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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 19, NO. 2, APRIL 2004
TransientMeter: A Distributed Measurement System
for Power Quality Monitoring
P. Daponte, Senior Member, IEEE, M. Di Penta, Associate Member, IEEE, and G. Mercurio
Abstract—This paper describes the design and the implementation of TransientMeter, a monitoring system for the detection,
classification, and measurement of transient disturbances on electrical power systems. TransientMeter relies on a common object
request broker architecture (CORBA) as a communication interface, wavelet-based methods for automatic signal classification and
characterization, and on a smart trigger circuit for disturbance detection. The system has been successfully applied to detect, classify,
and measure disturbances in an industrial environment.
Index Terms—Distributed information systems, monitoring,
neural networks, power quality, power systems, power system
transients, wavelet transforms.
I. INTRODUCTION
I
N the recent years, users of electric power have detected an
increasing number of drawbacks caused by electrical power
quality variations. These variations already existed on electrical
systems, but only recently they are causing serious problems.
As a matter of fact: 1) devices used in modern electrical installations are more susceptible to disturbances with respect to the
oldest ones, because they contain control systems provided with
microprocessors that can suffer a large scale of disturbances;
and 2) the growing use of power electronics implies more precautions to limit harmonic distortion.
To improve power quality with adequate solutions, it is
necessary to know what kinds of disturbances occurred. A
measurement system able to automatically detect, characterize,
and classify disturbances on electrical lines is therefore required. This brings up advantages for both end users and utility
companies [1]–[3].
The main advantages for end users are: 1) risk avoidance, a
monitoring system can detect disturbances that can cause damages to user equipment; 2) manpower efficiency, in that an automatic monitoring system eliminates time losses due to analyzing
large signal records and preparing reports; and 3) process improvements, in that a monitoring system allows to identify the
most sensitive equipments and, therefore, install power conditioning systems only where necessary.
The main advantages for utilities are: 1) risk avoidance, a
utility can show customers the effective quality of the power
produced, to prove that the utilities themselves are not responsible for any damages that have occurred on a customer’s
Manuscript received April 11, 2003.
P. Daponte and G. Mercurio are with the University of Sannio, Department
of Engineering, Benevento I-82100, Italy (e-mail: daponte@unisannio.it; mercurio@unisannio.it.).
M. Di Penta is with RCOST, University of Sannio, Benevento I-82100, Italy
(e-mail: dipenta@unisannio.it).
Digital Object Identifier 10.1109/TPWRD.2004.825200
equipment; 2) manpower efficiency, likewise the end user, the
utility can rapidly detect a problem and recognize its causes,
avoiding massive personnel scheduling; 3) capital investment
reductions, a continuous monitoring allows expensive power
system improvements to be limited where strictly necessary;
and 4) competitiveness, an efficient monitoring allows utilities, in a deregulated power market, to stipulate special power
quality contracts and, in general, to offer a better product. Finally, by installing a monitoring system in the customer’s site,
and allowing customer access to the power quality database,
utilities can offer an important service, thus differentiating
themselves from the competition.
The challenge of developing software-based monitoring systems has been discussed in the recent years–analyzing architectures and features required by these systems, as well as the
enabling technologies. The problem of analyzing data from
power quality monitoring systems was discussed in [4]: panelists discussed different aspects such as capturing trends, as
well as the architectures and installation issues of the power
quality monitoring systems. Wagner et al. [5], [6] carried out
an experiment aimed at identifying disturbances causing problems in production environments. Guidelines for implementing
a power quality monitoring system were presented in [7] by
Rauch et al., and in [8] and [9] by Parihar and Liu.
Sawyer [10] discussed the evolution and the future trends
of power monitoring systems, in terms of architecture, installation, and software functionalities. In [11], Makinen et al.
discussed the state of the art of monitoring systems in Finland.
The advantages of a software, object-oriented-based power
quality monitoring system were highlighted in [12] by Qiu
and Wimmer, where all the enabling state-of-the art software
technologies were summarized. Leou et al. [13] presented a
web-based system for monitoring steady-state disturbances
and outages. In particular, the paper focused on the web
technologies to adopt and their advantages.
