Abstract. Ferroresonance is a complex electromagnetic phenomenon that affects voltage transformers. The appearance of the ferroresonant phenomenon provokes important oscillations that are responsible for serious thermal and dielectric overstresses, which may lead to catastrophic failures in the electrical power system. This paper analyzes the behaviour of a novel ferroresonance detection technique, based on artificial neural networks, through the results obtained in several verification tests conducted in a real voltage transformer

A Etxegarai

A J Mazón

G Buigues

I Zamora

V Valverde

CiteSeerX

 
 
International Conference on Renewable Energies and Power Quality (ICREPQ’13) 
Bilbao (Spain), 20th to 22th March, 2013 
exÇxãtuÄx XÇxÜzç tÇw cÉãxÜ dâtÄ|àç ]ÉâÜÇtÄ (RE&PQJ) 
 ISSN 2172-038 X, No.11, March 2013 
 
 
 
 
 
Verification tests of a novel ferroresonance detection technique 
V. Valverde, G. Buigues, A.J. Mazón, I. Zamora, A. Etxegarai 
Department of Electrical Engineering 
Faculty of Engineering of Bilbao, UPV-EHU 
Alameda de Urquijo s/n, 48013 Bilbao (Spain) 
Phone/Fax number:+0034 946014172, e-mail:  victor.valverde@ehu.es 
 
 
 
Abstract. Ferroresonance is a complex electromagnetic 
phenomenon that affects voltage transformers. The appearance 
of the ferroresonant phenomenon provokes important 
oscillations that are responsible for serious thermal and dielectric 
overstresses, which may lead to catastrophic failures in the 
electrical power system. This paper analyzes the behaviour of a 
novel ferroresonance detection technique, based on artificial 
neural networks, through the results obtained in several 
verification tests conducted in a real voltage transformer. 
 
Key words 
Ferroresonance, Ferroresonance Detection, Voltage 
Transformer, Artificial Neural Networks 
 
1. Introduction 
 
Voltage transformers are devices particularly prone to 
ferroresonance phenomenon. This phenomenon is a 
special case of resonance that appears after transient 
disturbances (transient overvoltage, lightning overvoltage 
or temporary fault) or switching operations (transformer 
energizing or fault clearing), and its effects are 
characterized by high sustained overvoltages and 
overcurrents with maintained levels of current and voltage 
waveform distortion [1]. The practical measures that can 
be taken in order to mitigate the ferroresonance basically 
consist of introducing some losses into the system, in 
order to make the energy supplied by the power source 
insufficient to maintain this phenomenon. These losses 
can either be temporary (detection techniques) or 
permanent (prevention techniques). On the one hand, the 
latter may affect the efficiency of the installation in a 
considerable way, even provoking thermal failures under 
unbalanced situations. On the other hand, if the 
introduction of these losses is temporary, some sort of 
ferroresonance detection system would be necessary. 
 
Nowadays, the current development level of this kind of 
detection systems is quite limited, although during the last 
years, the production of detection techniques and devices 
has started to be increased. In this context, Artificial 
Neural Network (ANN) theory emerges as an optimal 
solution to tackle the difficulties of the ferroresonance 
detection, as can be shown in the detection techniques 
presented in [2-3]. 
This paper analyzes the behavior of the ferroresonance 
detection technique presented in [2] through the results of 
several verification tests conducted in a real voltage 
transformer. 
 
2. Ferroresonance detection techniques 
 
Since ferroresonance problem in voltage transformers was 
first analysed by L.N. Robinson in 1915 [4], several 
authors have studied the possibilities of preventing the 
ferroresonant oscillations in this kind of transformer [5-6]. 
Nowadays, the most common measure against 
ferroresonance is the permanent inclusion of a damping 
circuit (ferroresonance suppression circuit) in the 
secondary winding of the transformer. The final design of 
this ferrorresonance suppression circuit depends on the 
transformer type and the electric configuration of the 
power system [7-8]. Figure 1 shows the typical elements 
included in the design of a ferroresonance suppression 
circuit [9]. 
 
 
Figure 1.  Basic circuits for ferroresonance suppression [9]. 
Furthermore, some techniques based on the selectively 
connection of the damping resistor have recently been 
developed. This way the damping circuit is only 
connected under ferroresonance situation and its impact 
on the efficiency of the installation is reduced. Within this 
field of ferroresonance detection, there are currently three 
main lines of research, depending on the criteria used to 
detect this phenomenon: saturation analysis [5,10-11], 
overvoltage analysis [12-13] and current/voltage 
waveform analysis [2-3,14]. 
 
