Analysis of Transformer Loadings and Failure Rate in Onitsha Electricity Distribution Network

American Journal of Electrical and Electronic Engineering, 2016, Vol. 4, No. 6, 157-163
Available online at http://pubs.sciepub.com/ajeee/4/6/2
©Science and Education Publishing
DOI:10.12691/ajeee-4-6-2
Analysis of Transformer Loadings and Failure Rate in
Onitsha Electricity Distribution Network
Hachimenum N. Amadi*
, Fabian I. Izuegbunam
Department of Electrical & Electronic Engineering, Federal University of Technology, Owerri, Nigeria
*Corresponding author: amadihachy@gmail.com
Abstract This study investigated transformer loadings and failure rate in the Onitsha Electricity Distribution
Network by using the Electrical Transient Analysis Program (ETAP) software 12.6 and the Statistical Package for
the Social Sciences (SPSS) software 16.0. Data collected over the period 2011-2015 on the distribution network
were simulated on ETAP software using the Newton-Raphson (N-R) technique to determine the transformer
loadings while responses to 350 copies of questionnaire distributed among the technical staff were statistically
analysed on the SPSS software to ascertain the failure rate among transformers in the network. The findings of the
study show that during the 5 years period covered by the study, the sampled substations recorded transformer
average failure rate of 11.7 %. It was further revealed that besides insulation issues which accounted for 24.2% of all
the failures, overload (22.5%) was the next major cause of transformer breakdowns in the distribution network. The
study recommends installation of more transformer units, use of high quality transformers, balanced loading of the
transformers and proactive inspection and maintenance program of transformers units within the network. The
outcome of this work would help electricity utilities provide more reliable and cost effective services to customers.
Keywords: distribution network, electricity supply failures, failure rate, transformer failure, transformer loading
Cite This Article: Hachimenum N. Amadi, and Fabian I. Izuegbunam, “Analysis of Transformer Loadings
and Failure Rate in Onitsha Electricity Distribution Network.” American Journal of Electrical and Electronic
Engineering, vol. 4, no. 6 (2016): 157-163. doi: 10.12691/ajeee-4-6-2.
1. Introduction
The recent reforms in the Nigerian power sector pose
fresh challenges to the nation’s electricity industry such
that most electricity utilities now resort to increased
equipment utilization, deferred capital expenditures and
reduced maintenance expenditure in the provision of
electricity to the consumers. Transformers, for instance,
are now frequently operated beyond the nameplate rating
in order to meet increases in energy demand either on
short term emergency such as the loss of another
transformer in a substation or on a long term basis [1].
This happens because it is considered a more economic
strategy to overload an existing transformer than to install
a new transformer unit [2]. Consequently, while load on
each transformer continues to grow at about 2 % per year,
installation of new transformer unit keeps declining [3].
Certain factors such as the hot spot temperature, the top
oil temperature and the ratings of the ancillary equipment:
the bushings and the load tap changers (LTCs) determine
how much load the transformer can support beyond the
nameplate rating [1]. However, utilities now overlook
these factors and the safety of the transformer thereby
leading to increased cases of transformer failures and
cutting off of power supply to the service areas concerned.
According to ref [4], an average failure rate of
approximately 0.5 % is often designated to European
substation transformers while the U.S. Department of
Energy (DOE) estimate gives transformer constant failure
rate of 0.5 %/year due to lightning and other random
failures unrelated to transformer age [5]. Researches have
also put the failure rate of transformers in India and many
developing economies in the range 12% -15% as against
less than 1% in developed countries [6].
Power system experts had declared that given “ideal
conditions”, a transformer can last 30 to 40 years. Recent
studies, however, have found this claim not to be
completely true. A study carried out by Hartford Steam
Boiler (HSB) in 1975, for instance, revealed that
transformer average age at time of failure was 9.4 years. A
further research by HSB in 1985 indicated the average age
of a transformer as 11.4 years. Another related study
spanning a 10-year period from 1988-1997 still by HSB
put the transformer average age at 14.9 years [3]. These
statistics underscore the need to undertake periodic checks
on transformers to ascertain the operational condition and
proactively avert possible sudden breakdowns.