Some power quality systems, as well as algorithms and
methods for disturbance detection and analysis, have been
proposed in literature during the last few years. A circuit for disturbance detection, similar to that adopted for TRANSIENTMETER,
has been proposed by Shakarijan et al. in [14]. The application
of wavelet transform, in order to decompose the power signal
and to extract the disturbance from the fundamental, was
proposed in [15] and [16] by Gaouda et al. The application
of the wavelet analysis for the detection and measurement of
disturbances in a noisy environment was discussed in [17].
A digital signal processor (DSP)-based system to analyze
steady-state power disturbances was proposed by Lakshmikanth
and Morcos in [18] and [19]. Another software monitoring
0885-8977/04$20.00 © 2004 IEEE
DAPONTE et al.: TRANSIENTMETER: A DISTRIBUTED MEASUREMENT SYSTEM FOR POWER QUALITY MONITORING
system, tested in a pilot project inside an industrial environment
in Brazil, was presented in [20]. The system aimed to measure
duration and frequency of disturbances occurred. Bucci et
al. [21] proposed a distributed, virtual instrument for power
quality monitoring. The system, implemented in LabView™,
aimed to measure steady-state disturbances (sags, swells,
flickering), and also perform some time-domain analysis on
transient disturbances (rise and fall time, amplitude, duration).
Finally, an Internet-based system for detecting and measuring
steady-state disturbances was presented by Waclawiak et al. in
[22].
This paper proposes TransientMeter, a distributed measurement system for the automatic detection, classification, and
measurement of disturbances affecting an electrical power
system. The software design philosophy is oriented to adopt
standard and open technologies. Its components are completely
software implemented, except for a trigger circuit for disturbance detection and a data-acquisition board. A measurement
algorithm developed by using the wavelet transform and the
wavelet networks has been adopted for the automatic classification and measurement of disturbances.
The paper is organized as follows: Section II highlights the
characteristics of the monitoring systems; Section III describes
the TRANSIENTMETER architecture; Section IV reports the
wavelet-based algorithms for the disturbance classification and
characterization; Section V reports numerical and experimental
results.
II. CHARACTERISTICS OF POWER MONITORING SYSTEMS
A. Monitoring Instrument Characteristics
Monitoring instruments today available continuously digitize current and voltage signals, and evaluate root mean square
(rms) values of current and voltage, active, reactive, and apparent power, and harmonic distortion. These measurements are
stored in a large memory inside the instrument.
Some more sophisticated equipment allow a transient event
to be detected: when the voltage level or the current level goes
below (or above) user-defined thresholds (this means that a sag,
a swell, an interruption, or an impulse occurred), then amplitude, duration, and occurrence time are evaluated. At end of
the measurement, all of the collected information is printed in a
report summarizing the events that occurred.
The disadvantage of these instruments is that their operation
strongly depends on the correct calibration of the thresholds.
Furthermore, if a very low threshold is fixed and electrical
lines are particularly affected from problems, an instrument’s
memory rapidly fills up. However, a high threshold could
cause the loss of some information. Therefore, the best way
to proceed is an adaptive threshold that varies according to
disturbance occurrence [23].
Some instruments can also increase their sampling rate whenever a disturbance occurs. Another remarkable characteristic is
the possibility of comparing events with Computer Business
Manufacturers Association (CBEMA) curves, opportunely customized [23].
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Finally, some instruments allow transferring acquired data to
remote computers for post-elaboration and storage. Major disadvantages of existing equipments are:
• cost (due to the use of complex hardware solutions);
• low flexibility (hardware solutions are not very configurable and adaptable to particular operative conditions);
• configuration and operation from remote locations are
usually limited;
• total absence of disturbance classification: where present,
the instrument only distinguishes impulsive events from
rms variations;
• no information about time-frequency signal analysis;
• The need for a large amount of memory to store samples
of disturbances occurred, since, as said, these instruments
cannot automatically classify the event, and therefore,
users must manually examine all of the waveforms.
B. Distributed Monitoring Systems
In most cases, the information produced by power monitoring system has to be used in places physically different from
those where the monitoring instrument has been placed. Here
are some typical situations that may occur: 1) technicians may
have to analyze data acquired in wind farm plants situated
on the top of a mountain; 2) technicians of a utility have
to monitor power quality in a customer’s industrial site, to
verify if problems claimed from customer depend on power
produced, or instead depend on customer’s devices faults; and
3) customers would have access to power quality data, stored
into a utility’s database.
