculates daily energy yield, PR, average PI, and average

inverter efficiency. This data automatically populates in charts

created for each month of the year. July’s chart is given below

in Fig. 4 as an example of a nearly complete monthly report.

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FIGURE 4: EXAMPLE OF MONTHLY REPORT

The Dining Hall Data spreadsheet automatically computes

the monthly energy yield and PR and populates this information

in an annual chart as well. The annual chart also displays the

expected energy yield and PR as modeled in PV Watts for

comparison. Thus, SA can quickly reference the Dining Hall

Data file to find the array’s historical data and assess its daily

and monthly performance. Should an anomaly be discovered,

SA staff can trace back through the data - from month to day to

hour – to find when and where the error occurred. At the end of

the year, or the beginning of a new year, SA can archive an

annual report by performing a “save-as” operation on the file.

Then by changing the year in the spreadsheet, the Dining Hall

Data file can be used to monitor performance in the new year.

PV system monitoring and maintenance works hand in

hand to ensure that solar arrays are working properly and

performing well. When properly implemented, a monitoring

system can minimize the number of site visits and make those

visits more productive. In the near future, SA will need to

make system monitoring a more important part of their

maintenance strategy so they can better protect their investment

and thereby help more children.

A FUTURE VISION FOR SUNSHINE ACRES There is no doubt that SA will continue to grow their solar

portfolio in the coming years. Efforts are already underway to

design an electrical infrastructure, an electrical spine of sorts,

which would give SA a single interconnection point with SRP

and enable them to add more solar systems in the future. As the

number of arrays increase, SA’s need for a sophisticated

monitoring system will become paramount in protecting their

investment. SA will need a centralized location to store and

display operational data from all of their PV systems.

Fortunately, there exists a handful of companies which

specialize in creating on-line dashboards; dashboards that

aggregate data from a variety of inverter makes and models and

provide the system owner one place to access the performance

data of all their solar sites. One or two of these companies also

have the ability to provide expected power output

measurements (using NREL and Clean Power Research

application programmable interfaces or APIs) and monthly

estimated energy yields alongside actual real-time system

measurements.

The dashboard that was built for this project is in reality a

proof-of concept. It exists as an oversimplified version of what

SA will need in the near future and what companies like Also

Energy, Deck Monitoring, and Draker Energy are able to

provide: a smart dashboard that displays important system

parameters (output power; daily, monthly, and annual energy

yields; expected power output, estimated energy yields),

performance metrics (PI and PR) and info graphics. Each of

these companies will also furnish a publically-available kiosk

view of interesting system information at the request of the

customer. A professional dashboard will also help facilitate

system maintenance by reminding the owner of maintenance

visits and sending alerts when performance anomalies are

detected. The information that was gathered as part of SA’s

solar inventory will be used in the near future to generate a

Request for Proposal (RFP) which will solicit a quote for

services from each of the above companies. Perhaps one of the

largest challenges SA faces in implementing a site-wide

dashboard is the proximity of a communications network to the

solar arrays. The “electrical spine”, if and when it is built, will

solve this problem. Nevertheless, SA will need to overcome

this and many other roadblocks as they strive towards their

goal.

FUTURE WORK A number of opportunities exist for students or

professionals to make meaningful contributions to SA through

future solar projects. Certainly, the body of work described

herein could be further enhanced and refined to give

maintenance staff additional diagnostic and troubleshooting

capabilities. Or additional work could be performed to increase

the number of arrays that are being monitored or to provide SA

a central monitoring interface. Another potential project could

be to develop a curriculum which teaches children some

fundamental math and science skills by using the Dining Hall

array and data collected from the CR10X data logger.

Moreover, SA’s Solar Committee sees an opportunity for

students to get involved in planning large-scale solar

installations for the property, or evaluating on-site solar water

heaters to give SA an idea of the economic benefit each system

provides, or developing a hands-on demonstration of PV solar

for children and site visitors. These three types of projects are

among the Solar Committee’s top priorities, and the successful

accomplishment of each one will move SA closer to realizing

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their vision of a much more green and sustainable campus.

Without a doubt, Sunshine Acres provides solar students the

chance and challenge to grow professionally, and the

experience is rewarding.

ACKNOWLEDGMENTS The author would like to here acknowledge the

contribution of several key people and organizations that made

this project possible. First, to the staff of Sunshine Acres

Children’s Home, Kevin Humphrey, Jon Markwell, Larry Paap,

and Milt Laflen, thank you for all your help. Thank you, Joel

Dickinson of Salt River Project and Devarajan Srinivasan of

Via Sol Energy for your generous donation of material and

expertise. Also much thanks goes to Gilbert Palomino, John

Balfour, and Jim Hansen of Arizona Public Service for their

advising and expertise.

REFERENCES [1] Personal conference with Jim Hansen of APS

[2] Whitaker, C.M., et al., 1997, “Application and Validation of

a New PV Performance Characterization Method,” Proceedings

of the 26th

IEEE PVSC, pp. 1253-1256.

[3] Kimber, A., et al., 2009, “Improved Test Method to Verify

the Power Rating of a Photovoltaic (PV) Project,” Proceedings

of the 34th

IEEE PVSC, pp. 316-321.

[4] ASTM E2848-11e1, “Standard Test Method for Reporting

Photovoltaic Non-Concentrator System Performance.”

[5] King, D.L., Boyson, W.E., Kratochvil, J.A., 2004,

“Photovoltaic Array Performance Model,” SAND2004-3535,

Sandia National Laboratories, Albuquerque, NM.

[6] IEC 61724, “Photovoltaic System Performance Monitoring

– Guidelines for Measurement, Data Exchange, and Analysis.”

[7] Newmiller, J., et al., 1995, “PVUSA Instrumentation and

Data Analysis Techniques for Photovoltaic Systems,” Report

Number 95-30910000.3, Pacific Gas & Electric, San Ramon,

CA.

[8] Endbar, N., Key, T., 2010, “Addressing Solar Photovoltaic

Operations and Maintenance Challenges,” 1021496, EPRI, Palo

Alto, CA.

[9] Banke, B., 2009, “Solar Electric Facility O&M: Now

Comes the Hard Part,” RenewableEnergyWorld.com.

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ANNEX A

MONITORING DISPLAY SCREENSHOTS

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14

15

16

17

18

19

20

21

22

23

24

ANNEX B

MAINTENANCE CHECKLIST

25

26

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ANNEX C

LIST OF PROJECT FILES

1. SA Regression Analysis REV1 – Excel Spreadsheet

2. SA Solar O&M Spreadsheet – Excel Spreadsheet

3. Dining Hall Data – Performance Reports – Excel Spreadsheet