Recent researches have also pinpointed electrical
disturbances, lightning, insulation degradation, loose
connections and overload among the chief causes of
transformer failures in electrical distribution networks
[3,7,8]. According to ref [9], however, overload,
insulation oil degradation, thermal stress, humidity in
oil/paper and bushing defective are the major causes of
transformer problems. The term “Overload” describes a
situation whereby a transformer is subjected to voltages
and/or currents that exceed its nameplate rating such that
excess heat is generated which causes the insulation

158 American Journal of Electrical and Electronic Engineering
system to break down resulting in decreased life
expectancy of the transformer unit [10]. Transformer
overload in industries has always been traced to rapid
plant expansion without adequate capacity improvement.
Inadequate planning coupled with the presence of low
power factor and high harmonic currents generated by
inductive loads cause a transformer to become heavily
overloaded. It is widely acknowledged also that
overloaded transformers hinder future plant expansion
while the resulting excessive heat pose a potential fire
hazard [11]. Overloading leads to accelerated aging and
increased losses in transformers [12].
In this paper, the failure rate among transformers in the
Onitsha Electricity Distribution Network was investigated
and the loading on each transformer evaluated. Though
featured among vulnerable Nigerian Cities which are
susceptible to perennial flooding [13], Onitsha is an
economic hub for commerce and industry in Anambra
state; a major centre for trade between the coastal regions
and the north, as well as between eastern and western
Nigeria and across the West African region. The
metropolitan and high density areas of Onitsha include
Awada, Woliwo and 3-3. This strategic nature of the town
thus makes it imperative to carry out this research in order
to determine predominant factors that could contribute to
transformer failures and consequent sudden blackouts
thereby preventing unnecessary financial losses to both
utility and electricity customers within the network.
1.1. Overview of Transformers
Transformers perform the function of stepping up or
stepping down electrical voltages and are therefore a vital,
essential and one of the most expensive components in
any electrical network. Its cost varies between thousands
and millions of dollars depending on the design and size
of the unit [14]. Any failure of the transformer before
expiration of its designed lifespan results in unplanned outage,
production loss, unavailability of critical services and in most
cases huge financial losses to both utilities and customers.
Transformer winding insulation deteriorates as aging
sets in. Heat is a major cause of winding insulation
breakdowns. Overloading the transformer causes its
temperature to rise due to the resistive (I2
R) losses, stray,
and eddy current losses. Temperature is widely noted as
the main parameter affecting transformer insulation failure.
Hence, the heat produced through the process called
pyrolysis in the transformer as a result of loading and the
effect of ambient temperature is the important factor
affecting the life of the transformer [15]. Any increase in
temperature adversely influences the properties of the
winding insulation and the oil surrounding it. In other
words, overloading of transformer leads to increase in the
winding temperature, leading to deterioration of the
insulation material and subsequent reduction in the
transformer life span.
Routine testing and performing diagnostics can
minimize loss and down time of transformers. According
ref [16], the three categories of testing and diagnostics
required in determining transformer electrical, thermal,
and mechanical characteristics include:
(i) Performing acceptance test after installation and
commissioning of the transformer;
(ii) Predictive maintenance test during normal
operation of the transformer to ascertain that
electrical properties have not changed from design
specifications;
(iii) Failure test to identify breakdown cause of the
transformer.
Owing to limited data on transformer cycle, however,
statistical analysis only evaluates transformer failure rate
based on the operational experience. IEEE C57.125 is
considered an excellent guide for transformer failure
investigations [17].
The U.S. Department of Energy (DOE) defines
distribution transformer life as the age at which the
transformer retires from service, while the IEEE defines a
transformer failure as “The termination of the ability of a
transformer to perform its specific function” [17].
A transformer hardly fails as its insulation withstand
strength is usually higher than the normal operating or the
fault stress. But as the transformer ages, however, the
insulation withstand strength gradually reduces due to its
normal degradation and the cumulative effects from
transient events. This continues till a point where the
insulation withstand strength can no longer sustain the
high operational stress thus resulting to failure of the
transformer as illustrated in Figure 1. The impulses in the
actual stress curve indicate the sudden increased stresses
from transient events and because these events occur
randomly during transformer operation, there is often
likelihood of reduction in the insulation withstand strength
of the transformer [18]. Each step change in the insulation
withstand curve therefore indicates a slight reduction of
insulation withstand strength. It follows that as the load
increases, and/or a transient event occurs, the insulation
withstand strength reduces. The crossover point of
insulation withstand curve and operation stress curve in
Figure 1 shows the expected operation lifetime of a
transformer [18].
Every transformer is designed to withstand the expected
growing load and the system transient fault events [18].
