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Conference Paper

NREL/CP-5K00-76022

June 2020

Overcoming Communications Outages 

in Inverter Downtime Analysis

Preprint

Kevin Anderson1 and Ryan Blumenthal2

1 National Renewable Energy Laboratory

2 Cypress Creek Renewables

Presented at the 47th IEEE Photovoltaic Specialists Conference (PVSC 47) 

June 15 - August 21, 2020
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Conference Paper

NREL/CP-5K00-76022

June 2020

Overcoming Communications Outages 

in Inverter Downtime Analysis

Preprint

Kevin Anderson1 and Ryan Blumenthal2

1 National Renewable Energy Laboratory

2 Cypress Creek Renewables

Suggested Citation

Anderson, Kevin and Ryan Blumenthal. 2020. Overcoming Communications Outages in 

Inverter Downtime Analysis: Preprint. Golden, CO: National Renewable Energy 

Laboratory. NREL/CP-5K00-76022. https://www.nrel.gov/docs/fy20osti/76022.pdf
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NREL 46526.

NREL prints on paper that contains recycled content. 
Overcoming Communications Outages in Inverter 

Downtime Analysis

Kevin Anderson

National Renewable Energy Laboratory

Golden, USA

kevin.anderson@nrel.gov

Ryan Blumenthal

Cypress Creek Renewables 

Santa Monica, USA

blumenthal@ccrenew.com

Abstract— This paper presents two methods of detecting 

inverter downtime and estimating lost production from downtime 

events using timeseries system production measurements. The 

methods focus on distinguishing communications interruptions

from true production outages and are successful in most cases. To 

enable fleet-scale analysis of inverter availability, the methods are 

designed to be semi-autonomous. The first method uses only 

inverter and system meter AC power measurements while the 

second uses cumulative production and expected energy data to 

classify outages and estimate lost production. The methods are 

demonstrated using real field data and the results are discussed.

Keywords— solar power, photovoltaic inverter, system reliability, 

inverter availability, downtime, communication, production loss

I. INTRODUCTION

Inverter downtime is a major source of PV system 

production loss. Inverters have been reported as the most 

common point of failure in PV systems [1], [2], with some fleetwide analyses reporting inverter-related issues as the cause of 

over one third of total lost production [3]. It is common for 

utility-scale financial models to assume a flat 1% production 

loss from system downtime to account for scheduled downtime 

for preventative system maintenance with the expectation that 

appropriate maintenance routines prevent material unexpected 

downtime. However, individual case studies of fielded systems 

report inverter downtime loss in excess of the 1% rule of thumb 

[4]. Such individual case studies provide valuable insight and 

demonstrate the need for further study. Analyzing availability at 

fleet scale offers a way to reduce uncertainty in system financial 

projections but is complicated by communication outages that 

may or may not be associated with actual production outages. A 

scalable method capable of distinguishing the two without 

manual intervention and estimating the associated production 

loss would enable such fleet-level analysis. Such a method 

would also be of use to system owners and operators for ongoing 

asset performance monitoring to inform decision making in 

operations and maintenance (O&M).

Several components in a typical PV system are capable of 

reporting their status in real time. Especially in utility-scale

systems, inverters often report a variety of values like AC and 

DC electrical measurements as well as internal status and faults. 

The inverters report these data streams to a datalogger that may 

also monitor readings from a system revenue-grade meter,

meteorological instrumentation, array monitoring equipment, 

and other such data sources. The datalogger combines the 

various data streams into one coherent dataset and, depending 

on the system configuration, may report the data in real-time to 

a cloud monitoring platform or data historian for O&M use. The 

methods we describe in this paper are intended to be applied to 

this dataset. Other strategies such as manual monitoring by 

O&M teams may be effective for individual systems but are 

costly to apply at fleet scale and are not considered here.