On one hand, the first line of research is based on the use 
of an additional saturable inductor, whose saturation point 
has a slightly lower value than the voltage transformer’s 
one. Consequently, the additional inductor will get 
https://doi.org/10.24084/repqj11.320 388 RE&PQJ, Vol.1, No.11, March 2013
saturated before the voltage transformer, leading to an 
early detection of the phenomenon. Its effectiveness 
depends on the degree of accuracy achieved in the design 
of the saturable inductor. On the other hand, the presence 
of overvoltages over a specified threshold may be 
implemented as an activation trigger for the connection of 
a damping circuit. This technique largely depends on the 
selected threshold value and does not take into account the 
type of event that causes the overvoltage. 
 
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
-3
-2
-1
0
1
2
3
Ferrorresonancia fundamental (50 Hz)
Tiempo (s)
T
en
si
ón
 (
p.
u.
)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
-10
-5
0
5
10
Tiempo (s)
In
te
ns
id
ad
 (
p.
u.
)
0 0.1 0.2 0.3 0.
1
3
‐
‐
‐
0
5
10
‐5
‐10
Fundamental ferroreso nce (50 Hz)
time (s)
V
o
lt
ag
e
(p
.u
.)
C
u
rr
en
t(
p
.u
.)
time (s)
0.1 0.2 0.3 0.4
 
Figure 2.  Voltage and current waveform of fundamental 
ferroresonance. 
Finally, the last line of research is based on the 
characteristic waveforms that adopt the ferroresonant 
oscillations (Fig. 2). These characteristic oscillations of a 
ferroresonant situation may allow its identification by 
recognizing the disfigurement of voltage and current 
waveforms. In this way, for the last decade, a few methods 
of ferroresonance identification have been presented by 
combining the use of Wavelet Transform (WT) and ANN 
theories [3]. These methods based their performance on 
extracting a distinctive feature of the signal, in order to be 
classified by different ANNs. 
 
 
3. Novel ferroresonance detection technique 
 
The detection technique presented in [2] uses a Multi-
Layer Perceptron (MLP) network to identify the 
waveform of the voltage signal under ferroresonant 
conditions. This MLP network is trained to analyze the 
direct sampling of a full-cycle data window of the voltage 
signal at the fundamental frequency (50 Hz).  
 
The detection system is configured to process the voltage 
signal provided by the secondary winding of the voltage 
transformer. Thus, it collects 32 samples/cycle, having a 
new sample every 0.625 ms. These samples are stored in a 
32-element vector, which is the input of the ANN after a 
conditioning stage (scale down the samples in a range 
between -1 and 1), until a data window is completed. The 
ANN processes these 32 data and provides a “0/1” 
response (non-ferroresonant/ferroresonant window) 
depending on its interpretation (Fig. 3). 
 
1
 C
Y
C
L
E
 
Figure 3.  Network structure for ferroresonance detection 
 
Once a response has been achieved, the process starts all 
over again by analyzing the next data window. This 
process is equivalent to moving the data window every 
0.625 ms, which is the time distance between two 
consecutive samples. 
 
During the training process, the Levenberg-Marquardt 
learning algorithm is used. 1,640 different neural network 
architectures have been trained, varying the number of 
neurons of the first hidden layer from 1 to 40 and the 
neurons of the second hidden layer from 0 to 40. Each 
network architecture has been tested several times, due to 
the randomness of each training. 
 
After the training and verification process, the network 
architectures with better responses correspond to networks 
with 32 neurons in the input layer, a large number of  
neurons in the first hidden layer, a few neurons in the 
second hidden layer and one neuron in the output layer. 
Whereas the sigmoid activation function has been 
implemented in the neurons of the hidden layers, the 
identity function has been applied to the neuron of the 
output layer.  
 
 
4. Verification tests 
 
In order to verify the behaviour of the novel 
ferroresonance detection technique, several simulations 
and laboratory tests have been conducted using a real 
voltage transformer valid for outdoor service. Its general 
characteristics are 20 kV / 230 V (50 Hz), 50 VA. 
 
The artificial neural network selected during the tests has 
an architecture of 32 neurons in the input layer, 30 
neurons in the first hidden layer, 2 neurons in the second 
hidden layer and one neuron in the output layer. 
Previously, this network has been tested by means of 
software simulation providing highly satisfactory results. 
 