First attempts of automatic data transfer relied on modems
to allow downloading information directly from monitoring
instruments and, in some cases, for controlling them. Now,
with the growing diffusion of computer networks and internetworking, and thanks to middleware for developing distributed
systems common object request broker architecture (CORBA),
distributed component object model (DCOM), remote method
invocation (RMI), it is possible to create monitoring systems
more and more complex.
Monitoring instruments are directly connected to the network, using a modem or a serial line. Data coming from these
instruments flow in a database that users can access via the web.
One or more control workstations are then used to drive and
configure monitoring instruments and to manage the database.
In the following, the main characteristics of all the components
of a distributed monitoring system will be analyzed:
1) Measurement instruments:
In a distributed monitoring system an instrument
should be:
• remotely configurable and controllable;
• able to control other devices [e.g., starting uninterruptible power supply (UPS) when a certain event
occurs];
• able to temporarily retain acquired data, to avoid
loss of information in case of communication breakdown.
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2) Control workstation:
A control workstation should be able to supply the following functions:
• configuration of monitoring instrument parameters;
• start/stop of a remote measurement procedure;
• gather and store data coming from remote monitoring instruments;
• data analysis;
• data export (i.e., paste into the clipboard, export to
spreadsheets and to scientific software).
A good control software should also have
• graphical features (event plots, CBEMA curves,
statistical graphs, trends);
• possibility to generate customized reports;
• possibility of software evolution without expensive
maintenance operations.
3) Database server:
The database management system (DMBS) should
have the following characteristics:
• fast and concurrent access for many users without
critical performance degradation;
• open database connectivity (ODBC) support;
• any unauthorized access must be avoided;
• transaction support.
4) Web-based data-access software:
The peculiarities of the software for Web data access
are:
• restricted access: any user wishing to access data
must login, typing his/her username and password;
afterwards, he/she can only access to data of his/her
own interest;
• advanced query: users may set filters based on various fields, and using any logical operator (AND, OR,
NOT, etc.);
• disturbance plots: users may want to see (and
download) directly from the browser events plots,
CBEMA curves, statistical graphs, temporal trends,
etc.
5) Communication channels and hierarchy:
The selection of the communication channel strongly
depends on monitoring instruments, connectivity functions, and on their physical locations. Some of the possible channels are:
• fixed telephone lines by using a modem;
• mobile communication system by using a Global
System for Mobile Communications (GSM)
modem;
• telephone lines by using dial-tone multifrequency
(DTMF) coding;
• radio communication;
• communication on the power distribution line: a
typical example of this kind of transmission is
the distribution line carrier (DLC) developed by
Cannon Technologies [24];
• use of existing local-area network (LAN) or
wide-area network (WAN): this is undoubtedly the
most versatile way to build a distributed monitoring
system.
• the major advantage is the use of widely diffused
protocols (TCP/IP) and middleware that allow very
flexible and maintainable systems to be realized.
Another very important aspect related to communication
between monitoring instruments and control software is the
possibility of a hierarchic communication system design. For
example, if several monitoring instruments are placed on the
same site, it is not convenient to supply each one with a GSM
modem or with an independent telephone line. So, a computer
could be used to gather all information coming from these
instruments, and to periodically send information to a control
workstation using a common telephone line.
III. ARCHITECTURE OF TransientMeter
A. Overview
Let us now describe the main characteristics of the proposed
monitoring system. Its components (including monitoring instruments) are completely software implemented, except that a
trigger circuit for disturbance detection and a data acquisition
board.
TransientMeter uses pre-existing Internet/intranet as a communication channel, and it is composed of the following components (Fig. 1).
1) Monitoring workstations, on its turn composed by:
• a trigger circuit, able to detect transient events on
the electrical signals;
• a data acquisition board, driven by the trigger circuit, that acquires (using pretriggering mode) all of
the detected disturbances: in the current version, a
National Instruments PCI 6024-E is used;
• a software component, named TRANSIENTMETER
Server (implemented using Borland C++ Builder
4.0), that processes the digitized signals and sends
results to the control workstation.
