Interpretation and analysis of power quality measurements lo max

1
Interpretation and Analysis of Power Quality Measurements
Christopher J. Melhorn
Electrotek Concepts, Inc.
Knoxville, Tennessee
Mark F. McGranaghan
Electrotek Concepts, Inc.
Knoxville, Tennessee
ABSTRACT
This paper describes advances in power quality
monitoring equipment and software tools for analyzing
power quality measurement results. Power quality
monitoring has advanced from strictly problem solving
to ongoing monitoring of system performance. The
increased amount of data being collected requires more
advanced analysis tools. Types of power quality
variations are described and the methods of
characterizing each type with measurements are
presented. Finally, methods for summarizing the
information and presenting it in useful report formats
are described.
INTRODUCTION
Power quality has become an important concern for
utility, facility, and consulting engineers in recent years.
End use equipment is more sensitive to disturbances that
arise both on the supplying power system and within the
customer facilities. Also, this equipment is more
interconnected in networks and industrial processes so
that the impacts of a problem with any piece of equipment
are much more severe.
The increased concern for power quality has resulted
in significant advances in monitoring equipment that can
be used to characterize disturbances and power quality
variations. This paper discusses the types of information
that can be obtained from different kinds of monitoring
equipment and methods for analyzing and presenting the
information in a useful form.
Important objectives for the paper include the
following:
• Describe important types of power quality
variations.
• Identify categories of monitoring equipment
that can be used to measure power quality
variations.
• Offer examples of different methods for
presenting the results of power quality
measurements.
• Describe tools for analyzing and presenting
the power quality measurement results.
Analysis tools for processing measurement data will
be described. These tools can present the information as
individual events (disturbance waveforms), trends, or
statistical summaries. By comparing events with libraries
of typical power quality variation characteristics and
correlating with system events (e.g. capacitor switching),
causes of the variations can be determined. In the same
manner, the measured data should be correlated with
impacts to help characterize the sensitivity of end use
equipment to power quality variations. This will help
identify equipment that requires power conditioning and
provide specifications for the protection that can be
developed based on the power quality variation
characteristics.
CATEGORIES OF POWER QUALITY
VARIATIONS
It is important to first understand the kinds of power
quality variations that can cause problems with sensitive
loads. Categories for these variations must be developed
with a consistent set of definitions so that measurement
equipment can be designed in a consistent manner and so
that information can be shared between different groups
performing measurements and evaluations. An IEEE
Working Group has been developing a consistent set of
definitions that can be used for coordination of
measurements.[1]
Power quality variations fall into two basic categories:
1. Disturbances. Disturbances are measured by
triggering on an abnormality in the voltage or
the current. Transient voltages may be
detected when the peak magnitude exceeds a
specified threshold. RMS voltage variations
(e.g. sags or interruptions) may be detected

2
when the RMS variation exceeds a specified
level.
2. Steady State Variations. These include
normal RMS voltage variations and harmonic
distortion. These variations must be measured
by sampling the voltage and/or current over
time. The information is best presented as a
trend of the quantity (e.g. voltage distortion)
over time and then analyzed using statistical
methods (e.g. average distortion level, 95%
probability of not being exceeded, etc.).
In the past, measurement equipment has been
designed to handle either the disturbances (e.g.
disturbance analyzers) or steady state variations (e.g.
voltage recorders, harmonics monitors). With advances in
processing capability, new instruments have become
available that can characterize the full range of power
quality variations. The new challenge involves
characterizing all the data in a convenient form so that it
can be used to help identify and solve problems.
Table 1 summarizes the different categories and lists
possible causes and power conditioning equipment
solutions for each category.
Steady State Voltage Characteristics
There is no such thing as steady state on the power
system. Loads are continually changing and the power
system is continually adjusting to these changes. All of
these changes and adjustments result in voltage
variations that are referred to as long duration voltage
variations. These can be undervoltages or overvoltages,
depending on the specific circuit conditions.
Characteristics of the steady state voltage are best
expressed with long duration profiles and statistics.
Important characteristics include the voltage magnitude
and unbalance. Harmonic distortion is also a
characteristic of the steady state voltage but this
characteristic is treated separately because it does not
involve variations in the fundamental frequency
component of the voltage.
