NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Deployment of High Resolution 

Real-Time Distribution Level 

Metering on Maui

Preprint

Jason Bank

To be presented at the 46th Hawaii International Conference on 

System Sciences (HICSS 2013)

Grand Wailea, Hawaii

January 7-10, 2013

Conference Paper

NREL/CP-5500-56417

January 2013
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1 

Deployment of High-Resolution Real-Time Distribution Level Metering on 

Maui

Jason Bank

National Renewable Energy Laboratory

jason.bank@nrel.gov

Abstract

In order to support the ongoing Maui Smart Grid 

demonstration project advanced metering has been 

deployed at the distribution transformer level in Maui 

Electric Company’s Kihei Circuit on the Island of 

Maui. This equipment has been custom designed to 

provide accurately time-stamped Phasor and Power 

Quality data in real time. Additionally, irradiance 

sensors have been deployed at a few selected 

locations in proximity to photovoltaic (PV) 

installations. The received data is being used for 

validation of existing system models and for impact 

studies of future system hardware. Descriptions of 

the hardware and its installation, and some 

preliminary metering results are presented. Real-time 

circuit visualization applications for the data are also 

under development. 

1. Introduction

The ongoing Okinawa-Hawaii Smart Grid 

Demonstration project is preparing to install several 

different types of smart grid equipment into the 

distribution system in the Kihei area of Maui. This 

equipment includes advanced Distribution 

Management Systems, electric vehicles and chargers, 

and battery storage technology along with upcoming 

distributed PV installations. The goal of this project 

is to demonstrate all of these systems in concert on a 

real distribution network, and how they can improve 

efficiency and operation through advanced renewable 

integration and energy management and control 

systems. 

To support these efforts the National Renewable 

Energy Laboratory (NREL) has been deploying 

advanced metering into the area in preparation of the 

upcoming base lining and experimental data 

collection needs. This includes advance real-time 

distribution level metering which has been developed 

at NREL. The distribution metering is focused at the 

low voltage points on the system and provides highresolution power quality and Phasor data in real time 

through an active Internet connection. Additionally 

NREL is collecting solar irradiance data to assess the 

available resource and the impact on PV generation 

in the Kihei area.

The measurement data is archived on servers 

housed at NREL and will be used in several different 

aspects of the project. Initially, they will provide 

model validation input as the circuit is simulated

which will also tie in with system impact studies of 

the upcoming smart grid equipment. In the later 

phases of the project, the collected data will be used 

in an experimental fashion to assess the system 

impacts and benefits of the equipment after it has 

been installed and operational.

This paper presents a brief technical overview of 

the installed metering equipment and some 

preliminary data collection results.

2. Distribution Transformer Monitoring

The transformer monitoring for this project is 

provided by NREL’s real-time distribution 

monitoring network. This network is made up of 

remote metering devices which communicate to a set 

of data collection servers. The metering devices are

built from off-the-shelf hardware with customdeveloped software to handle data collection, 

processing, and communications. They implement 

phasor calculations similar to phasor measurement 

units [1] and additionally include power quality 

calculations. Hereafter the distribution transformer 

meters are referred to as Distribution Monitoring 

Units (DMU).

2.1. DMU Overview

Design of the DMUs has been specifically 

targeted for installation at remote and disparate points 

within a distribution feeder. Ease of installation was a 

major requirement so that the DMU could be 

installed at any location—ranging from the service 

panel of a single home up to the distribution 

transformers and medium voltage locations 

(assuming proper potential transformers were added).
2 

Voltage sensing of this application is done 

directly using clip on voltage probes. The current 

sensing is provided by Rogowski Coils. These probes 

allowed for quick installation in a wide range of 

locations. 

Two different models of DMU have been 

deployed on Maui. The first unit is designed for 

installation at 120/240 volt (V) split-phase locations 

and outputs the Phasor and root mean square

measurements for the three voltages and currents 

along with all of the associated power measurements 

and temperature measurements.