2) A control software, simply named TRANSIENTMETER (also
implemented using Borland C++ Builder 4.0), used to:
• configure measurement parameters;
• initialize remote monitoring workstations;
• start/stop measurement procedures, in manual or
automatic mode;
• gather data coming from remote monitoring workstations and store them into the database;
• process signals coming from text files or wave files;
• query and manage the database.
3) A database, containing information about measurements
made and events occurred.
4) A web-based software, named TransientMeter Web (implemented with the server-side scripting language PHP),
that allows remote queries to database, shows results and
allows downloads of event samples in text format and
event plots in GIF format.
DAPONTE et al.: TRANSIENTMETER: A DISTRIBUTED MEASUREMENT SYSTEM FOR POWER QUALITY MONITORING
Major advantages of the TransientMeter architecture
are:
• it contemporaneously detects and classifies disturbances;
• disturbances are extracted from electrical signal
fundamental;
• it is possible to select those disturbances the system
has to detect (e.g., only some types of disturbances,
only disturbances having a given amplitude and
frequency);
• it is possible to store into the database only extracted disturbances or only disturbance parameters (without waveform recording, therefore saving
space);
• the system is provided by a function that periodically calculates statistics on the current measurement, removing from the database event details and
storing on it only these statistics–this is important
for saving space in case of long monitoring sessions;
• automatic start/stop of measurements, programming a list of timers;
• distributed architecture able to support monitoring
workstations that dynamically register themselves
on the control workstation to notify their presence;
• advanced query functions present in the control
software and in the web software.
Fig. 1.
Architecture of TRANSIENTMETER.
Fig. 2.
Database entity-relationship model.
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B. Database Architecture
Database entity-relationship model is shown in Fig. 2.
Entities present in the database are the following.
1) Monitoring site: it represents a generic monitoring site.
Attributes are identifying code, description and state (On
or Off);
2) Measurement: it represents a measurement, executed on a
certain site, and composed of a certain number of events.
Attributes are: identifying code, date and time of start and
stop, configuration parameters (sampling rate, monitored
phases and quantities), and number of disturbances detected;
3) Event: it represents a disturbance detected during a measurement. Attributes are phase on which the disturbance
has been detected, quantity measured (voltage or current),
date and time of occurrence, type of event, amplitude,
duration or frequency, samples of disturbances extracted
from fundamental, and added to fundamental (both stored
in blob fields).
Relationships between entities are as follows.
1) Executed On: relates the Monitoring Site entity to Measurement entity, specifying on which monitoring site a
measurement was performed;
2) Detected: relates Measurement entity too the Event
entity, specifying events detected during a particular
measurement.
C. User Interfaces
As shown in Fig. 3, TransientMeter has a Multiple Document
Interface, composed of the following windows:
• a window used to select sites where to start/stop a
measurement;
• a window containing a row for each measurement;
• a window for each active measurement, containing a row
for each event that occurred in that measurement;
• event plot windows;
• statistics windows.
Moreover, the instrument is provided with a panel used to
control the state of the system, to monitor the number of events
that occurred on a certain site, and to manually start/stop the
measurements. When users access to TransientMeter Web, they
can set up filters, submit a query, and then browse results.
Clicking on each row of the query result page, it is possible to
plot events (extracted or added to the fundamental).
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Fig. 3.
IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 19, NO. 2, APRIL 2004
Fig. 4.
CORBA architecture.
Fig. 5.
Measurement algorithm.
TRANSIENTMETER user interface.
D. Communication Interfaces
The interface between control software (TransientMeter)
and monitoring software (TransientMeter Server) has been
built using a CORBA architecture. One of major advantages
deriving from the use of CORBA is the great maintainability
and extendibility of the structure.
As shown in Fig. 4, remote methods are called to configure
monitoring instruments, to start/stop a measurement, and to read
processed data.
The object request broker (ORB) adopted is TAO (The ACE
ORB) built on the component level of adaptive communication environment (ACE), an open-source framework for developing distributed architectures [25], [26]. This full-compliant
ORB is particularly suitable for high-performance and real-time
applications.
IV. MEASUREMENT ALGORITHMS
This section describes the measurement method adopted in
TRANSIENTMETER, obtained by combining and improving the algorithms presented in [27]–[33]. As shown in Fig. 5, the instrument processes the signal coming from a data acquisition board,
driven by a trigger circuit. After the signal acquisition, signal
processing is performed by software modules dealing with: 1)
classifying the disturbance; 2) calculating its duration or its frequency; and 3) extracting the disturbance from fundamental and
calculate its amplitude.