The most basic method of detecting inverter downtime is to 

compare the inverters’ self-reported AC power measurements 

with zero. In situations where the inverter power data stream is 

serially complete with no missing values, this simple method is 

effective and easy to implement. However, it is not robust to data 

gaps caused by communication interruptions between the 

inverter and the datalogger or the datalogger and the data 

historian. Communication outages can be caused by such issues, 

fiber optic line damage, power supply faults, cellular network 

interference, and electrical faults, among others. They can affect 

individual pieces of equipment (e.g. a “partial communication 

outage” affecting a single inverter) or a full outage that affects 

the entire system. Communication with an inverter can also drop 

when the inverter trips offline, and moreover, some data 

acquisition systems fill in missing data values with zero when 

communications are lost, expanding the problem scope from just 

missing data to untrustworthy data as well. 

Because of the wide variety of possible causes and resulting 

data features, no one rule of thumb about equipment state during 

a communications outage will be accurate across different 

system configurations. To classify inverter status more reliably 

in the face of communications outages, this paper presents two 

methods of making use of other data streams to infer inverter 

status. The first (described in detail in Section II) compares

system meter AC power and inverter-level AC power with the 

assumption that meter power will reliably reflect the aggregated 

power of all producing inverters. For instance, meter power will 

read higher than the aggregated inverter powers in situations 

where an inverter is producing but not communicating, but not 

when the inverter is truly offline. The second method (described 

in Section III) addresses cases where comparisons to other 

devices is not possible (for instance during full communications 

outages) by comparing an expected production value for the 

outage to the actual production as inferred from cumulative 

production data integrated by the measurement device itself. 

The methods first locate all outages in the dataset (whether they 

be production, communication, or both) and then estimate the 

production loss associated with each outage, if any. Section IV

shows that the two methods are complementary in that they 

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handle different cases of communications outages but can be 

used independently as well. 

II. THE “POWER COMPARISON” METHOD

The power comparison method is useful for partial outages 

when the system meter and at least one inverter are still 

communicating production. The first stage examines individual 

inverter data streams and the system meter power to detect if any 

inverters are offline at each timestamp. The second builds on the 

first and focuses on the system in aggregate to estimate

timeseries production loss at the system level. 

A. Inverter Status

At a high level, inverter status is classified by comparing the 

system meter power to a hypothetical “no downtime” value 

estimated from any inverters still communicating production. If 

the two values are sufficiently close, any inverters not 

communicating production are presumed to be online and just 

suffering a communications outage. However, if the difference

is large enough to be plausibly caused by an offline inverter, the 

outage is classified as a production outage.

Note that we use the phrase “communicating production” to 

refer to power measurements that are clearly associated with 

normal operation. In many cases it is sufficient to simply check 

that the power value is not missing and is greater than zero. 

However, the quirks of some data collection systems can create

small nonzero power readings when the inverter is not actually 

producing. Although data quality checks are largely out of scope 

here, we guard against this possibility by comparing power 

values with a small nonzero threshold instead of zero. To 

accommodate inverters of different sizes, the threshold is 

determined empirically from the inverter’s power 

measurements:

PPtthrrrrrrhoooooo = PP99

1000 (1)

Where PP99 is the 99th percentile of the inverter’s AC power 

readings. The 99th percentile is preferred over the maximum 

value to guard against spurious nonphysical readings sometimes 

seen in field measurements. This threshold value is used to 

identify readings where the inverter is not confidently known to 

be producing – if the power value is missing or below the 

threshold power, the inverter is considered potentially offline. 

This check is later referred to as the “threshold test”.

To estimate the hypothetical system “full power” value, a 

virtual “mean inverter” is constructed. The mean inverter’s 

timeseries power is intended to equal the mean of all the 

inverters’ power readings if they were all producing. Because 

inverter production can vary between inverters, this is a distinct 

concept from just the mean of the reporting inverters. The raw 

mean of reporting inverters can be biased from the virtual mean 

in cases where the inverters are differently-sized and some 

inverters are not producing or non-communicative. To remove 

this bias, the relative capacity of each inverter is calculated by 

filtering for points that pass the threshold test, dividing by the 

raw mean inverter power, and taking the median of this 

normalized power for each inverter over the entire filtered 

dataset. This gives a ratio for each inverter that relates its 

production capacity against the system mean. In cases where 

downtime is common enough for the median to not reflect 

normal production, known relative sizing can be used instead. 