Most end use equipment is not very sensitive to
these voltage variations, as long as they are within
reasonable limits. ANSI C84.1 [7] specifies the steady
Table 1. Summary of Power Quality Variation Categories
Power Quality
Variation Category
Method of
Characterizing Typical Causes
Example
Power Conditioning
Solutions
Impulsive Transients
Peak magnitude,
Rise time,
Duration
Lightning,
Electro-Static Discharge,
Load Switching
Surge Arresters,
Filters,
Isolation Transformers
Oscillatory Transients
Waveforms,
Peak Magnitude,
Frequency Components
Line/Cable Switching,
Capacitor Switching,
Load Switching
Surge Arresters,
Filters,
Isolation Transformers
Sags/Swells
RMS vs. time,
Magnitude, Duration Remote System Faults
Ferroresonant Transformers,
Energy Storage Technologies*,
UPS
Interruptions Duration
System Protection
(Breakers, Fuses),
Maintenance
Energy Storage Technologies*,
UPS,
Backup Generators
Undervoltages/
Overvoltages
RMS vs. Time,
Statistics
Motor Starting,
Load Variations
Voltage Regulators,
Ferroresonant Transformers
Harmonic Distortion
Harmonic Spectrum,
Total Harm. Distortion,
Statistics
Nonlinear Loads,
System Resonance
Filters (active or passive),
Transformers (cancellation or
zero sequence components)
Voltage Flicker
Variation Magnitude,
Frequency of Occurence,
Modulation Frequency
Intermittent Loads,
Motor Starting,
Arc Furnaces Static Var Systems
* Note: Energy Storage Technologies refers to a variety of alternative
energy storage technologies that can be used for standby supply
as part of power conditioning
(e.g. superconducting magnetic energy storage, capacitors, flywheels, batteries)

3
state voltage tolerances for both magnitudes and
unbalance expected on a power system. Long duration
variations are considered to be present when the limits are
exceeded for greater than 1 minute.
Figure 1. Example 24 hour voltage profile illustrating
long duration voltage variations.
Harmonic Distortion
Harmonic distortion of the voltage and current results
from the operation of nonlinear loads and devices on the
power system. The nonlinear loads that cause harmonics
can often be represented as current sources of harmonics.
The system voltage appears stiff to individual loads and
the loads draw distorted current waveforms. Table 2
illustrates some example current waveforms for different
types of nonlinear loads. The weighting factors indicated
in the table are being proposed in the Guide for Applying
Harmonic Limits on the Power System (Draft 2)[2] for
preliminary evaluation of harmonic producing loads in a
facility.
Harmonic voltage distortion results from the
interaction of these harmonic currents with the system
impedance. The harmonic standard, IEEE 519-1992 [2],
has proposed two way responsibility for controlling
harmonic levels on the power system.
1. End users must limit the harmonic currents
injected onto the power system.
2. The power supplier will control the harmonic
voltage distortion by making sure system
resonant conditions do not cause excessive
magnification of the harmonic levels.
Harmonic distortion levels can be characterized by the
complete harmonic spectrum with magnitudes and phase
angles of each individual harmonic component. It is also
common to use a single quantity, the Total Harmonic
Distortion, as a measure of the magnitude of harmonic
distortion. For currents, the distortion values must be
referred to a constant base (e.g. the rated load current or
demand current) rather than the fundamental component.
This provides a constant reference while the fundamental
can vary over a wide range.
Table 2. Example current waveforms for various
nonlinear loads.
Type of Load Typical Waveform
Current
Distortion
Weighting
Factor (Wi)
Single Phase
Power Supply
0 10 20 30 40
-1.0
-0.5
0.0
0.5
1.0
C
80%
(high 3rd)
2.5
Semiconverter
0 10 20 30 40
-1.0
-0.5
0.0
0.5
1.0
C
high 2nd,3rd,
4th at partial
loads
2.5
6 Pulse Converter,
capacitive smoothing,
no series inductance
0 10 20 30 40
-1.0
-0.5
0.0
0.5
1.0
C
80% 2.0
6 Pulse Converter,
capacitive smoothing
with series inductance > 3%,
or dc drive
0 10 20 30 40
-1.0
-0.5
0.0
0.5
1.0
C
40% 1.0
6 Pulse Converter
with large inductor
for current smoothing
0 10 20 30 40-1.0
-0.5
0.0
0.5
1.0
C
28% 0.8
12 Pulse Converter
0 10 20 30 40-1.0
-0.5
0.0
0.5
1.0
C
15% 0.5
ac Voltage
Regulator
0 10 20 30 40-1.0
-0.5
0.0
0.5
1.0
C
varies with
firing angle 0.7
Harmonic distortion is a characteristic of the steady
state voltage and current. It is not a disturbance.
