Journal of Power and Energy Engineering, 2013, 1, 30-40 Published Online October 2013 (
Copyright © 2013 SciRes. JPEE
Early Failure Detection i n Power Transformers
Luiz Eduardo Borges da Silva1, Erik Leandro Bonaldi2, Levy Ely de Lacerda de Oliveira2,
Germano Lambert-Torres2, Giscard F. C. Veloso 1, Ismael Noronha1, Felipe dos Santos Moreira3,
José Nielze Caminha3
1Itajuba Federal University, Itajuba, Brazil; 2PS Solutions, Itajuba, Brazil; 3Termopernambuco Thermal Power Plant, Recife, Brazil.
Email:,,, erik@pssoluç, levy@pssoluç,
Received August 2013
This paper presents equipment for early detection of failures in the insulation of power transformers, checking existing
partial discharges inside. The equipment involves hardware, control and signal acquisition software, and signal analysis
software. This equipment has a set of algorithms that were made with intelligent extraction techniques and interpreta-
tion of data. The degradation diagnosis of the equipment insulation is based on digital signal processing algorithms for
extraction of features and also in artificial intelligence algorithms that allies a mining involving all the data linked to the
equipment throughout its operating life can make an assessment of the operating conditions of the equipment and sug-
gest interventions and provide an estimated time so that they have to be made.
Keywords: Transformers; Measuring System; Signal Processing; Partial Discharges; Predictive Maintenance
1. Introduction
Transformers are among the most important equipment
of the electrical power systems. Currently, the level of
reliability required of electricity companies causes the same
have a high degree of functional characteristics informa-
tion of their equipment, in special their transformers [1].
The economic implications involving operation of equip-
ment failures electrical network ar e huge then the integr-
ity of the operation of each one of its components must
ensure. This integrity can be achieved with the imple-
mentation of new technologies for monitoring and evalu-
ation of their performance [2]. However with time and
the loading scheme, the electrical system submits this
equipment to stresses, which over time can lead to reduc-
tion in their processing capacity, resulting in uncertainty
about its real operational capacity [3]. Therefore, a care-
ful and rigorous as sessment of the operating condition of
a set of equipment will produce, for sure, a substantial
increase in the reliability of the electric system, a visible
reduction of costs with regard to the correct treatment
involving preventive maintenance properly executed and
the possibility to avoid operational failures in such equip-
In order to avoid unscheduled interruptions in the op-
eration of complex systems due to unscheduled contin-
gency, systematic maintenance procedures have been de-
veloped over the last forty years. Initially only the pro-
cedures involving a corrective maintenance were used.
That is, if the equipment failure occurs and the equip-
ment stops to run, the repair procedure starts. Then, it
involves, of course, interruption in the availability of the
equipment in question. With the passage of time and the
accumulation of knowledge involving the operation of
equipment, power network engineers have developed pre-
ventive maintenance procedures that trigger the process
of maintenance before the crash happen, based on the
history of each equipment.
Although it produces a satisfactory result in terms of
reduction of electricity outages for consumers, this pro-
cedure implies somet imes in unneces sary pre-programmed
maintenance, because the equipment suffered the inter-
vention is in perfect working order, even though it has
been running for a number of hours, then the average of
the same type of equipment would be indicating the prox-
imity of a failure.
Recently with the advancement of digital acquisition
systems and digital signal processing, another mainten-
ance strategy is being developed [4]. This new technique,
called predictive maintenance, aims to develop a diag-
nostic process of the equipment under supervision, so
that the indication for a maintenance intervention is in-
dicated only when the operating state of the equipment
will provide an important deterioration condition, stating
to its level of deterioration and showing an estimate of
how long it can continue to function without a failure.
Early Failure Detection in Power Transformers
Copyright © 2013 SciRes. JPEE
This paper treats exactly of this type of maintenance
for transformers. The proposed methodology uses partial
discharges signals occurred inside of the transformers.