The other installed model is designed for 

installation at three-phase locations, both 120 Y and 

277 Y locations, and outputs the individual phase 

voltages, currents, apparent, and real and reactive 

powers along with temperature measurements.

Installation locations for these metering units 

were selected based on importance to model 

validation and proximity to PV generation. This 

range of potential installations also dictated the 

design be self-contained and able to withstand all 

weather conditions and temperature extremes. 

Each unit transmits its data points in real time 

through an Internet connection. This connection can 

be in the form of a Wi-Fi or wired Ethernet 

connection if available. Additionally, each meter can 

be outfitted with a cellular modem. This modem 

provides an Internet connection through the 3G 

network of a cellular provider. The DMUs deployed 

into the Kihei circuit are equipped with this cellular 

modem and transmit data at one point per second. 

2.2. Data Collection Network

One of the major reasons for designing this 

metering device from the ground up is to allow for 

the design of a data collection network. In this 

architecture, each DMU streams data back to a 

central location in real time or near real time. As data 

is received by the collection server, it is made 

available to a variety of visualization and analysis 

applications. The received data is also stored locally 

at the receiving end. 

The basic system architecture is given in Fig. 1. 

Each DMU collects data from its location and 

transmits the data back to the data concentrator 

located at NREL. From there, it is distributed to 

various visualization applications and data services 

both internal and external to NREL.

Fig. 1: Simplified data collection network architecture

The server side Data collection and storage 

architecture is handled by the OpenPDC software 

package [2]. This is an open-source software package 

designed for Phasor data collection and storage, 

initially developed by the Tennessee Valley 

Authority and is now maintained by Grid Protection 

Alliance.

This application parallels many of the desired 

requirements of the data concentrator system needed 

for this project. The individual DMUs are designed to 

communicate using the Institute of Electrical and 

Electronics Engineers (IEEE) Std C37.118 for 

synchrophasors [3], which is one of the built-in 

protocols of OpenPDC. Thus, OpenPDC is capable of 

communication with the metering devices without 

any modification. This software package is used on 

both the Data Concentrator and Historian servers 

located at NREL. The data flow architecture of this 

protocol and software package also supports the 

desired real-time data transmission for the 

visualization applications.

3. Irradiance Measurements

In addition to the above electrical metering, 

NREL has also deployed a set of five irradiance 

sensors, located near current and future PV 

installations, in order to assess the available solar 

resource in the area. These units are part of the 

Measurement and Instrumentation Data Center 

(MIDC) and collect one-second global horizontal 

irradiance measurements and relay them back to a 

separate set of servers in one-hour batches. More 

information on MIDC and publically available data 

from other projects are available at 

www.nrel.gov/midc. 

4. Metering Installations 

The metering equipment described above was 

installed during April 2012 with the help of MECO 

engineers and linemen. Three split-phase DMUs were 
3 

installed on the secondary side of residential 

distribution transformers. Two of the three-phase

DMUs were installed on the secondary side of 

commercial transformers, one of which was a 277V 

Y location and the other was a 120V Y secondary. 

The Irradiance sensors were also deployed at this 

time at sites near existing and planned PV 

installations in order to investigate the resource 

variability, generation impacts, and effects of the PV 

on the distribution system.

In Fig. 2 the installation on the 120V Y secondary 

transformer is shown. Here the white DMU box is in 

the lower left of the access panel. The voltage and 

current probes can be seen connected to the 

transformer z-bars. Additionally, a combined cellular 

and Global Positioning System, GPS, antenna has 

been mounted on the top of the transformer. The GPS 

provides both location and accurate timing 

information. Data is output through the onboard 

cellular modem and antenna. 