Let us now examine the main characteristics of each module:
1) Trigger Circuit: It compares the original signal with its
low-pass version, obtained by a 70-Hz frequency cut filter. If the
difference goes beyond a certain threshold, a monostable multivibrator generates a TTL pulse that drives the data acquisition
board.
In order to monitor several phases and quantities, many
trigger circuits may be OR-connected: the output of the OR gate
will drive the data acquisition board, and the outputs of single
circuits will be connected to acquisition board’s digital inputs
to indicate on which phase the disturbance has been detected.
2) Classification: This phase is mainly based on wavelet
networks (WNs). Briefly, a WN is a feedforward neural network whose first layer’s activation functions have been replaced
by mother wavelet functions, and the training algorithm (backpropagation), modifies not only neural weights and thresholds,
but also scale and translation parameters of wavelet nodes. This
kind of network can extract time-frequency information (very
useful for transient analysis) from the signal. Besides, a simple
neural network can only extract waveshape information. The
classification block adopted in this paper is able to classify the
following transient disturbances: momentary interruptions, oscillatory transients, and impulsive transients.
3) Module for Disturbance Duration Estimation: It calculates the duration of a signal singularity. Initially, the module
computes the continuous wavelet transform (CWT) of the
signal, and its local maximums. Then, starting from the highest
scale values (scale for CWT is the inverse of frequency), each
CWT maximum is connected with the nearest one at the subsequent scale. Each sequence of maximums is called “chain.”
Afterwards, noise generated chains are pruned, using appropriate thresholds, based on the fact that most noise maximums
have amplitude growing with frequency, while others have a
smaller amplitude than those generated by the singularity. Once
only two chains remain, it is possible, at higher frequencies
(where CWT ensures a good temporal resolution), to calculate
DAPONTE et al.: TRANSIENTMETER: A DISTRIBUTED MEASUREMENT SYSTEM FOR POWER QUALITY MONITORING
distance between chains, corresponding to the disturbance
duration (e.g., in case of an interruption or an impulse), or to
the disturbance frequency (e.g., in case of oscillatory transient).
4) Disturbance Extraction and Amplitude Estimation: This
module decomposes the signal in subbands using discrete time
wavelet transform (DTWT) implemented by a tree of quadrature
mirror filters (QMFs), and then each subband is reconstructed
using another tree of QMF implementing an inverse DTWT
(IDTWT). The 50/60-Hz fundamental sinusoid is located at the
center of the first subband. Selecting the subband containing
the disturbance frequency previously calculated, and adding
this subband to those adjacent, the extracted disturbance is
now reconstructed. After the extraction, it is very easy to
compute several disturbance characteristics like peak-to-peak
amplitude, rise time, etc.
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Fig. 6. Experiment setup.
V. EXPERIMENTAL RESULTS
Initially, TransientMeter was tested by means of simulated
signals. This phase gave the possibility to opportunely set up
the several TransientMeter’s software components.
Successively, experiments were performed by means of signals produced by an arbitrary waveform generator. These tests
were aimed to adopt the better conditions for the trigger and
acquisition phase. Finally, experiments are in progress for the
monitoring of an industrial plant. Two monitoring workstations
are used to monitor the disturbances produced by two motors
(see Fig. 6). The workstations are driven from a remote control
workstation.
The purpose of the experience is to detect disturbances occurring during the motor starting and stopping operations. An
example of disturbance detected during the monitoring phase (a
damped oscillation occurred during motor stopping) is shown
in Fig. 7. The figure reports the digitized signal, the extracted
disturbance, and measurement results of the parameters characterizing the disturbance.
In order to validate the measurement method, a large set of
waveshapes containing all of the possible disturbances, having
different amplitude and duration, was generated and analyzed
with the proposed measurement instrument (the simulated
waveshapes are shown in Table I). Simulations were performed
adding to the signal Gaussian white noise, therefore considering
signals with different signal-to-noise ratio (SNR).