The use of partial discharges is one of the types of failure
that deserves mention because it contains much informa-
tion about inside problems in transformers. The partial
discharges are failures which are developed silently, and
able to produce an unexpected interruption in the trans-
The partial discharge phenomenon is a problem in the
insulation system, which manifests itself through tiny elec-
tric arcs within the dielectric material, causing its degra-
dation to culminate in the complete failure and possible
destruction of the equipment. The activity of partial dis-
charges in transformers must be monitored in order to
follow the evolution of this problem to plan an interven-
tion before a catastrophic failure occurs. Therefore, a care-
ful and rigorous assessment of the operating c ondition of
the transformers produces a substantial increase of its
reliability, a visible reduction of costs with regard to the
correct treatment involving preventive properly executed
and the possibility of to avoid operational failures of the
In this way, a device that detects internal defects early,
allows the maintenance activities may be planned to mi-
tigate these interruptions. This planning can be inserted
within the culture of predictive maintenance and aims to
develop a diagnostic process of the equipment under su-
pervision, so that the indication for a maintenance inter-
vention is indicated only when the operating state of the
equipment provides an important deterioration of condi-
tion. Their continued use creates parameters on the level
of deterioration in which the same lies and presents a
forecast of how long it can continue to function without
widespread failure will happen.
As the process of partial discharge s appears initially as
a one-off failure, sometimes with micro scop ic d imen sions,
can occur in any point of isolation and evolve to com-
promise it entirely. As partial discharges which occur in
the interior of the windings represent an important point
of information on their conditions of aging and deteriora-
tion, the proposed device is useful for any company with
The proposed evaluation methodology of transformer
operational conditions has been implemented in a proper
designed data acquisition system to do a sampling of the
variables involved in the process of deterioration of the
observed equipment.
The developed system is based on the digital signal
processing of the information contained in the electrical
variables involving the operation of the transformer, es-
pecially information directly obtained from the current
flowing through the windings of the transformer. Wave-
let transform was the chosen digital technique due to its
quality to extract information inside of the innumerous
other signals contained in the current. On the basis of
information extracted specifically currents of the trans-
former, with the use of Rogowski coil type sensors, ex-
pects to be able to infer about the operating conditions of
the equipment insulation. This is possible to expect be-
cause the standard involving the behavior of partial dis-
charges monitored presents a strong degree of correlation
with the deterioration of th e conditions of the isolation of
the same. In this way, through the information contained
in the acquired signals, extracted after an adequate digital
signal processing, it is possible to obtain an assessment
of the state of the windings of the observed equipment.
2. Insulation Degradation by Partial
The normal or accelerated aging of insulation deteriora-
tion caused by partial discharges and oil contamination
cause changes in the dynamic characteristics of the sys-
tem formed by the metal structure of the transformer and
its isolation. Considering the fact that partial discharges
are caused by non-homogeneity in isolation, contamina-
tion and overloading, the electrical signals of partial dis-
charges obtained this process may prove, through an
analysis of their distribution in the phase of the voltage
signal and its waveform, those features that are related to
the problems which want to diagnose. So, establishing a
chronology of measurements, the evolution of these prob-
lems can be modeled and forecasted a possible disrupting
of insulation, preventing this from happening through
appropriate intervention.
A partial discharge is an electric pulse or electric dis-
charge that occurs inside an insulating material under an
electric field. This pulse or discharge only partially closes
the circuit between the isolated conductive parts, hence,
the name, partial discharge. The partial discharges can be
generated by various types of mechanisms. In general, it
occurs due to a rapid change in the configuration of the
electric field due to some kind of internal electrical activ-
ity in to the presence of contaminants or particles. This
change leads to the appearance of an electric current
flowing in a conductor connected to the exterior of the
apparatus, being the effect most common and most ex-
ploited for parti a l discharge measure ments.
Power transformers are among the more expensive
equipment of the electrical system. So they are also made
and maintained to be most reliable equipment, which is
essential to the operation of the entire system. Proof of
this is the presence of power transformers with more than
30 years ago are still in operation. However, this reputa-
tion is maintained at the cost of maintenance of excel-
lence, which includes strict monitoring of their condi-
tions. This is absolutely necessary, because the transfor-
mers are strategic parts of the electrical system and fail-
Early Failure Detection in Power Transformers
Copyright © 2013 SciRes. JPEE
ures in their operation can cause huge problems such as
power interruption to larg e populations and for important
productive sectors. Their catastrophic destruction can cause
damage to other equipment, injuries in employees of elec-
tric utilit ies, en v iro n menta l po llu tion wi th to xic substances
leading to fines and penalties due to environmental leg-
islation. All these problems represent high costs that can
be avoided with a proper maintenance based on condition
monitoring. In this context, the transformer insulation
system is a critical aspect of this monitoring.