Fig. 2: Metering installation on a three-phase transformer 

Photo by Jason Bank, NREL

One of the installed LiPod irradiance sensors is 

shown in Fig. 3. This unit is located in the Kihei 

substation on Maui. This unit is mounted on a tripod 

and holds a Licor irradiance sensor at the very top of 

the assembly. Inside the data pack at the top of the 

tripod is a data logger with a cellular modem and 

GPS receiver. The data logger records one-second

global horizontal irradiance measurements and then 

outputs them to the MIDC data servers at one hour 

intervals over the cellular connection. The solar panel 

shown here provides power to the electronics in the 

data pack.

Fig. 3: Irradiance sensor installed in Kihei substation 

Photo by Jason Bank, NREL

5. Preliminary Measurement Results

Data has been collected and archived dating back 

to the equipment installation dates. This project is 

still in the data collection phases and in-depth 

analysis has not yet begun. What follows are some 

interesting examples of the type of data that has been 

collected.

Several of the metered transformers serve 

commercial and residential customers with installed 

rooftop PV systems. Measurements taken from one 

of these transformers are given in Fig. 4. The top 

trace in red is the global horizontal irradiance 

measurement take from a nearby sensor. It was a 

relatively clear day, especially in the morning with a 

clear ramp up to midday. In the afternoon, a few

clouds passed through as evidenced by the variability 

in the irradiance. The power flow through the 

transformer is given in the blue trace on the bottom 

of the plot. Here, a positive value indicates power 

flow from the utility to the customers and negative 

flow is from the customers to the utility. During 

midday when the irradiance is highest the flow 

toward the utility through this transformer reaches 

about 15 kW.
4 

Fig. 4: Measured irradiance and transformer power flow, 

April 25th 2012

Review of Fig. 4 shows that the power flow is 

highly variable—this is probably not a result of the 

PV production as the available solar resource is 

smooth, especially in the morning. In this case, the 

variability in power is more likely a result of the 

demand. Regularly sized steps in power are also 

occurring which would indicate the switching of a 

load.

With these installations, several points across the 

distribution system are metered. This allows a more 

comprehensive view of the entire circuit. Fig. 5

demonstrates some of the voltage corrective actions 

on this circuit. The three different secondary voltages 

from the residential transformers are plotted over a 

time span of two hours. Voltage corrective action on 

the circuit can easily be observed here as step 

changes in the voltage. The red and black traces 

shown are on the same feeder as they see the same 

switching event. The transformer represented by the 

blue trace, however, is on a different feeder and being 

regulated by a different device as evidenced by the 

switching event at a different time. The high 

correlations of the red and black traces reinforce this. 

Observation also demonstrates the feeder voltage 

profile and that the transformer represented by the 

black trace is further from the substation than the red 

trace.

Fig. 5: Voltage measurements on three transformers, April 

25th 2012

6. Load Characteristics 

As qualitatively observed in the daily power 

output in Fig. 4, the load introduced more variability 

than the PV generation on the selected day. This was 

due to the switching of one or more loads. These 

events are more clearly demonstrated in the time 

frame of Fig. 6. Here, the black trace is the real 

power flow through the transformer and the red trace 

is the reactive power. This data set was recorded 

from 1:30 p.m. to 3:30 p.m. when the PV system was 

producing and the power flow was negative (toward 

the utility). Several times, the load switches on and 

then off about ten minutes later. The steps in real 

power also correspond to steps in reactive power. 

This would indicate a load or multiple similar loads 

of 4 kW and 2 kVAR, switching on and off at regular 

intervals.

Fig. 6: Load switching events 

The load demonstrated by Fig. 6 has been 

observed on multiple transformers and occurs for 

several hours in a row, particularly in the afternoon 

and evening. Given the size, power factor, time of 

day, and switching nature it is mostly likely an air 

conditioning load. The frequency at which it switches 

can be explained by multiple similarly sized air 

conditioners on several buildings but connected to the 

same distribution transformer, all operating in the 

same time frame.