Once the software verification has been performed, 
several laboratory tests have been conducted in a 
specialized laboratory. The voltage transformer has been 
tested under no-load conditions using different switching 
times in order to force the ferroresonance to appear. Short-
circuit situations have also been provoked to force the 
phenomenon. A damping resistor has been used to damp 
https://doi.org/10.24084/repqj11.320 389 RE&PQJ, Vol.1, No.11, March 2013
the ferroresonant oscillations. Additionally, a series fuse 
has been installed in order to protect the circuit.  
 
Figure 4 shows a basic schematic diagram of how the 
laboratory tests have been conducted. It includes a 6.7 nF 
(50 kV of breakdown voltage limit) series capacitor and a 
100 Ω resistor (damping resistor). 
 
20 kV / 230 V  
Figure 4.  Basic schematic diagram of the laboratory set-up. 
In order not to jeopardise the integrity of the capacitor due 
to the ferroresonant overvoltages, the source voltage level 
has been limited to 28.5/√3 kV (Fig. 5) 
 
Below, four of the conducted laboratory tests are 
analyzed. 
 
 
Figure 5.  Laboratory set-up 
7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8
-400
-200
0
200
400
Tensión
tiempo (s)
T
en
si
ón
 (
V
)
7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8
-0.5
0
0.5
1
1.5
Respuesta Sistema Detección (1 = Ferrorresonancia ; 0 = No Ferrorresonancia)
tiempo (s)
R
es
pu
es
ta
Respons  of the detection tech ique (1 = Ferr reson nce; 0 = No Fe oreso ance)
Voltage
ti e (s)
ti e (
V
o
lt
ag
e
(V
)
R
es
p
o
n
se
0 1 2 3 4 5 6 7 8 9 10
-500
0
500
Tensión
tiempo (s)
T
en
si
ón
 (
V
)
0 1 2 3 4 5 6 7 8 9 10
-0.5
0
0.5
1
1.5
Respuesta Sistema Detección (1 = Ferrorresonancia ; 0 = No Ferrorresonancia)
tiempo (s)
R
es
pu
es
ta
Response of the detection technique (1 = Ferroresonance; 0 = No Fe roresonance)
Voltage
ti e  )
time (s)
V
o
lt
ag
e
(V
)
R
es
p
o
n
se
 
Figure 6.  Response of the novel detection technique during verification test #1 
12.8 12.9 13 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8
-600
-400
-200
0
200
400
Tensión
tiempo (s)
T
en
si
ón
 (
V
)
12.8 12.9 13 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8
-0.5
0
0.5
1
1.5
Respuesta Sistema Detección (1 = Ferrorresonancia ; 0 = No Ferrorresonancia)
tiempo (s)
R
es
pu
es
ta
0 5 10 15 20 25 30 35
-600
-400
-200
0
200
400
600
Tensión
tiempo (s)
T
en
si
ón
 (
V
)
0 5 10 15 20 25 30 35
-0.5
0
0.5
1
1.5
Respuesta Sistema Detección (1 = Ferrorresonancia ; 0 = No Ferrorresonancia)
tiempo (s)
R
es
pu
es
ta
Response of the detection technique (1 = Ferroresonance; 0 = No Ferroresonance)
Voltage
ti e (s)
ti e (s)
V
o
lt
ag
e
(V
)
R
es
p
o
n
se
Response of the detection technique (1 = F rroresonance; 0 = No Fe roresonance)
Voltage
ti e  s)
time (s)
V
o
lt
ag
e
(V
)
R
es
p
o
n
se
 
Figure 7.  Response of the novel detection technique during verification test #2 
https://doi.org/10.24084/repqj11.320 390 RE&PQJ, Vol.1, No.11, March 2013
29.2 29.3 29.4 29.5 29.6 29.7 29.8 29.9
-500
0
500
Tensión
tiempo (s)
T
en
si
ón
 (
V
)
29.2 29.3 29.4 29.5 29.6 29.7 29.8 29.9
-0.5
0
0.5
1
1.5
Respuesta Sistema Detección (1 = Ferrorresonancia ; 0 = No Ferrorresonancia)
tiempo (s)
R
es
pu
es
ta
0 5 10 15 20 25 30 35
-600
-400
-200
0
200
400
600
Tensión
tiempo (s)
T
en
si
ón
 (
V
)
0 5 10 15 20 25 30 35
-0.5
0
0.5
1
1.5
Respuesta Sistema Detección (1 = Ferrorresonancia ; 0 = No Ferrorresonancia)
tiempo (s)
R
es
pu
es
ta
Response of the detection tech ique (1 = Ferr reson nce; 0 = No Ferroreso ance)
Voltage
ti e (s)
ti e 
V
o
lt
ag
e
(V
)
R
es
p
o
n
se
Response of the detection technique (1 = Ferroresonance; 0 = No Ferroreso ance)
Voltage
time (s)
time (s)
V
o
lt
ag
e
(V
)
R
es
p
o
n
se
 