Then, the mean inverter power is calculated using only 

producing and communicating inverters but dividing each 

inverter’s power data by its inferred relative capacity before 

taking the mean across inverters. Fig. 1 shows how this mean 

inverter approach remains consistent for outages with 

differently-sized inverters. 

Fig. 1. An artificial example of three differently-sized inverters showing how 

correcting for the different inverter capacities keeps the virtual mean inverter 

consistent across outages. The uncorrected mean inverter is biased when an 

inverter goes offline.

The virtual mean inverter is used to estimate total system 

power as if all inverters were online and producing. Because the 

virtual inverter provides an outage-independent estimate of the 

mean inverter power for each timestamp, simply multiplying it 

by the number of inverters gives an estimate for system-level 

production at full capacity. When all inverters are producing 

(even if some are not reporting), this estimate should line up with 

meter power. If there is a large discrepancy between meter 

power and the estimated full power, it is likely that one or more 

inverters is truly offline. This capacity discrepancy is defined as:

CC dd = 1 − CCooooooii (2)

Where the estimated online fractional capacity is given by

CCoooooo oo = PP ss ss

PPffff ff ff

(3)

Here PP ss ss refers to the true power produced (system 

meter power) and PPffff ff ff refers to the power that would have 

been produced if all inverters were online.

To determine whether or not the capacity discrepancy

between actual meter power and the estimated full power is large 

enough to indicate an offline inverter, the discrepancy is 

compared with the expected discrepancy if the smallest inverter 

that is not communicating production is actually offline, that is:

DD = min

ii

CCii (4)5

Here CCii is the iith inverter’s fraction of system capacity and 

ii is each inverter the does not pass the threshold test. The CCii

values can be calculated from the relative capacities RRCCjj from 

earlier if needed via CCii = RRCCii/ ∑ RRCCjjjj . 

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If the difference between virtual system power and meter 

power is larger than the smallest possible difference, it is likely 

that at least one inverter is offline. However, to accommodate 

some noise between the meter and inverter power readings, we 

compare with a reduced minimum discrepancy:

CC dd > 0.75 DD (6)

When this criterion is true, at least one inverter is assumed 

to be offline and the method proceeds to the next stage of 

calculating lost production.

Note that determining which inverters are offline is not 

always possible – take the case where one inverter is nonproducing and another is just non-communicative. The virtual 

power vs meter power comparison will tell you an inverter is 

offline, but it gives no clues as to which. Although it may be 

useful in other contexts to know precisely which inverters are 

offline, knowing that one or more inverters are offline is 

sufficient here. 

B. Production Loss

The second stage of the method estimates timeseries 

production loss at the system level due to inverter production 

outages by determining the online and producing fraction of 

system capacity and scaling meter production accordingly. 

However, again to accommodate noise between the meter and 

inverter power readings, we impose a lower limit on the online 

capacity fraction so it is no less than the sum of the capacities of 

reporting inverters:

CCoooooo oo , = max[CCoooooo oo , ∑ CCiiii ] (7) 

Where again ii refers to all inverters measurements that pass 

the threshold test. With this estimate of the producing fraction 

of the system’s capacity, the lost power PP is given by: 

PP = 1 − CCoooooo oo ,

CCoooooo oo ,

PP ss ss (8)

A final optional step is to constrain the estimated power loss 

to be consistent with the system’s maximum power output PP mm. 

In this case: 

PP∗ = � PP , PP ss ss + PP ≤ PP mm

PP mm − PP ss ss, PP ss ss + PP > PP mm

(9)

Because this method depends on scaling the meter data by 

online capacity, it requires at least part of the system to be 

producing so that meter power is nonzero. Power loss when the 

entire system is offline must be calculated some other way since 

the meter data cannot act as a reference. 

III. THE “EXPECTED ENERGY” METHOD

In cases where no power data is available (for instance, full 

communications outages), the inverter-to-meter comparison 

strategy described in Section II cannot be used. Here we present 

an alternate method that uses cumulative production data before 

and after an outage to infer production during the outage. This 

actual production is compared with an expected production 

value to determine if the outage involved any downtime. To do

this, a performance model like PVwatts, SAM or PVSyst is used 

to calculate the expected energy. During a site wide 

communications outage, the expected production is calculated 

using satellite weather data or nearby weather station 

measurements if they're available.