Large power transformers are isolated with oil, paper
and porcelain (bushings). The oil, in general, does not
have a very high degree of purity, leading to partial dis-
charges generation mechanisms associated with impuri-
ties su ch as gas bubbles (wells) and solid particles (coro-
na in liquids). On paper, the presence of insulating cavities
is the main cause of partial discharges, and deterioration
by the presence of moisture and even by the partial dis-
charge activity action, tends to decrease its dielectric
properties. Thus, not only the aging of the insulation
system components contributes to the activity of partial
discharge in transformers, but also oversights in the con-
struction or operation of the equipment may cause dam-
age, as the oil contamination leading to an increase in the
intensity of discharges and the reduction of their working
Therefore, knowing how the partial discharges manif-
est themselves not only in a specific point, but in many
points inside the transformer, and knowing its behavior
over time, it is possible to infer about the state of the
monitored transformer, and even to anticipate potential
failures, which represent a great savings in maintenance
In general, depending on how the partial discharge is
manifesting, it can be inferred not only the seriousness of
the problem and its possible causes. Partial discharges
occur predominantly in the first and third quarters of the
voltage cycle, as shown in Figure 1 (pulses are exagger-
ated in the figure). In these quarters, the voltage value
growth occurs. The electric system formed by conductors
and insulation acts as a capacitor being loaded up to the
point at which partial discharges begin to appear. They
Figure 1. Occurrence of partial discharges in the cycle.
can be measured as voltage pu lses of high frequency and
low intensity. In the first quarter of the voltage, the dis-
charge cycle results in a negative pulse (or downward).
This is called the partial discharge of negative polarity.
In the third quarter of the cycle, the partial discharge re-
sults in a pulse-driven upwards, or positive, being called
a partial discharge of positive polarity. These pulses are
on the order of from millivolts to some units of volts.
Although appearing in Figure 1 as a pulse for each quar-
ter of a cycle, in fact they appear in large quantities, be-
ing your score within a cycle of rooms measured pa-
In general, a partial discharge has duration of no more
than one or two nanoseconds. However, the signal de-
tected outside of the equipment under test depends on the
nature of the connection between the point where the
partial discharge and the exterior. In good condition (case
of coaxial cables insulated with SF6), there are very few
losses (due to its good response at high frequencies), the
signal detected abroad be preserved almost intact, as will
suffer little attenuation or scattering. But there are cases
where there are major changes in the shape of the wrist
due to huge losses at high frequencies and due to the
complexity of the connection between the places where
the partial discharge and the outer, intrinsic detection to
each type of equipment under test.
3. Discrete Wav e let Transform
The discrete wavelet transform is a form of expansion.
The main difference for most expansions is in the organ-
ization of their coefficients, which vary depending on
two parameters instead of just one as in Fourier series.
The functions that form the Foundation are the wavelet
calls that can be arranged to form orthogonal sets. The
discrete wavelet transform coefficients are arranged at
different levels, which represent the different resolutions,
or scales, and are given as a function of time. The ob-
taining of these coefficients can be interpreted through
correlation between the sign and the functions of the base,
reinforcing the concept of multi-resolution [5].
Wavelets are waveforms of a limited duration, average
value equal to zero and unit standard. These waveforms
have concentrated their energy on time, which gives an
ability to use in expansion systems for non-stationary
analysis, i.e. for analysis where the signal is non-statio-
nary or non-stationary components has (transient) [6].