For the measurement and the extraction block, duration estiand amplitude estimate relative error
mate relative error
were computed and reported. It is worth noting that relative errors were computed with respect to the seeded disturbance
actual duration and amplitude. For the classification block, the
average number of successful classifications and its standard deviation were computed. The sampling frequency was fixed to
25.6 kHz and the number of samples for cycle to 1024. For all
of the categories of signal considered, 100 Monte Carlo simulations were performed. Disturbances were generated in different
positions with respect to the fundamental period. The duration
of the transient disturbances was chosen between 100 s and
10 ms.
Table II reports errors for disturbances of duration
ms, and
ms having amplitude 1 Vpp, for
Fig. 7.
Oscillatory transient occurred on motor stopping.
TABLE I
SIMULATED WAVESHAPES
different values of the SNR. Oscillatory transients were generated with a frequency of 800 Hz and 600 Hz, respectively, with
and
.
For the classification block, two kinds of tests were
performed: varying the SNR and varying the disturbance
amplitude. In both cases, the percentage of misclassifications
was computed and reported. In particular, a first statistic
treatment was performed generating 100 signals of different
categories, and repeating the operation for different values of
the SNR. Impulsive and oscillatory transients were generated
with a frequency between 600 and 1200 Hz, while momentary
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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 19, NO. 2, APRIL 2004
TABLE II
RELATIVE ERRORS OCCURRED IN DISTURBANCE DURATION
AMPLITUDE MEASUREMENT
AND
Fig. 8. Percentage of misclassifications of oscillatory transients for different
disturbance durations and frequency.
TABLE III
PERCENTAGE OF MISCLASSIFICATIONS FOR DIFFERENT SNR
TABLE IV
PERCENTAGE OF MISCLASSIFICATIONS OF MOMENTARY INTERRUPTIONS
FOR DIFFERENT DISTURBANCE DURATIONS
interruptions were generated with a duration between 1 and
3 ms. Results are shown in Table III.
A second statistical test was performed producing 100 signals
affected by momentary interruptions in different positions, and
repeating the operation for different durations of the interruption
and
itself. Results are reported in Table IV for both
dB.
A similar test was finally performed, generating 100 signals
affected by impulsive transients and 100 affected by oscillatory transients, determining the number of correct classifications for different values of the disturbance amplitude. Results
were very similar for both oscillatory transients (shown in Fig. 8
dB) and for impulsive transients.
for
VI. CONCLUSION
TransientMeter revealed to be, during all of the experiments
performed, particularly effective in the automatic classification
and characterization of transient disturbances. With an appropriate calibration, the system may be used in any operational
environment.
The use of a CORBA architecture to allow communication
between control workstation and monitoring sites makes the
software easily maintainable and extensible. In particular,
the ACE-TAO platform has revealed itself appropriate for
the scope, since it was specifically designed for real-time
applications. Furthermore, TransientMeter is particularly cheap
(it uses only a simple trigger circuit, a low-cost data acquisition
board, and some medium-level personal computers).
Future improvements of the system are related to:
1) improve the developed measurement method, introducing adaptive thresholds for wavelet neural network
and automatic calibration of thresholds used in the
duration estimation;
2) add modules to detect, classify, and characterize other
kinds of disturbances such as harmonic disturbances,
flickering, etc.;
3) use dedicated hardware [e.g., parallel DSPs for real-time
signal processing];
4) build an expert system able to detect disturbance causes
and propose adequate solutions.
ACKNOWLEDGMENT
The authors are grateful to Edison Energie Speciali S.pA and
I.T.I.S. “Bosco Lucarelli” for the collaboration given during the
experiments.
REFERENCES
[1] Recommended Practice for Monitoring Electric Power Quality—Draft
7, Dec. 1994.
[2] Why Monitoring Power Quality? [Online]. Available: http://www.pqmonitoring.com
[3] D. Sabin and M. McGranaghan, A Systems Approach to Power Quality
Monitoring for Performance Assessment. Arlington, VA: Electrotek
Inc..
[4] E. Liu, “Analysis of monitored power quality data,” in Proc. IEEE Power
Eng. Soc. Summer Meeting, vol. 1, 1999, pp. 320–324.
[5] V. E. Wagner, A. A. Andreshak, and J. P. Staniak, “Power quality and
factory automation,,” in Proc. IEEE Industry Applications Soc. Annu.
Meeting, vol. 2, 1988, pp. 1391–1396.