6.1. Assessment of Load Variability

Accurate load modeling is a major influence on 

validating the circuit models and for assessing the 

expected impacts of upcoming distribution 

management devices and control schemes. Mitigating 

the impacts of PV variability also plays into the 

objectives of this project. As shown in Fig. 4 and Fig. 

6 the load is also a source of variability on short time 

scales (on the order of minutes), especially down at 

the transformer level monitored here.
5 

To quantify the short-term load variability and 

nature of its distribution, the data must first be 

detrended and the median absolute deviation across a 

data set will be computed. The detrending algorithm 

is demonstrated in Fig. 7. A moving median filter is 

passed through the measured signal in order to filter 

out the short-term variability leaving the longer-term 

trend. Here three different median filters were 

implemented, one with a 1-minute window to capture 

the shorter term trend, another with a 15-minute 

window, and finally one with a 1-hour long window

to track the slower load responses. 

In Fig. 7 the raw data is plotted in blue, the 1-

minute median filter is in red, the 15-minute filter is 

in black, and the 1-hour filter is in green. During this 

time span, a few of the load switching events 

discussed earlier are present along with several 

shorter spikes. Here it is observed that the 1-minute

window tracks the switching event closely while 

removing the shorter term spikes. This occurs 

because the 1-minute window is significantly shorter 

than the period which the load is on. The longer 15-

minute window follows the more general trend and 

tracks through the center of the load events, 

effectively considering the load switching as part of 

the variability. The 1-hour window is flatter, tracking

even fewer load characteristics. These windows will 

be used as the basis for the variability assessments, 

with the short window capturing the shorter term 

response and the longer windows showing slower 

effects.

Fig. 7: Effects of median filtering on power measurements 

The load variability of a data set will be assessed 

using the above trending methods in conjunction with 

the median absolute deviation (MAD). The MAD is a 

measure of the variance of a data set similar to the 

standard deviation but since it employs the median 

rather than the mean it is more robust in the face of 

outliers and noisy input data. The MAD is computed 

here as follows:

( )

( ) 













 − = median X

X median X MAD median i

It is the median value of the difference between a 

sample and the data set median normalized by the 

data set median. In this case the data set median will 

be given by the trend as computed by the moving 

median filters of Fig. 7. The inner term of the MAD 

is analogous to the residual error between the sample 

value and the trend. The subtraction operation in this 

equation is accomplishing the detrending operation.

When the 1-minute window filter is used as the 

reference in the MAD calculation the result gives the 

load variability over a 1-minute time scale. If the 15-

minute or 1-hour window is used instead, the 

variability will be based on that timescale. These 

calculations result in three measures of load 

variability, one based on short term load changes and 

two others based on longer term effects. In the case 

of the observed load effects of Fig. 6 the 1-minute

window will treat the load switches as part of the 

trend while the 15-minute and 1-hour windows will 

treat them as part of the load variability.

The preceding detrending procedure and 

variability measure was computed over a week of 

real power measurement data in late April for all five 

of the instrumented transformers. Variability metrics 

were computed for the 1-minute, 15-minute and 1-

hour filters. The results are summarized in Table 1. 

Table 1: Load variability metric at instrumented transformers 

Transformer 1 Min. 15 Min. 1 Hour

Res 1 0.017 0.137 0.203

Res 2 0.031 0.194 0.242

Res 3 0.037 0.204 0.264

120 Y 0.025 0.146 0.159

277 Y 0.012 0.068 0.068

In Table 1 Res 1, Res 2, and Res 3 are the three 

instrumented residential transformers (split-phase

120/240 V secondary); the two three-phase

transformers are named by their secondary voltage 

configuration. As expected, all of the 15-minute and 

1-hour based metrics are higher because the median 

detrending is less sensitive to the load measurements 

and more of the changes in power are treated as 

excursions. Additionally, the 15-minute and 1-hour 

intervals produce metrics of similar size and both are 

generally 5 to 10 times larger than those of the 1-

minute window. This indicates that most of the load 

switching and variability is on the order of a few 

minutes (longer than 1 minute but less than 15). 