When the communications return, the actual production is 

compared with an uncertainty interval around the expected 

production for a given outage. If the uncertainty interval 

contains the actual production, the true production is consistent 

with the expected production and the outage is classified as 

communications only. If the actual production falls short of the 

uncertainty interval, the difference is assumed to be lost 

production.

The uncertainty associated with even simple energy 

estimates is difficult to analyze because of the large number of 

underlying sources of uncertainty. Rather than undertake an indepth uncertainty analysis that would depend on the PV model, 

the system, and other unknown factors, it is easier to quantify 

the uncertainty empirically by comparing the expected 

production model against the system’s actual output when the 

system is at full capacity. 

A. Expected Production Uncertainty

Because this uncertainty is intended to reflect the error 

between expected production and actual production under 

normal operating conditions, this analysis should only consider 

periods where the system is performing normally. Other sources 

of underperformance like array soiling and degradation are out 

of scope here but should be considered when applying this 

method. For the purposes of characterizing the uncertainty of 

expected energy, it is assumed that any such periods have been 

filtered from the dataset (perhaps by using the inverter status 

inference method in Section II.A).

To focus the uncertainty analysis on just the scatter of the 

expected energy model, we remove its bias by rescaling it to 

match the actual production over the entire dataset of interest 

(again ignoring times when the system is not necessarily 

performing as expected). This is done with a simple rescale: 

PP ,rrrrrrrr rr = �

EE ,ff

EE ,ff

� PP (10)

Where PP ,rrrrrrrr rr is the rescaled expected power, PP is 

the original expected power, and EE ,ff and EE ,ff are

the total expected and actual energies (filtered as previously 

mentioned). This ensures that the uncertainty in the expected 

power model approaches zero as the length of the period of 

interest increases. Note that if the expected energy model is 

accurate, the ratio EE ,ff /EE ,ff should be close to 1. 

Because the relative uncertainty of an expected energy 

model should decrease as the considered time period grows, we 

quantify the error between actual system production and 

expected production by characterizing the error distribution

across timescales. We choose a timeseries length (1 day, 1 

week, etc) and sweep that window across the dataset, calculating 

the error (as a fraction of expected energy) for each window and 

generating a population and distribution of errors for that 

window length. By generating that distribution for many 

different window lengths, it is possible to look up the uncertainty 

in expected energy for a given outage length. Note that, because 

our goal is to predict uncertainty based on outage length and the 

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portion of an outage at night will not help with predicting 

uncertainty, we consider “window length” to refer to only points 

where expected energy is greater than zero. This convention is 

used both in generating the distributions as well as when using 

them to predict uncertainty for a given outage length (i.e. the 

outage length is defined by the number of daylight intervals, not 

the true duration of the outage). 

Fig. 2 shows how the error distribution changes as timescale

increases. The window lengths chosen for this example are 

somewhat arbitrary; so long as the range of lengths covers the 

maximum outage length of interest, the exact window lengths 

chosen for characterization are not very important. We choose 

a logarithmic spacing for convenience. The 1st and 99th

percentiles of each distribution are chosen as the uncertainty 

bounds for that window length and used as interpolation points 

for a given outage length. For example, using the distributions 

shown in Fig. 2, an outage 80 intervals long would have bounds 

given by interpolating between the 1st and 99th percentiles of the 

54 and 114-interval distributions.

Fig. 2. Example error distributions of error between expected production and 

actual production for various window lengths. The box edges show the 25th and 

75th percentiles and the whiskers extend a distance 1.5 * IQR beyond the box 

edges where IQR = (75th percentile – 25th percentile). Values outside this range 

are plotted as circles. The error distribution tightens with increasing window 

length. Only daylight intervals (where expected energy is greater than zero) are 

considered.

Note that for simplicity, we assume that the uncertainty 

distributions are stationary over time and are not affected by 

seasonality, system age, or other time factors. For systems 

where this assumption does not apply, multiple sets of 

distributions could be calculated for each time period where they 

are relatively stationary. For example, a system might have a 

separate set of distributions for each season of the year and the 

set of distributions used for a given outage would depend on the 

time of year the outage occurs. For simplicity here, we proceed 

using a single set of distributions.