Therefore, characterizing harmonic distortion levels is
accomplished with profiles of the harmonic distortion over
time (e.g. 24 hours) and statistics. Figure 2 illustrates a
typical profile of harmonic voltage distortion on a feeder
circuit over a one month period.

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Time Trend for Voltage THD
05/26 06/02 06/09 06/16 06/23 06/30 07/07
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Time
THD(%)
Time Trend for Voltage THD
05/26 06/02 06/09 06/16 06/23 06/30 07/07
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Time
Figure 2. Example Profile of Harmonic Voltage
Distortion on a Distribution Feeder Circuit.
Transients
The term transients is normally used to refer to fast
changes in the system voltage or current. Transients are
disturbances, rather than steady state variations such as
harmonic distortion or voltage unbalance. Disturbances
can be measured by triggering on the abnormality
involved. For transients, this could be the peak
magnitude, the rate of rise, or just the change in the
waveform from one cycle to the next. Transients can be
divided into two sub-categories, impulsive transients and
oscillatory transients, depending on the characteristics.
Transients are normally characterized by the actual
waveform, although summary descriptors can also be
developed (peak magnitude, primary frequency, rate-of-
rise, etc.). Figure 3 gives a capacitor switching transient
waveform. This is one of the most important transients
that is initiated on the utility supply system and can affect
the operation of end user equipment.
0 20 40 60 80 100
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Time (mS)
Vlt(V)
34.5 kV Bus Voltage
Capacitor Switching Transient
Figure 3. Capacitor Switching Transient.
Transient problems are solved by controlling the
transient at the source, changing the characteristics of the
system affecting the transient or by protecting equipment
so that it is not impacted. For instance, capacitor
switching transients can be controlled at the source by
closing the breaker contacts close to a voltage zero
crossing. Magnification of the transient can be avoided
by not using low voltage capacitors within the end user
facilities. The actual equipment can be protected with
filters or surge arresters.
Short Duration Voltage Variations
Short duration voltage variations include variations in
the fundamental frequency voltage that last less than one
minute. These variations are best characterized by plots
of the RMS voltage vs. time but it is often sufficient to
describe them by a voltage magnitude and a duration that
the voltage is outside of specified thresholds. It is usually
not necessary to have detailed waveform plots since the
RMS voltage magnitude is of primary interest.
The voltage variations can be a momentary low
voltage (voltage sag), high voltage (voltage swell), or loss
of voltage (interruption). Interruptions are the most
severe in terms of their impacts on end users but voltage
sags can be more important because they may occur much
more frequently. A fault condition can cause a momentary
voltage sag over a wide portion of the system even
though no end users may experience an interruption. This
is true for most transmission faults. Figure 4 is an example
of this kind of event. Many end users have equipment
that may be sensitive to these kinds of variations. Solving
this problem on the utility system may be very expensive
so manufacturers are developing ride through
technologies with energy storage to handle these voltage
variations on the end user side.
Figure 4. Voltage Sag Caused by a Remote Fault
Condition.

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TYPES OF EQUIPMENT FOR
MONITORING POWER QUALITY
Multimeters or DMMs
After initial tests of wiring integrity, it may also be
necessary to make quick checks of the voltage and/or
current levels within a facility. Overloading of circuits,
under- and over-voltage problems, and unbalances
between circuits can be detected in this manner. These
measurements just require a simple multimeter. Signals to
check include:
• phase-to-ground voltages
• phase-to-neutral voltages
• neutral-to-ground voltages
• phase-to-phase voltages (three phase system)
• phase currents
• neutral currents
The most important factor to consider when selecting
and using a multimeter is the method of calculation used in
the meter. All of the commonly used meters are calibrated
to give an RMS indication for the measured signal.
However, a number of different methods are used to
calculate the RMS value. The three most common
methods are:
1. Peak Method. The meter reads the peak of the
signal and divides the result by 1.414 (square
root of 2) to obtain the RMS.
2. Averaging Method. The meter determines the
average value of a rectified signal. For a clean
sinusoidal signal, this average value is related
to the RMS value by the constant, k=1.1. This
value k is used to scale all waveforms
measured.