However, given a rapidly growing load demand that is
faster than envisaged in Figure 1, or the more frequently
occurring transient event, or given that the fault stress
exceeds the insulation withstand strength, the transformer
fails before the designed age of 40. A transformer, which
failure occurred due to the significant effect from a
transient event before the designed life, is shown in Figure 2.
Figure 1. Transformer failure illustration [18]

American Journal of Electrical and Electronic Engineering 159
Figure 2. Transformer failure before expiration of designed life span [18]
Figure 3. Transformer failure beyond designed life span [18]
Operation experiences also show that given slower
increases in load and less frequent occurrences of transient
events, every transformer has insulation withstand
strength capable of sustaining the actual operation and
fault stress beyond the 40 years expected life-span [18, 19].
This is because under such condition, the insulation
strength reduces less than expected thereby making it
possible for the transformer to survive beyond the 40
years. A transformer post-lifespan failure caused by less
loaded condition is illustrated by solid curves in Figure 3.
The dash curves represent the expected operation
condition and the expected reduction of insulation
withstand strength [18].
Generally, therefore, transformer failure is determined
by individual design, loading experience, maintenance
culture and the environment in which it is installed and
operated and not on whether or not the transformer has
attained the designed life-span of 40 years [18].
1.2. Onitsha Electricity Distribution Network
Figure 4 shows Onitsha Electricity distribution network
in ETAP environment. The network consists of 45MVA;
132/33/11KV Transmission substation feeding seven (7)
Injection stations with variant capacities. Table 1 shows
the Injection Substations, their capacities and 11KV
feeders radiating from them. In addition to Table 1, there
are other feeders namely PPI/Enamel, IUNIT and Inland
11KV feeders which radiated directly from the 11KV bus
of the transmission substation located at the Onitsha
Works Centre.
Figure 4. ETAP Single line representation of Onitsha Electricity Distribution Network
Table 1. Onitsha Electricity Distribution Network Injection Substations, Capacities and Feeders
S/N Injection Substations Rating/Capacity 11KV Feeders
1. Ugwunwanosike 15 MVA, 33/11KV Toll gate, Mkpor and Ogidi
2. Army Baracks 15MVA, 33/11KV Omagba, Minaj, GRA and Army
3. Atani 2X15MVA, 33/11KV Market, Iweka, Water works, Uga, Industrial and Premier
4. GCM 7.5 MVA, 33/11KV Habour, Golden oil, GCM and Dozzy
5. Awada I 15 MVA, 33/11KV Woliwo and Nwaziki
6. Awada II 15 MVA, 33/11KV Ugwuagba and Mgbemena
7. 3-3 7.5 MVA, 33/11KV Housing and Nsugbe

2. Materials and Methods
The study employed the qualitative and quantitative
approaches to investigate transformer failures in the
network using the Electrical Transient Analysis Program
(ETAP) software 12.6 and the Statistical Package for the
Social Sciences (SPSS) 16.0. Transformer reports from
2011 to 2015 on the seven injection substations in the
Onitsha Electricity Distribution network were accessed
from the Enugu Electricity Distribution Company (EEDC).
The Injection substations are Ugwunwanosike, Army
Barracks, Atani, GCM, 3-3, Awada I and Awada II (See
Table 1). The collected data include list of all transformers
rated 200KVA - 1.5MVA connected to each of the
injection substations, monthly maximum loadings on the
injection station feeders, number of transformers units that
failed, age of the failed transformers, cause of failure, the
number of outages caused by transformers failure, the
outage duration, voltage level etc. for the study period
covering 2011-2015. Additional data were collected
through 350 copies of a well structure questionnaire which
were served on the technical staff of the electricity
distribution companies. The network data and transformer
parameters were used in power flow simulation using
ETAP 12.6 software. The simulation results obtained were
used to evaluate the loadings on the transformers within
each of the injection substations, whereas the responses to
the questionnaire were statistically analysed on the SPSS
16.0 software to determine the failure rate and actual
causes of failure of the transformer units.
To determine the transformer failure rate, the following
formula was used [4,20]:
1
1
.100%
i
i
i
i
n
N
λ =
∑
∑
(1)
Where:
λ = Failure rate per annum (p.a) in percentage
in =Number of transformers that failed in the ith
year.
iN = Number of transformers in service during the ith
year
For the calculation of failure rates a constant
transformer population of 4500 was assumed for the
investigated failure time period.