B. Outage Classification and Production Loss

Once the uncertainty for a given outage’s expected 

production is known, the next step is to identify outages. 

Because the cumulative production data stream from an inverter 

or meter should only increase over time, testing for missing data 

or values equal to zero is a good place to start. Depending on 

the specific behavior of the data acquisition system used, other 

strategies may be necessary. Once an outage is identified, the 

expected production and associated uncertainty is calculated as 

described in Section III.A. Lastly, the difference in cumulative 

production values immediately before and after the outage are 

used to calculate the actual system production. If the actual 

production falls within the uncertainty interval, no production 

loss is assigned to the outage. If the actual production falls short, 

then lost production is defined as the difference between actual 

production and the expected production: 

EE = �

EE − EE , EE < EE (1 + σσ )

0, otherwise (11)

Here EE is the estimated lost production during the outage, 

EE is the expected production during the outage, EE is 

the actual production during the outage, and σσ is the lower 

uncertainty interval bound (and typically a negative number).

IV. CASE STUDIES

Here we demonstrate the two methods using 15-minute

timeseries data from utility-scale PV systems. Although the 

methods are intended to be applied to entire lifetime datasets, 

here we show small subsets to illustrate how the methods 

behave. Both examples shown here are cases complicated by 

communication outages. The first example shows a partial 

communication outage where only a few inverters and the 

system meter maintained communication. The second shows a 

full communication outage. 

A. Power Comparison Method

This example is taken from a utility-scale system with 

several dozen string inverters. Fig. 3 compares the aggregated 

inverter power with the system meter power, showing how 

roughly two thirds of the inverters stopped communicating 

power for a day. Manual investigation of this day revealed that 

two of the system’s three dataloggers were offline on that day 

and no true downtime occurred. A naïve power analysis might

incorrectly estimate roughly 1000 kW of loss at midday, despite 

the system performing normally in reality. 

Fig. 3. Example showing how a partial communications outage affecting 

several inverters creates a difference between the summed inverter powers and 

the system meter power. Using only inverter AC power would classify those 

inverters as offline when in reality they are producing normally.

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Applying the power comparison method yields the power 

loss timeseries shown in Fig. 4. Although roughly 1000 kW of 

inverter capacity appears to be offline when looking only at the 

inverter power data, the method estimates only ~35 kW of 

capacity offline. Again, because this specific case is known to 

be a pure communications outage, the correct loss should be zero 

on July 8th. Note that the small spikes of power loss on other 

days are due to an intermittent inverter and are accurate. 

Fig. 4. Lost power calculated by the power comparison method. The small 

power loss spikes are due to an inverter repeatedly cycling on and off. 

Although the power comparison method is much more 

accurate than the naïve method in this case, it still overestimates 

the production loss. The method is assigning lost production 

because one of the power comparison method’s assumptions

isn’t exactly true in reality; the inverter sum and the meter are 

often not precisely aligned when all inverters are operating

normally. The power comparison method assumes that any 

difference between meter power and the virtual full power is 

from offline inverters, so the discrepancy between inverter and 

meter power readings caused by AC-system losses means the 

virtual full power estimate will exceed meter power to some 

extent. Under certain conditions, this discrepancy can cause the 

method to incorrectly classify a communications outage as a 

partial production outage.

Fig. 5 shows the distribution of the ratio of meter power to 

summed inverter power for this system, ignoring times when not 

all inverters are reporting power. 

Fig. 5. A histogram showing the distribution of instantaneous meter power 

divided by summed inverter power, filtering out periods where any inverter is 

not reporting power. The distribution is not centered around unity, meaning 

there is a bias between the meter readings and the summed inverter readings. 

The distribution is centered around 0.988, meaning this 

system’s meter typically reads about 1.2% lower than its

summed inverter readings. The tails of the distribution are likely 

caused in part by a combination of weather variability and nonsimultaneous sampling between inverters and meter. 