Figure 8.  Response of the novel detection technique during verification test #3 
6.5 7 7.5 8 8.5
-300
-200
-100
0
100
200
300
Tensión
tiempo (s)
T
e
ns
ió
n 
(V
)
6.5 7 7.5 8 8.5
-0.5
0
0.5
1
1.5
Respuesta Sistema Detección (1 = Ferrorresonancia ; 0 = No Ferrorresonancia)
tiempo (s)
R
es
pu
es
ta
0 5 10 15 20 25 30 35 40 45
-400
-200
0
200
400
Tensión
tiempo (s)
T
e
n
si
ó
n
 (
V
)
0 5 10 15 20 25 30 35 40 45
-0.5
0
0.5
1
1.5
Respuesta Sistema Detección (1 = Ferrorresonancia ; 0 = No Ferrorresonancia)
tiempo (s)
R
e
sp
u
e
st
a
Response of the detection technique (1 = Ferroresonance; 0 = No Ferroresonance)
Voltage
time (s)
time ( )
V
o
lt
ag
e
(V
)
R
es
p
o
n
se
Response of the detection technique (1 = Ferroresonance; 0 = No Ferroresonance)
Voltage
time ( )
time 
V
o
lt
ag
e
(V
)
R
es
p
o
n
se
 
Figure 9.  Response of the novel detection technique during verification test #4 
 
 
 
A. Verification test #1 
 
Firstly, the transformer is energized under no-load 
conditions, establishing a ferroresonant state at the 
fundamental frequency. Once the novel detection 
technique is applied, the ferroresonance is detected. 
Approximately at the second 7.5, it is subsequently 
damped using a 100 Ω resistor in the secondary winding 
(Fig. 6). 
 
 
B. Verification test #2 
 
After 5 seconds, a first energization of the transfomer 
under no-load conditions provokes ferroresonant 
oscillations at the fundamental frequency. The novel 
detection technique works perfectly and, approximately at 
the second 12, the 100 Ω resistor is connected damping the 
phenomenon. From this moment on, the 100 Ω resistor is 
consecutively connected and disconnected several times, 
leading to an intermittent appearance of the 
ferroresonance.  
 
In every case, the novel detection technique successfully 
detects it (Fig. 7). Around the second 27.5, the 100 Ω 
resistor is disconnected for the last time but the 
transformer does not reach a ferroresonant state. This is 
because the appearance of the phenomenon is strongly 
dependent on the exact moment of time in which the 
connection/disconnection is accomplished. Even in this 
last case, the novel detection technique works perfectly 
identifying a non-ferroresonant state. 
 
 
C. Verification test #3 
 
Since a first energization of the transfomer under no-load 
conditions does not provoke the appearance of the 
ferroresonance, the transformer is disconnected for 
around 15 seconds. Then it is connected again and the 
ferroresonant oscillations appear. Just before the second 
30, the 100 Ω resistor is connected damping the 
phenomenon. Almost 2 seconds later, this resistor is 
disconnected without the appearance of the ferroresonant 
phenomenon (Fig. 8). Through the whole process, the 
https://doi.org/10.24084/repqj11.320 391 RE&PQJ, Vol.1, No.11, March 2013
novel detection technique succeeds in identifying the 
ferroresonance every time it appears. 
 
D. Verification test #4 
 
The transformer is energized under load conditions (100 Ω 
resistor is connected) and, during 45 seconds, its secondary 
winding is shortcircuited several times (Fig 9). This is 
performed in order to get a transient that triggers a 
ferroresonant state but none of the shortcircuits establishes 
the phenomenon. The novel detection technique 
successfully identifies the whole process as non-
ferroresonant. 
 
Conclusion 
 
This paper presents the results provided in several 
verification tests for a ferroresonance detection technique 
based on Artificial Neural Networks. During the conducted 
tests a real voltage transformer has been tested under 
ferroresonance conditions. It is shown that the novel 
detection technique clearly identifies the ferroresonance, 
as well as the non-ferroresonant oscillations providing a 
continuous output value of 1 or 0 respectively.  
 
 
Acknowledgement 
 
The work presented in this paper has been supported, 
partially, by the Basque Government (Ref. IT532-10) and 
by the University of the Basque Country UPV/EHU (UFI 
11/28). 
 
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Verification tests of a novel ferroresonance detection technique

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Verification tests of a novel ferroresonance detection technique

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