, “Power quality and factory automation,” IEEE Trans. Ind. Ap[6]
plicat., vol. 26, pp. 620–626, July/Aug. 1990.
[7] G. B. Rauch, D. D. Sabin, M. McGranaghan, and A. Sundaram, “Implementing system-wide power quality monitoring plans,” in Proc. IEEE
Transm. Dist. Conf., 1996, pp. 37–44.
DAPONTE et al.: TRANSIENTMETER: A DISTRIBUTED MEASUREMENT SYSTEM FOR POWER QUALITY MONITORING
[8] P. Parihar and E. Liu, “Identification, classification and correlation of
monitored power quality events,” in Proc. IEEE Power Eng. Soc. Winter
Meeting, vol. 1, 1999, pp. 437–441.
[9] W. H. E. Liu, “Classification and correlation of monitored power quality
events,” in Proc. IEEE Power Eng. Soc. Summer Meeting, vol. 1, 1999,
p. 481.
[10] D. Sawyer, “Nonstop monitoring [power quality],” Inst. Elect. Eng. Rev.,
vol. 45, no. 3, pp. 126–127, May 1999.
[11] A. Makinen, M. Parkki, P. Jarventausta, M. Kortesluoma, P. Verho, S.
Vehvilainen, R. Seesvuori, and A. Rinta-Opas, “Power quality monitoring as integrated with distribution automation,” in Proc. Elect. Dist.,
Part 1: Contributions CIRED, 16th Int. Conf. Exhibition on (IEE Conf.
Publ no. 482), vol. 2, 2001, p. 5.
[12] X. Qiu and W. Wimmer, “Applying object-orientation and component
technology to architecture design of power system monitoring,” in Proc.
Int. Conf. Power System Technology, vol. 2, 2000, pp. 589–594.
[13] R.-C. Leou, Y.-C. Chang, and J.-H. Teng, “A Web-based power quality
monitoring system,” in Proc. Power Eng. Soc. Summer Meeting, vol. 3,
2001, pp. 1504–1508.
[14] D. R. Shakarjian and R. B. Standler, “AC power disturbance detector
circuit,” IEEE Trans. Power Delivery, vol. 6, pp. 536–540, Apr. 1991.
[15] A. M. Gaouda, M. M. A. Salama, and M. R. Sultan, “Automated recognition system for classifying and quantifying the electric power quality,”
in Proc. 8th Int. Conf. Harmonics and Quality of Power, vol. 1, 1998,
pp. 244–248.
[16] A. M. Gaouda, M. M. A. Salama, M. R. Sultan, and A. Y. Chikhani,
“Power quality detection and classification using wavelet-multiresolution signal decomposition,,” IEEE Trans. Power Delivery, vol. 14, pp.
1469–1476, Oct. 1999.
[17] H.-T. Yang and C.-C. Liao, “A de-noising scheme for enhancing
wavelet-based power quality monitoring system,” IEEE Trans. Power
Delivery, vol. 16, pp. 353–360, July 2001.
[18] I. Ananth and M. M. Morcos, “A power quality monitoring system: A
case study in DSP-based solutions for electric power industry,” IEEE
Power Eng. Rev., vol. 19, no. 7, pp. 47–50, July 1999.
[19] A. Lakshmikanth and M. M. Morcos, “A power quality monitoring
system: A case study in DSP-based solutions for power electronics,”
IEEE Trans. Instrum. Meas., vol. 50, pp. 724–731, June 2001.
[20] J. N. Garcez, U. H. Bezerra, A. L. S. Castro, R. N. C. Alves, W. J. F.
Lima, A. R. G. Castro, J. H. Maciel, M. N. Moscoso, K. Tenorio, A. A.
Tupiassu, and J. E. Mesquita, “Integrating a power quality monitoring
system in a distribution control center,” in Proc. IEEE Power Tech., vol.
3, Porto, Portugal, 2001, p. 5.
[21] G. Bucci, E. Fiorucci, and C. Lani, “Digital measurement station for
power quality analysis in distributed environments,” in Proc. 18th IEEE
Instrum. Measure. Technol. Conf., vol. 1, 2001, pp. 368–373.
[22] M. Waclawiak, M. McGranaghan, and D. Sabin, “Substation power
quality performance monitoring and the Internet,” Power Engineering
Soc. Summer Meeting, vol. 2, pp. 1110–1111, 2001.