The use of wavelets as expansion set is what is usually
called Wavelet System. This expansion set is not unique,
as is the case of the Fourier series expansion, for example,
which is formed only by complex exponentials. In fact,
there are many different Wavelet systems, and their co m-
mon features are:
a) Can form a set of building blocks: this set can be
used to construct or represe nt s igns and f unc tions;
Early Failure Detection in Power Transformers
Copyright © 2013 SciRes. JPEE
b) By providing a location and time signal frequency,
the wavelet expansion represents the largest part of the
energy of the signal with few coefficients;
c) The calculation of the coefficients of the expansion
is done efficiently;
d) The wavelet systems or different wavelet families
have waveforms that balance in a relationship between
how the functions are compactly located in time or in
space (duratio n) and how soft they are;
e) Multi-resolution;
f) The value of the coefficients of the expansion
quickly decays to zero;
g) Ability to perform a more accurate location descrip-
tion and a separation of signal characteristics; the wave-
let expansion coefficients represent the correlation be-
tween the signal stretch and the wavelet; “stretch” be-
cause the wavelet is a wave of limited support (limited
duration), thus, the coefficients represent components that
are per se;
h) The possibility to define wavelet syste ms persona-
lized” for specific applications.
There is a line of research that studies the characteris-
tics and wavelet system develops. In the present study,
existing systems are used, as for example the Daubechies
wavelet family.
A generic form, the wavelet expansion can be written
throug h as follows:
However, as one of the characteristics of wavelet is to
have an own unitary standard, it means a unitary energy,
therefore the analysis expression becomes:
The coefficients vary with two parameters: the time
shift k, and range j, unlike, for example, Fourier series,
where the coefficients vary just w ith the frequencies. For
each scale, there is a set of coefficients vary in time.
The expansion is made up of versionsof the moth-
er-wavelets scaled and displaced in time. This means that
there is a waveform with a format determined by the
characteristics of the wavelet (the mother wavelet) and
the entire expansion is created from it, varying its posi-
tion in time or space (independent variable) and varying
its range, i.e. “stretchingor shrinkingthe waveform.
The scale is a parameter inversely proportional to fre-
4. Presentation of the Proposed Device
The system consists basically of current transd ucers with
adequate pas s band, based on the techno logy of Rogows-
ki coil, a signal conditioning circuit to adjust the signal
measured data acquisition circuit, an analog/digital con-
version circuit for high speed and resolution, a computer
to the digital processing and storage of electrical variables
and measures a smart program data consolidation, evalu-
ation and diagnosis of the operating condition of the
equipment. A signal processing technique that has brought
important results in the detection of partial discharges is
a technique called Wavelet Transform, which is used in
this development [7]. This type of transform was also
implemented in the process of breakdown of fundamental
characteristics related to th e cond itio ns of the transformer
The development makes an analysis of the characteris-
tic of current signal involving the partial discharges cir-
culating by the equipment housing, being measured in a
specific point on the connection with the earth.
4.1. Detection of Partial Discharges Using
Rogowski Coil
A technique that takes advantage of the enormous possi-
bilities of Rogowski effect sensor and the processing power
of the discrete wavelet transform for detection of partial
discharge in transformers is described.
The partial discharge pulses are generated in high fre-
quencies, but depending on the circuit that range from
the inside of the transformer to the point where it is de-
tected, may suffer several mitigations, which significant-
ly changes its waveform and, especially, their range con-
siderably, reducing it, causing pulses with small ampli-
And more, to small amplitude pulse problem, the noise
problem also must be added. At a substation, there are
several sources of noise such as radio waves, electrostatic
discharge, coronas, lightning, in addition to the thermal
noise. The signal acquisition equipment itself for having
high bandwidth for signs of partial discharges also con-
tributes to contamination with white noise. So, a simple
visual analysis of a signal collected, for example, a tran s-
former grounding terminal, may not reveal anything re-
lated to par tial discharges. It is essentia l, therefore, a more
accurate analysis using digital signal processing tech-
Taking into account the transformer ground terminals,
as one of the paths through which pulses of partial dis-
charges in the form of electric current, its detection could
be monitoring these sites. For this, the chosen sensor,
besides being able to detect high frequency currents,
must also provide a good electrical insulation. The ideal
device for this task is in fact Rogowski coil.
4.2. Rogowski Coil Composition
As shown in Figure 2, Rogowski coil shall consist of a
circular plastic mould with a mounted in such a way as to
have a density of turns evenly distributed. The cross sec-
Early Failure Detection in Power Transformers
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Figure 2. Rogowski coil [8].
tions should also keep their uniform areas. The coil must
be mounted around the conductor where the current must
be measured. A voltage proportional to the current varia-
tion appears between the winding terminals, according to
the Equation (3), where H is related to the sensitivity of
the coil. So, integrating this voltage signal, the required
current signal appears.