3. True RMS. The RMS value of a signal is a
measure of the heating which will result if the
voltage is impressed across a resistive load.
One method of detecting the true RMS value is
to actually use a thermal detector to measure a
heating value. More modern digital meters use
a digital calculation of the RMS value by
squaring the signal on a sample by sample
basis, averaging over a period, and then taking
the square root of the result.
These different methods all give the same result for a
clean, sinusoidal signal but can give significantly different
answers for distorted signals. This is very important
because significant distortion levels are quite common,
especially for the phase and neutral currents within the
facility. Table 3 can be used to better illustrate this point.
Each waveform in Table 3 has an RMS value of 1.0 pu
(100.0%). The corresponding measured value for each
type of meter is displayed under the associated
waveforms, per-unitized to the 1.0 pu RMS value.
Oscilloscopes
An oscilloscope is valuable when performing real time
tests. Looking at the voltage and current waveforms can
tell a lot about what is going on, even without performing
detailed harmonic analysis on the waveforms. You can get
the magnitudes of the voltages and currents, look for
obvious distortion, and detect any major variations in the
signals.
Table 3. Methods for Measuring Voltages and Currents with Multi-Meters.
Meter Type Circuit Sine Wave Square Wave Distorted
Wave
Light
Dimmer
Triangle Wave
-1.5
-1
-0.5
0
0.5
1
1.5
-1.5
-1
-0.5
0
0.5
1
1.5
-3
-2
-1
0
1
2
3
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Peak
Method
Peak / 1.414 100 % 82 % 184 % 113 % 121 %
Average
Responding
Sine Avg. x 1.1 100 % 110 % 60 % 84 % 96 %
True RMS RMS Converter 100 % 100 % 100 % 100 % 100 %

6
There are numerous makes and models of
oscilloscopes to choose from. A digital oscilloscope with
data storage is valuable because the waveform can be
saved and analyzed. Oscilloscopes in this category often
have waveform analysis capability (energy calculation,
spectrum analysis) also. In addition, the digital
oscilloscopes can usually be obtained with
communications so that waveform data can be uploaded
to a PC for additional analysis with a software package.
Disturbance Analyzers
Disturbance analyzers and disturbance monitors form
a category of instruments which have been developed
specifically for power quality measurements. They
typically can measure a wide variety of system
disturbances from very short duration transient voltages
to long duration outages or under-voltages. Thresholds
can be set and the instruments left unattended to record
disturbances over a period of time. The information is
most commonly recorded on a paper tape but many
devices have attachments so that it can be recorded on
disk as well.
There are basically two categories of these devices:
1. Conventional analyzers that summarize events
with specific information such as over/under
voltage magnitudes, sags/surge magnitude
and duration, transient magnitude and
duration, etc.
2. Graphics-Based analyzers that save and print
the actual waveform along with the descriptive
information which would be generated by one
of the conventional analyzers.
It is often difficult to determine the characteristics of a
disturbance or a transient from the summary information
available from conventional disturbance analyzers. For
instance, an oscillatory transient cannot be effectively
described by a peak and a duration. Therefore, it is almost
imperative to have the waveform capture capability in a
disturbance analyzer for detailed analysis of a power
quality problem (Figure 5). However, a simple
conventional disturbance monitor can be valuable for
initial checks at a problem location.
Figure 5. Graphics Based Analyzer Output
Spectrum Analyzers and Harmonic Analyzers
Many instruments and on line monitoring equipment
now include the capability to sample waveforms and
perform FFT calculations. The capabilities of these
instruments vary widely and the user must be careful that
the accuracy and information obtained is adequate for the
investigation. The following are some basic requirements
for harmonic measurements used to investigate a problem:
• Capability to measure both voltage and current
simultaneously so that harmonic power flow
information can be obtained.
• Capability to measure both magnitude and
phase angle of individual harmonic
components (also needed for power flow
calculations).
• Synchronization and a high enough sampling
rate for accurate measurement of harmonic
components up to at least the 37th harmonic
(this requirement is a combination of a high
sampling rate and a sampling interval based on
the 60 Hz fundamental).
• Capability to characterize the statistical nature
of harmonic distortion levels (harmonics levels
change with changing load conditions and
changing system conditions).