[23] C. J. Melhorn and M. McGranaghan, Interpretation and analysis of
power quality measurements, TN: Electrotek Inc..
[24] [Online]. Available: http://www.cannontech.com
[25] U. Syvid, The adaptive communication environment: ACE: Hughes Network Systems, Nov. 1998.
[26] Real-time CORBA with TAO (The ACE ORB) [Online]. Available:
http://www.cs.wustl.edu/~schmidt/TAO.html
[27] T. B. Littler and D. J. Morrow, “Wavelets for the analysis and compression of power system disturbances,” IEEE Trans. Power Delivery, vol.
14, pp. 358–364, Apr. 1999.
[28] S. J. Huang, C. T. Hsieh, and C. L. Huang, “Application of Morlet
wavelets to supervise power system disturbances,” IEEE Trans. Power
Delivery, vol. 14, pp. 235–243, Jan. 1999.
[29] P. Pillary, P. Ribeiro, and Q. Pan, “Power quality modeling using
wavelets,” in Proc. IEEE Int. Conf. Harmonics Quality Power, Las
Vegas, NV, Oct., 16–18 1996, pp. 625–631.
[30] L. Angrisani, P. Daponte, M. D’Apuzzo, and A. Testa, “A measurement method based on the wavelet transform for power quality analysis,”
IEEE Trans. Power Delivery, vol. 13, pp. 990–998, Oct. 1998.
463
[31] L. Angrisani, P. Daponte, and M. D’Apuzzo, “A virtual digital signal
processing instrument for measuring superimposed power line disturbances,” Measurement, vol. 24, no. 1, pp. 9–19, 1998.
[32] S. Santoso, E. J. Powers, and W. M. Grady, “Power quality disturbance
identification using wavelet transforms and artificial neural networks,”
in Proc. IEEE Int. Conf. Harmonics Quality Power, Las Vegas, NV, Oct.,
16–18 1996, pp. 615–618.
[33] H. W. Furst, M. Pigl, and J. Baier, “An analog method for detecting
superimposed power line voltage disturbance,” IEEE Trans. Instrum.
Meas., vol. 43, pp. 889–893, Dec. 1994.
Pasquale Daponte (M’91–SM’99) was born in Minori (SA), Italy, on March 7, 1957.
Currently, he is a Full Professor of Digital Signal
Processing and Measurement Information at University of Sannio, Benevento, Italy. He has published
many scientific papers in journals and presented
at national and international conferences on ADC
modeling and testing, digital signal processing,
distributed measurement systems, sensors, and
transducers.
Prof. Daponte is a Senior Member of IEEE
Instrumentation and Measurement Society Editorial Board of the Measurement
Journal, Elsevier Publisher; Working Group of the IEEE Instrumentation and
Measurement Technical Committee no. 10 Subcommittee of the Waveform
Measurements and Analysis Committee for the definition of the new standard
IEEE-1241 “Standard for Terminology and Test Methods for Analog-to-Digital
Converters.” He is also Secretary of IMEKO Technical Committee TC-4
“Measurements of Electrical Quantities” and coordinator of the IMEKO
Working Group on ADC and DAC Metrology. He is Rector’s Delegate for
International Relations for the University of Sannio.
Massimiliano Di Penta (S’02–A’03) was born
in Campobasso, Italy, in 1973. He received the
information engineering degree from the University
of Sannio, Benevento, Italy. He is currently pursuing
the Ph.D. degree at the University of Sannio.
Currently, he has been working in the Research
Center on Software Technology of the University
of Sannio. He is the author of several papers that
appeared at IEEE Computer Society conferences,
as well as papers that appeared in some software
engineering international journals. His research interests include software maintenance, program comprehension, object-oriented
testing, and empirical software engineering.
Gianpaolo Mercurio was born in Benevento, Italy,
in 1969. He receive the electronics engineering degree from the University “Federico II,” Naples, Italy,
in 1998
Curreently, he is a Consultant with “Edison
Energie Speciali S.p.A.,” a society of Montedison
group dealing with wind power. He is also with
the L.E.S.I.M of the University of Sannio, dealing
with research about time-frequency representations.
He teaches “Measurements for Automation and
Industrial Production” with a Faculty of Engineering
degree from the University of Sannio.