Rogowski coil has great sensitivity to high frequencies
and its construction provides excellent electrical insula-
tion. It is also able to measure high current levels, rang-
ing from a few hundred of miliamperes up to hundreds of
thousands amps.
Installing a Rogowski coil in grounding terminals of
the transformer, the current signal at this point can be mo-
nitored. The use of proper techniques of signal processing
allows you to extract the partial discharge pulses which
eventually existing in those terminals.
4.3. Signal Processing
Considering the characteristics of partial discharge pulses
that can be detected in the ground terminals of the trans-
former, it should establish a good signal processing strat-
egy collected, taking into account all the issues involved.
The basic mathematics of signal processing strategy is
the discrete wavelet transform. It provides noise reduc-
tion and simplifies detection of transient. The detailing of
the strategy is done below.
The separation of features for frequency bands can be
useful when your signal noise reduction is required. To
accomplish that, the Inverse Discrete Wavelet transform
must be used. The inverse transform is able to recon-
struct an original function (or signal) from the coeffi-
cients, in a reverse path of the transform application.
However, as in discrete wavelet transform, a two-dimen-
sional set of coefficients, which represent different signs
that make up the original signal, can rebuild the original
signal (or part of it) by simply selecting the appropriated
So, if some decomposition levels correspond to spu-
rious signals, the corresponding coefficients can drop and
reconstruct the signal without the noisy part. This con-
cept is formally shown in [9].
To illustrate the noise reduction, Figure 3 displays a
sine signal contaminated with white noise. The decom-
position of this four-level signal shown in Figure 4 note
that the coefficients of the details levels contain almost
all the energy of the noise.
Determining a threshold below which all coefficients
in these levels are considered noise, just delete them (re-
place with a null value) and rebuild the sign with the
modified coefficients resulting in the graph in Figure 5.
Despite the small deformity in the third cycle, it can be
said that the noise reduction was successful. In [7,9], a
way of calculating the threshold of the noise is presented.
To apply the discrete wavelet transform to a sign con-
taining a relatively high frequency transitory, the orig inal
signal is decomposed into various levels (or frequency
bands, as explained earlier), separating the stationary sig-
nal and the transient signal. Proper reconstruction of le-
vels that contain the trans ient allows viewing it and being
able to determine when and how long it lasted.
An important fact that must be taken into account is
that the same sign where you want to search for partial
discharge pulses also suffered a noise reduction using the
discrete wavelet transform. In this case, if this process is
not well done, the pulses that you want to find can be
eliminated along with the noise, since they are very small
amplitudes, almost totally immersed in the noise.
Thus, the noise removal using the Discrete Wavelet
transform combined with the search for corresponding to
transient partial discharge pulses depends largely on the
choice of the wavelet whose waveform must be well
correlated with the pulse. This makes the coefficients
related to your wrists have higher values and stay above
the threshold noise reduction.
Figure 3. Example of sine signal contaminated with noise.
Early Failure Detection in Power Transformers
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Figure 4. Noisy signal decomposition on 4 levels with the discrete wavelet transform.
Figure 5. Signal after the noise reduction processing.
4.4. The Proposed Device
The equipment developed, shown in Figure 6, uses the
technique of signal collection that explores the linkage
coil of Rogowski effect so as to minimize noise and give
emphasis to the signs of partial discharges. The condi-
tioning of the subsystems (microcomputer, purchasing
Figure 6. The proposed device.
system, battery, filters, Rogowski probe Integrator) pre-
dicted a total shielding to minimize the interference typical
of the measuring area.
Figure 7 shows some internal parts of the device. It
was necessary a forced cooling scheme, since they are all
confined. Finally, the anti-aliasing filter essential for the
conversion of analog signals to digital [10], has been
properly sized for the application that aims in signal ac-
Early Failure Detection in Power Transformers
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Figure 7. Internal part of the proposed device: (1) anti-
aliasing filter, (2) 12v/1.3 Ah battery; (3) connection be-
tween passage box modules; and (4) notebook control mod-
quisition system, as shown in Fi gure 8 .
This version of the program brings a Graphical User
Interface (GUI) design ed to facilitate its handling, as well
as make more intuitive analysis of collected signals. The
interface has a simple introduction, with all commands
and settings visible and easy to use, as can be seen in
Figure 9. All interfaces are written in Portuguese lan-
guage (the native language in Brazil).