Harmonic distortion is a continuous phenomena. It
can be characterized at a point in time by the frequency
spectrums of the voltages and currents. However, for
proper representation, measurements over a period of time
must be made and the statistical characteristics of the
harmonic components and the total distortion determined.

7
Combination Disturbance and Harmonic
Analyzers
The most recent instruments combine limited
harmonic sampling and energy monitoring functions with
complete disturbance monitoring functions as well (Figure
6). The output is graphically based and the data is
remotely gathered over phone lines into a central
database. Statistical analysis can then be performed on
the data. The data is also available for input and
manipulation into other programs such as spreadsheets
and other graphical output processors.
ANALYZING POWER QUALITY
MEASUREMENT DATA
Analyzing power quality measurements has become
increasingly more sophisticated within the past few years.
It is not enough to simply look at RMS quantities of the
voltage and current. Disturbances that occur on the
power system have durations in the milli-second time
frame, equipment is more sensitive to these disturbances,
and there is more equipment connected to the power
systems that cause disturbances or power quality
problems. For these reasons, it is often necessary to
continuously monitor system performance and
characterize possible impacts of disturbances.
The data analysis system must be flexible enough to
handle data from a variety of monitoring equipment and
maintain a database that can be used by many different
applications. The concept is illustrated in Figure 7.
Event
Information
PQ Data
CHARACTERIZER
Database Manager
Site
Information
Event
Viewer
Protection
Analysis
Data
Trending
Energy
Analysis
Statistical
Analysis
Applications
PQ Monitoring
Database
Power
Quality
Monitoring
Instruments
Voltage
Recorders
InPlant
Monitors
Digital Fault
Recorders
Data
Translator
PQ Data
Interchange
Format
Data
Translator
Data
Translator
Data
Translator
Figure 7. Example Data Analysis System.
Different types of power quality variations require
different types of analysis to characterize system
performance. Some examples are given in the following
sections. With a flexible system, these applications can be
customized to individual user needs.
Transients
Transients are normally characterized by the actual
waveform, although summary descriptors can also be
developed for:
• peak magnitude
April 22, 1992 at 21:43:21 LocalIBM503E
Phase C-A Voltage
RMS Variation
Trigger
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
80
85
90
95
100
105
110
115
Time (Seconds)
Voltage(%)
0 25 50 75 100 125 150 175
-150
-100
-50
0
50
100
150
Time (mSeconds)
Voltage(%
Duration
0.150 Sec
Min
81.38
Ave
96.77
Max
101.4
BMI/Electrotek
November 19, 1991 at 06:07:56 PQNode LocalRGE_BETA
Phase A Voltage
Wave Fault
Trigger
0 10 20 30 40 50 60 70
-1.5
-1
-0.5
0
0.5
1
1.5
Time (mSeconds)
Voltage(pu)
Max1.094
Min-1.280
Uncalibrated Data
May 09, 1992 at 13:04:07 LocalIBM503M
Phase A Current
SS Wave
0 10 20 30 40 50
-600
-400
-200
0
200
400
600
Time (mSeconds)
Current(Amps)
0 10 20 30 40 50
0
20
40
60
80
100
Harmonic
Amps
Fund267.5
RMS 281.0
CF 1.772
Min -495.6
Max 498.0
THD 30.67
HRMS86.19
TIF/IT70249
Figure 6. Output From Combination Disturbance and Harmonic Analyzer.

8
• primary frequency
• time of occurrence
• rate of rise
An example of this data in statistical form is presented
in Figure 8.
Transient Magnitude
0.0 0.5 1.0 1.5 2.0
0
50
100
150
200
250
0
20
40
60
80
100
Magnitude (per-unit)
Ct
CumulativeProba
Samples:
Min:
Max:
Range:
Mean:
Std Dev:
Variance:
Std Err:
Mode:
Skewness:
Kurtosis:
561
1.05014
1.59
0.53986
1.14418
0.0825617
0.00681643
0.00348576
1.11834
1.73749
4.45834
Figure 8. Bar Chart for Transient Peak Voltage.
RMS Variations
RMS variations are generally characterized by the
RMS value vs. time or by the minimum magnitude of the
voltage during the event vs. the duration of the event.
Figure 1 was an illustration of a plot of magnitude vs. Time
for a 24 hour period.