3. Results and Discussion
The calculated transformer failure rates among the
injection substations are given in Table 2. The findings
show that out of a sampled 4,500 units of transformers
that were installed and in use within the Onitsha
distribution network during the 2011-2015 period of study,
a total of 525 units of the total transformer units (See
Table 2) failed owing to several cause factors (See Table 3).
This represents an average failure rate of 11.7% and is
close to failure rate of transformers in India which
according to ref [6] is in the range 12% - 15% but much
higher than 0.5 percent obtainable in the European
countries [5].
Table 2 indicates also that the Army Barracks Injection
Substation had the highest transformer failure rate of
23.8%. This was followed by GCM and Atani Injection
Substations with 22.7% and 20.6% failure rates
respectively.
Results of the analysis presented in Table 3 shows that
Insulation Issues topped the list of failure causes in the
distribution network with 24.2%. This was followed by
Overloading and Inadequate Maintenance with 22.5% and
16.4% respectively. The study thus established the
following causes of transformer failures in the Onitsha
electricity distribution network (See Table 3 and Figure 5):
1. Moisture - The moisture category includes failures
caused by leaky pipes, leaking roofs, water entering the
tanks through leaking bushings or fittings, and confirmed
presence of moisture in the insulating oil. Moisture could
be included in the inadequate maintenance or the
insulation failure category, but it is reported separately
here [7].
2. Overloading - This category includes failure arising
from established cases of overload and includes basically
transformers that experienced a small but sustained annual
increases in load (See Table 4) that exceeded the
nameplate capacity over time such that failure occurs [7].
Excessive load on a transformer can lead to increase in
temperature and deterioration of the winding insulation,
which if unchecked, can result in transformer failure after
a sustained period of time.
3. Flood – This category of causes are due to the
flooding of substation transformers sites such that there is
breakdown of the transformer. Onitsha is among
vulnerable Nigerian Cities which are susceptible to
perennial flooding [13].
Table 2. Transformer failure rate according to Injection Substations
during 2011-2015
S/N
Injection
Substations
No. of
Transformer
Units Installed
Failures
Frequency Percentage
1. Ugwunwanosike 717 97 18.5
2. Army Barracks 574 125 23.8
3. Atani 620 108 20.6
4. GCM 659 119 22.7
5. Awada I 673 37 7.1
6. Awada II 624 15 2.9
7. 3-3 533 24 4.6
Total 4500 525 100
Table 3. Classification of failure causes, frequency and percentage
during 2011-2015
Failure Cause
Failures
Frequency Percentage
Moisture 16 3.1
Overloading 118 22.5
Flood 4 0.76
Poor workmanship/ Loose Connections 17 3.24
Overheating 6 1.14
Insulation Issues 127 24.2
Lightning surges 43 8.2
Line surges/External short circuit 74 14.1
Inadequate maintenance 86 16.4
Vandalism 14 2.7
Others 20 3.81
Total 525 100

Table 4. Transformer MVA Loadings (%) during 2011-2015
Injection Substation
Transformer Annual MVA Loadings (%)
Average MVA Loading (%)
2011 2012 2013 2014 2015
Ugwunwanosike 104.2 110.3 115.7 120.9 127.3 115.68
Army Barracks 137.5 138.2 140.4 145.7 149.8 142.32
Atani 125.6 127.4 130.3 138.1 141.6 132.6
GCM 130.9 133.5 136.8 140.4 144.1 137.14
Awada I 85 87.1 88.2 89.9 93.3 88.7
Awada II 23.5 25.7 28.9 34.6 39.5 30.44
3-3 56.1 58.2 60.1 66.4 70.2 62.2
4. Poor workmanship/ Loose connections – This category
though somewhat similar to Inadequate Maintenance
/Operation includes workmanship errors in making
electrical connections, for instance, the improper use of
dissimilar metals together or poor tightening of bolted
connections. Ordinarily, loose connections should have
been placed in the inadequate maintenance/operation
category, but this study had chosen to report it alongside
poor workmanship for purpose of emphasis.
5. Overheating -These are failure causes due to
excessive heating of the transformer which increases
transformer losses, weakens the insulation and finally
results in reduced transformer life. Due to abnormal
operation conditions such as excessive loads, transformer
windings could become overheated resulting in sudden
breakdown of the transformer unit.