In Figure 9, the graph in the region 1 shows the
processed signal after noise reduction; and the region 2
shows the wavelets used in the procedure for noise re-
duction (or elimination). The region 3 the user must in-
dicate the number of cycles of 60 Hz (ordinary funda-
mental frequency in Brazil) to be sampled.
In the region 4, the user selects a wavelet and the
amount of decomposition levels of discrete wavelet trans-
form to be applied to the signal. The threshold value is
indicated by a pair of red lines cutting each chart of coef-
ficients, shown in the graphs of region 2. When the user
acts on the buttons of threshold, these red lines close on
the coefficients so that the part of the signal to be be-
tween them is considered noise and the algorithm re-
moves it.
In the region 5, the user selects the parameters for the
search for transient signals. This algorithm also makes
use of wavelet technique and thus it depends on the choice
of a wavelet and number of decomposition levels to be
used. In addition, a threshold gain value must be pro-
vided in order to make more selected search. It means
that if the value is higher, the largest amplitudes have the
transient to be selected.
For instance, besides to the waveform visualization of
transient (region 2 in Figure 10), the user can see the
position in the original signal. In the region 1 of the Fig-
ure 10 (Captured Signal part), where appears the original
signal, also appears a red rectangle on the sign that con-
tains the transient that is being displayed.
Figure 8. The antialiasing filter circuit.
5. Real Illustrative Example
The application of tests have been carried out in three 29
MVA transformers of Termopernambuco Thermal Power
Plant, located in the port of Suape, near Recife, in North-
east of Brazil. Several signs were collected using the
grounding terminals of t hese transform ers. Figure 11 shows
one of the observed transformers; while the Figure 12
shows the installation of the proposed device in the
grounding of observed transformer.
An example of the captured signal of this transformer
is shown in Figure 13. Continuing with the analysis, the
next step is the noise reduction. It is done by selecting
the wavelet db8 and 4 levels of decomposition, conduc-
ing to gra p hs of Figure 14.
And the user can ask for the program to compute the
best adjustment of the thresholds for the wavelet coeffi-
cients of Figure 14, resulti ng i n the g ra phs of Figure 15.
Finally, the user can visualize the signal without noise,
as shown in Figure 16, and the transient, shown in re-
gion 2 of Figure 17.
6. Conclusions
Unscheduled outages of transformers can cause huge power
system problems, social disorder and financial losses.
The only way to avoid unexpected shutdowns by failures
of any kind in transformers is through a good mainten-
ance strategy.
This paper has presented a device based on the tech-
niques of Rogowski coil and wavelet transform, which is
being used in the collection and analysis of signals of
Termo Pernambuco Thermal Power Plant transformers.
Several tests were produced, including a very interesting
collection, that was two of the transformers working with
cargo and a third empty; in this way, it was possible a
clear comparison of the activity relates to partial dis-
charges in each transformer operation cycles.
Early Failure Detection in Power Transformers
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Figure 9. Main screen of the proposed software: (1) processed signal after noise reduction; (2) procedure for noise reduc-
tion/elimination; (3) sampling and signal scaling to be analyzed; (4) adjustments to the wavelet decomposition used in noise
reduction; and, (5) parameters for the search for transient.
Figure 10. Transient waveform (2) and its position in the original signal highlighted by the red rectangle in the original signal (1).
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Figure 11. 29 MVA transformer of Termopernambuco Thermal Power Plant.
Figure 12. Installation of the proposed device in the grounding of the 29 MVA transformer.
Figure 13. Original captured signal.
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Figure 14. Coefficients of wavelet decomposition of the original signal.
Figure 15. Adjustement of the wavelet coefficient decomposition of the original signal.
Figure 16. Original capt ured signal without noise reduction.
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Figure 17. (1) Original captured signal (the transient wave is located in red rectangle), and (2) transient waveform.
7. Acknowledgements
The authors gratefully acknowledge the technical contri-
butions of Termo Pernambuco Thermal Power Plant en-
gineers. And also, the academic authors would like to
thank CNPq, CAPES, and FAPEMIG for the partial fi-
nancial suppo rt of this project.
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