Normally this discrepancy is not large enough to be an issue, 

but because this system has so many inverters, that 1.2% is a 

significant fraction of the contribution of a single inverter,

biasing the virtual full power comparison and creating an 

inverter’s worth of downtime where it doesn’t really exist. In 

cases like this, it may be beneficial to try to account for this 

effect before comparing with the meter readings, for example by 

scaling the estimated full power value by the median ratio of this 

distribution. Note that the value 1.2% is specific to this system’s 

AC topology and may not be accurate for other system 

configurations. 

B. Expected Energy Method

Here we apply the expected energy method to a 17-day data 

outage in a system meter data stream. The uncertainty 

distributions shown in Fig. 2 were calculated for this meter with 

expected energy estimated from the National Renewable Energy 

Laboratory’s PSM3 satellite weather model [5] and the PVWatts 

PV performance model [6]. Fig. 6 shows how the outage’s 

expected energy and uncertainty interval compare with the 

reported cumulative meter value after the outage. Because the 

actual production value falls outside the uncertainty interval, the 

method assigns production loss to the outage (roughly 50% of 

the expected energy in this case).

Fig. 6. Example outage classification using the expected energy method. The 

blue line shows the cumulative production available in the dataset and contains 

a ~17 day outage. Because the reported meter data shows less production 

during the outage than is necessary to be consistent with the uncertainty interval 

around expected energy, the difference between actual production and expected 

production is classified as a production loss.

Manual investigation revealed that some (but not all) 

inverters did trip offline for several days during this 

communications outage. This example demonstrates the value 

of the uncertainty analysis as well as a weather-adjusted 

expected energy over a more naïve estimate. Because this 

outage is a mixed outage, it would be difficult to reject the

possibility that the production shortfall was just a consequence 

of poor solar resource during the outage period without the rigor 

of the uncertainty interval. However, because this is a 

probabilistic method, it is not guaranteed to detect all production 

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loss. For instance, if the outage shown in Fig. 6 had only ~5% 

production loss instead of ~50%, the actual production would 

move inside the expected energy’s uncertainty interval and the 

outage would be incorrectly classified as a pure communications 

outage. The method’s ability to detect small amounts of 

production loss in a large outage is therefore limited by the 

accuracy of the expected energy model; a poor expected energy 

model will have wider uncertainty bounds and the method will 

have less ability to resolve production loss in mixed outages.

Note that Fig. 6 shows the expected meter and uncertainty 

interval calculated progressively over the outage duration for 

illustrative purposes only. Estimating lost production only 

requires calculation of the final values shown as solid circles at 

the end of the outage.

V. CONCLUSIONS

Several studies have shown that inverter downtime is a 

major source of PV system production loss. Unfortunately, 

detecting and quantifying this loss from timeseries power data 

is complicated by the presence of communication outages. We 

present two independent methods of classifying data outages 

and estimating the associated production loss. The first uses

power data from peer inverters and a system meter to infer 

inverter status and estimate lost production. The second uses

cumulative production measurements before and after the 

outage and an expected energy model to estimate lost 

production. In a real implementation, the power comparison 

method could be used for partial outages to classify each 

timestamp and estimate associated production loss, while the 

second would be used for full outages to estimate loss for the 

outage as a whole. The methods are straightforward to automate 

and designed to be scalable to fleet-scale analysis. The methods 

are demonstrated using outages in datasets from real utilityscale PV systems. 

VI. ACKNOWLEDGEMENT

This work was authored in part by Alliance for Sustainable 

Energy, LLC, the manager and operator of the National 

Renewable Energy Laboratory for the U.S. Department of 

Energy (DOE) under Contract No. DE-AC36-08GO28308. 

Funding provided by the U.S. Department of Energy's Office of 

Energy Efficiency and Renewable Energy (EERE) under Solar 

Energy Technologies Office (SETO) Agreement Numbers 

34348. The views expressed in the article do not necessarily 

represent the views of the DOE or the U.S. Government. The 

U.S. Government retains and the publisher, by accepting the 

article for publication, acknowledges that the U.S. Government 

retains a nonexclusive, paid-up, irrevocable, worldwide license 

to publish or reproduce the published form of this work, or allow 

others to do so, for U.S. Government purposes.

The authors would also like to thank NREL’s corporate 

partners for providing the field system data used as case studies 

in this paper, as well as Robert White for setting up the database 

infrastructure to store it.

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