This method is fine for looking at single sites and
single events. But when a whole system is involved,
either customer or utility, it may be preferable to look at a
range of events (e.g. one month, one year, etc.) for
multiple sites. This would give an indication as to what
type of RMS events are occurring on a given system. The
magnitude duration plot in Figure 9 illustrates the minimum
voltage (in percent) during the event and the duration of
the event (time in cycles that voltage was out of the
thresholds).
RMS Magnitude vs Duration
10
-2
10
-1
10
0
10
1
10
2
10
3
10
4
0
25
50
75
100
125
150
Duration (60 Hz Cycles)
VltMitd
Total Events:
Events Below:
Events Above:
Below CBEMA:
Above CBEMA:
149
141
8
26
0
Figure 9. Example Magnitude Duration Plot.
Another method for displaying this type of data is a
three dimensional bar graph where the count, magnitude,
and duration is shown. Figure 10 illustrates this type of
plot.
1-10
10-60
60-
180
180-
300
300-
3000
>3000
Duration(Cycles)
0
20
40
60
80
100
120
140
160
Magnitude(%)0
100
200
300
400
500
600
700
800
Count
RMS Variation Magnitude vs Duration vs Count
Figure 10. Three Dimensional RMS Variation Bar Graph.
Harmonics
Harmonics are characterized by individual snapshots
of voltage and current with the associated spectrums. It is
important to understand that the harmonic distortion
levels are always changing and these characteristics
cannot be represented with a single snapshot. Therefore,
time trends and statistics are needed. An example time
trend plot for one month was included in Figure 2. Figure
11 shows the statistics of the harmonic current level. This
would be good for comparison with IEEE-519 limits.
Histogram for Harmonic RMS Current
0 5 10 15 20 25
0
250
500
750
1000
1250
1500
0
20
40
60
80
100
Current (A)
Ct
CumulativeProba
Samples:
Min:
Max:
Range:
Mean:
Std Dev:
Variance:
Std Err:
Mode:
Skewness:
Kurtosis:
6691
4.93836
20.9626
16.0242
6.95771
1.9269
3.71296
0.0235567
6.06852
1.53821
1.2563
Figure 11. Histogram for Harmonic RMS Current for
Approximately Four Months.
SUMMARY
Systematic procedures for evaluating power quality
concerns can be developed but they must include all
levels of the system, from the transmission system to the
end user facilities. Power quality problems show up as
impacts within the end user facility but may involve
interaction between all levels of the system.

9
A consistent set of definitions for different types of
power quality variations is the starting point for
developing evaluation procedures. The definitions permit
standardized measurements and evaluations across
different systems.
A data analysis system for power quality
measurements should be able to process data from a
variety of instruments and support a range of applications
for processing data. With continuous power quality
monitoring, it is very important to be able to summarize
variations with time trends and statistics, in addition to
characterizing individual events.
BIOGRAPHIES
Christopher J. Melhorn received an ASE from York
College of Pennsylvania in 1986 and a BSEET from the
Pennsylvania State University in 1989. Chris has been
employed with Electrotek Concepts, Inc. since 1990. His
experience at Electrotek includes working with EPRI and
utilities on case studies involving power quality issues.
He was also extensively involved in the EPRI DPQ project
site selection phase. Chris is presently involved in
developing new software for the power systems
engineering environment and working to increase
Electrotek's industrial based clientele.
Mark F. McGranaghan received a BSEE and an MSEE
from the University of Toledo and an MBA from the
University of Pittsburgh. Mark serves as Manager of
Power Systems Engineering at Electrotek Concepts, Inc.,
Mark is responsible for a wide range of studies, seminars,
and products involving the analysis of power quality
concerns. He has worked with electric utilities and end
users throughout the country performing case studies to
characterize power quality problems and solutions as part
of an extensive Electric Power Research Institute (EPRI)
project. He has also been involved in the EPRI
Distribution Power Quality Monitoring Project which is
establishing the baseline power quality characteristics of
U.S. distribution systems through a multi-year monitoring
effort. Mark was involved in the design and specification
of the instrumentation and software for this project.
REFERENCES
1. IEEE Working Group P1159, Recommended
Practice for Monitoring Electric Power Quality -
Draft 7, December, 1994.
2. IEEE Std. 519-1992, IEEE Recommended Practices
and Requirements for Harmonic Control in
Electrical Power Systems, IEEE, New York, 1993.
3. “Electrical Power System Compatibility with
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