=Paper=
{{Paper
|id=Vol-3058/paper69
|storemode=property
|title=Review Paper On Diagnostics Study Of Dry Transformer
|pdfUrl=https://ceur-ws.org/Vol-3058/Paper-101.pdf
|volume=Vol-3058
|authors=Pankaj Kumar,Piush Verma
}}
==Review Paper On Diagnostics Study Of Dry Transformer==
https://ceur-ws.org/Vol-3058/Paper-101.pdf
Review Paper on Diagnostics Study of Dry Transformer
Pankaj Kumar1and Piush Verma2
1
Electrical Engineering Department, National Institute of Technical Teachers Training & Research,
Chandigarh, India.
2
Electrical Engineering Department, National Institute of Technical Teachers Training & Research,
Chandigarh, India.
Abstract
Where fire safety and reliability are the major concern due to Non-flammable
characteristics, dry type transformer suited for schools, high rise buildings,
hospitals, chemical factories, steel plants, small scale industries etc. These
transformers are available indifferent ratings ranging from 25KVA-12500KVA. It
suffers issues related with temperature rise of low voltage and high voltage
windings, winding insulation failure, more losses due to overloading and in case
of insulation burnout whole windings and core limb have to be changed. In long
run these issues will impact the transformer functioning life, or may cause the
transformer failure.Various strategies and methods were utilized by numerous
analysts to limit these impacts and promising outcomes. In this paper literature
review, techniques, merits and demerits are discussed.
Keywords 1
Dry Transformer, Insulation design, Insulation safety, Temperature rising, Hot-spot
temperature.
1. Introduction
Due to rapidly increase in maximum demand, the need of improvement in grid infrastructure and
its components for the smooth operation and management with high reliability is required. The
transformer plays a significant role for the distribution of electricity from the generation to the
distribution end consumers.
Dry type transformers are the best choice, where the safety of the people and reliability are the
major concerns. Physical dimensions of these transformers are smaller in comparison with the oil
protection transformers. Likewise, they enjoy benefits like a higher mechanical strength, the chance of
establishment near the load point is more, doesn't need transformer infusion wells for oil assortment,
oil oversight adornments, firefighting frameworks, fireguard dividers and the most minimal degree of
partial discharges internally (because of its vacuum encapsulation) [3, 12]. Therefore, they are
normally utilized in populated regions, for instance, in enterprises, skyscraper private structures and
medical sectors. However, these transformers are more costly and have power and voltage limits.
Because of this malfunctioning of these equipment's greatly impact on the financially loss of the
company and social loss to the people [19]. So, their design must be specifically analyzed before
manufacturing as per the specific usage needs, meeting the limit sets by international standards [11].
Designing of transformer is very crucial in order to reduce the losses and increased its life span [16].
International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07, 2021,
NITTTR Chandigarh, India
EMAIL: pankaj.elect2019@nitttrchd.ac.in. (A. 1); piush@nitttrchd.ac.in.(A. 2)
ORCID:0000-0003-3713-6123. (A. 1);
©2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
While designing of dry transformers, main aim is to decrease the power losses occurs internally
[2]. Iron core and windings are the main source of internal losses and varies with the resistance of
windings, interior surface temperature, current flowing through windings, voltage across the load,
connected load and type of aluminum (quality) used [23]. Ultimately, these losses influence the entire
machine's temperature system, which straightforwardly diminishes the effectiveness and the
transformer's lifespan. Due to this the thermal stress is a significant reason for its failure, as it causes
the weakening of the protecting material [18]. However, predicting the interior temperature (or an
increase in temperature) is a big challenge while designing the transformer design.
In this paper a brief survey is done on the dry type transformer parameters such as temperature
rise, construction design, losses due to overloading, insulation breakdown due to excessive heat.
2. Literature Review on Different Techniques Used on Dry Transformer
A brief summary is done below which describes the monitoring techniques, monitoring
parameters and their advantages and disadvantages.
Table 1
Transformer health assessment methodologies comparison.
Monitoring Techniques Year Monitoring Merits Demerits
Parameters
Soft Computing 2010 -Harmonics -For any load variation -Large
Techniques using ANFIS problems due to temperature prediction can be data
by Ebenezer et. al [1] Non- Linear done. required
Loads -More accurate results for
-Winding compare to other techniques. Training
Temperature -Harmonics reduction can be for
done by using filters. optimum
results.