6. Insulation Failures – These were the leading causes
of failure as affirmed by this study. This category excludes
those failures where there was evidence of a lightning or a
line surge. There are actually four factors that are
responsible for insulation deterioration: pyrolosis (heat),
oxidation, acidity, and moisture. But moisture is reported
separately. The average age of the transformers that failed
due to insulation Issues was 18 years. In this study,
insulation breakdown has been found to be the major
cause of transformer failures in the Onitsha electricity
distribution network followed closely by overload.
7. Lightning surges - are transformer failures due to
surges arising from a lightning strike. It is normal to first
confirm that there was a lightning strike before attributing
such transformer failure to lightning surges.
8. Line surges/External short circuit - This category of
faults includes switching surges, voltage spikes, line
faults/flashovers, and other transmission and distribution
(T&D) abnormalities resulting from poor surge protection
or inadequate coil clamping and short circuit strength.
9. Inadequate Maintenance /Operation - This category
of failure causes include accumulation of dirt, foreign matters
and oil, disconnected or improperly set controls, corrosion
and loss of coolant, which should have been promptly
identified and corrected. Early detection and correction of
abnormal conditions in and around electrical equipment often
help to prevent eventual breakdowns and loss of finances [7].
10. Vandalism - These are failures due to carting away
by vandals of vital parts of the transformer such as the
oil, copper and aluminium, etc. Electrical Infrastructure
vandalisation is a common occurrence among electricity
distribution networks in Nigeria including the Onitsha
distribution network.
11. Others –These are unclassified failure causes and
includes all transformer failure causes that are not easily
ascertainable.
The identified causes of the transformer failures in the
Onitsha electricity distribution network during the period
2011-2015 have been expressed in terms of percentages in
Figure 5.
Figure 5. Classification of failure cause by percentage during 2011-2015
0 20 40 60 80 100 120 140
Moisture
Flood
Overheating
Lightning surges
Inadequate maintenance
Others
Failure Causes in Percentages (%)
Percentage Frequency

Figure 6. MVA Loading of transformers at the sampled Injection substations in Onitsha distribution network in 2015
The computed transformer MVA loadings in each of
the injection substations studied during the period 2011-
2015 are as presented in Table 4 while Figure 6 shows the
loadings for the year 2015 only.
Observe that many of the transformers are loaded well
above 100%. It is obvious also from Figure 6 that the
loadings on the transformers increased steadily over the
years as the power consumption increased leading
eventually to the failure of the affected transformer units.
The findings of the study showed that during the period
covered by the research, about 60% i.e. four out of the
seven Injection substations in the distribution network had
various transformer units loaded beyond the nameplate ratings.
It is obvious that this high percentage of overloaded
transformers contributed significantly to the rapid deterioration
of the transformers insulating materials and therefore the
high rate of transformer failures as recorded in the study.
4. Conclusion and Recommendations
This study investigated transformer failure rates and
failure causes in the Onitsha Electricity Distribution
Network using both the Electrical Transient Analysis
Program (ETAP) software 12.6 and the Statistical Package
for the Social Sciences (SPSS) software 16.0. Network
data and transformer parameters collected over the period
2011-2015 from substations within the distribution
network were simulated on the ETAP software using the
Newton-Raphson (N-R) technique to ascertain the
transformer loadings while responses to the research
questionnaire were statistically analysed on the SPSS
software to determine the causes and rate of transformer
failures. The findings of the study show that during the
five years period of study, injection substations in the
Onitsha Electricity Distribution Network recorded an
average failure rate of 11.7 % among the installed and
operational transformers. The study revealed also that
besides Insulation Issues (24.2%), Overloading (22.5%) is
the next major cause of transformer failures in the Onitsha
Electricity Distribution Network. The study found also
that the Army Barracks Injection Substation recorded the
highest transformer failure rate of 23.8%. This is followed
by GCM and Atani Injection substations with 22.7% and
20.6% failure rates respectively. It is obvious from the
responses to the structured questionnaire by the technical
staff of the distribution company that these high failure
rates were due to inadequate number of transformers
which necessitated the overloading of the available units.
The study therefore recommends installation of additional
transformer units in order to reduce the loads on the
existing transformer units within the area. The study also
suggests strict statutory legislation against vandalism,
good workmanship, balanced loading of the transformers,
use of quality transformers as well as proactive monitoring,
inspection and maintenance program of the transformers
within the network in order to ensure improvement in
transformer life span and increased availability of
electricity supply. The outcome of this work would help
electricity utilities in providing more reliable and cost
effective services to customers.
Statement of Competing Interests
The authors declare that no conflicting interests exist.
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