When using this method to assess load variability it is 

important to consider the intended application and 
6 

what rate of change in load is of concern, and then 

tune the variability metric to that value. 

In general, the three-phase transformers are 

demonstrating less variability. On all time intervals 

their metrics are demonstrating lower values that the 

residential transformers. This is primarily due to a 

more constant load shape at those locations.

7. Impacts of PV Variability

The same metrics used to assess the load 

variability were also applied to the irradiance 

measurements over the same time span. All of the 

instrumented transformers include sites with PV 

production, thus the power measurement contains a 

dependence on the solar resource. This dependence 

may not be a direct correlation, however, due to 

several effects, most notably the distance between the 

irradiance measurement and the PV system.

Applying the discussed variability metric to the 

three active irradiance measurements over the same 

week’s worth of data gives the results of Table 2. 

Table 2: Variability metric at irradiance stations

Irrad. Sens. 1 Min. 15 Min. 1 Hour

1 0.002 0.074 0.240

2 0.001 0.049 0.161

3 0.001 0.035 0.149

The results in Table 2 indicate that the solar 

resource variability for this data set is encountered 

mostly on longer time scales. The 1-minute window

produces very low metric values while the 1-hour

window is orders of magnitude higher. These results 

imply that the resource variability is observed 

primarily on time scales in excess of 15 minutes. 

Comparing Table 1 to Table 2, it is observed that 

the solar resource variability for this data set is 

significantly lower than that of the load on the 1-

minute and 15-minute time scales. The variability

metrics based on hourly windows fall into a similar 

range though. This would indicate that on 1- and 15-

minute intervals, the observed variability in power 

flow is primarily due to load effects and had less 

dependence on the PV generation. On the hourly 

basis the variability of the solar resource is on par 

with that of the transformer power flow and may 

have significant impacts. 

8. Conclusions

In order to support an ongoing smart grid 

demonstration project on the Island of Maui, NREL 

has deployed several pieces of metering equipment to 

collect data at the distribution transformer level and 

solar resource data. These are all one-second data sets 

and provide valuable information on the state of the 

distribution circuit. As this project develops, the data 

will be used to assess the advantages and drawbacks 

of smart grid equipment that will be installed in the 

future.

To date, five transformer meters and five 

Irradiance sensors have been installed, and data is 

being collected and archived. The data analysis stage 

has not yet begun in earnest with only a few 

preliminary assessments presented here. Some 

interesting features of the collected data sets have 

been presented including a case in which power was 

flowing toward the utility through a distribution 

transformer due the presence of PV and light loading 

conditions. Operation of voltage control devices 

within the distribution circuit can also be observed as 

step changes in voltage by this metering.

As the modeling and simulation portion of this 

project begins, more accurate load models are 

becoming important. To achieve this, some 

preliminary assessment of load variability has been 

done based on robust variance statistics. This metric 

can be tuned to look at different time scales, and 

assess the time dependence of certain load 

characteristics including how particular switched 

loads behave. One type of loads has been identified 

that switched on and off at a rate of about ten 

minutes. The developed variability metric 

demonstrates this time dependence through a series

of progressively larger time windows used as a base.

The same metric was also applied to solar 

irradiance measurements to estimate the expected 

variability in PV generation sources and how PV 

generation relates to the observed load variability.

9. References 

[1] A. G. Phadke, and J. S. Thorpe, “Synchronized 

Phasor Measurements and Their Applications,” 

Springer Science and Business Media LLC, 

NewYork, 2010, pp. 5–103.

[2] Grid Protection Alliance, “OpenPDC – Open 

Source Phasor Data Concentrator Software 

Package,” http://openpdc.codeplex.com, 2011.

[3] "IEEE Standard for Synchrophasors for Power 

Systems," IEEE Std C37.118-2005 (Revision of 

IEEE Std 1344-1995), vol., no., pp.0_1-57, 2006