Transformer 2008 -Temperature -Real time monitoring and -To
Temperature Controller -Transformer controlling improve
Using HCI system by Long Health life -Low cost precision
et. al [2] -High reliability further
-User friendly HCI system research
needs to
be done.
Automatic Partial 2002 -Voltage -Automatic -For
Discharges -Apparent -Damping or distortion has no improvin
diagnosis using multi charge effect on results. g
sensor system by Werle -Small physical dimension accuracy
et. al [3] -High accuracy and
quality of
PD tests,
a
calibratio
n impulse
has to be
injected
in parallel
to the
specimen
as well as
to the
winding
ends.
-Sensors
needed.
Analysis of Temperature 2012 -Temperature -Hottest spot-on LV windings -Need to
field based on Fluid-solid Field lie at 1/3 of the top. add the
Coupling by Ding et. al [4] distribution -Makes online monitoring hottest
easier by defining the location spot on
of the sensors. HV
-Reduce maintenance cost windings.
-Extend life span -Effect of
temperat
ure on
the
insulation
was not
discussed
.
Condition monitoring 2008 -Voltage -Monitor all size and rating of -To
using LabVIEW with Fuzzy -Current oil and dry type transformer. enhance
Logic Controller -Fuzzy Logic -User friendly interface for the
Simulation Panel by Controller monitoring through LabVIEW. accuracy
Muhamad et. al [5] -DGA -Identifies fault types broader
-Interpretation -Predict the transformer testing
of Fault health condition. needs to
behavior be done.
Comprehensive study 2018 -Invasive Weed -In short time, give near -Not
with Swarm-based Optimization optimal results. guarante
metaheuristic (IWO) -Time saving e for the
optimization methods by -Firefly -Reduce weight of Dry best
Aksu et. al [6] Algorithm transformer results.
-Particle Swarm -Increase efficiency -Works
Optimization specificall
(PSO) y to a
-Current density particular
-Iron section problem
compatibility structure.
factor
Digital measurement 2011 -Cold Resistance -Low-cost digital -Not
system for temperature of LV and HV measurement system applicabl
rise by Srinivasan et. al -Hot Resistance -Accurate measurement e to all
[7] of LV and HV -Low errors measure
-Temperature -Data Acquisition System not ment
rise required. systems
i.e proper
algorithm
required
in the
program
ming
environm
ent.
-High
speed
DSP
required
Design of temperature 2020 -Temperature -Simple structure of few -Fast
controller based on IoT -Real time devices communi
by Leng et. al [8] monitoring -Make remote centralized cation
monitoring medium
-Reduce costs, labour required.
-Improves efficiency -
management Interfere
-Data can record and view nce or
easily through cloud data lost
-Integrate multiple during
temperature controllers. long
-Monitor multiple Dry type remote
transformers. location
-Low power consumption monitori
ng
-Data is
not
secure on
cloud
Monitoring using 2001 -Temperature -Prevent secondary failures -Every
Fiber optictechnology by rise of windings and cost time PD
Gockenbach et. al [9] -Failure of -Offline analysis gives the detection
secondary indication of short circuits system is
windings -Repair costs, maintenance not
-Life Expectancy reduced. economic
al.
3-dimensional Finite 2012 -Temperature -Reliable -Does not
Element analysis on Fluid rise -Very low error work on
Thermal Field by Ning et. -Age -Simulation analysis helps in the low
al [10] -Temperature minimizing temperature rise. voltage
field winding
temperat
ure rise.
Online condition 2016 -Leakage flux -Fault localization -
monitoring and diagnosis
techniques by during inter-turn -Helped in detection of Insulation
Subramaniam et. al [11] fault. potential monitoring constrain
parameter. ts
-Fault
detection
by
terminal
current
analysis is
limited to
fault
identifica
tion and
cannot
use for
fault
localizati
on.
Different temperature 2011 -Infrared, -Reliable -The
Sensing and control Thermal - Provides a more efficient accuracy
technology by Feng et. al resistance, Fiber solution to difficult problems depends
[12] optic - It's simple to increase or majorly
temperature change the system's on
sensing performance. selected
technique -Back Propagation Neural paramete
- Transformer Network (BP) increase rs.
windings response speed. -Need to
-Current train with
-Voltage large
-Power factor number
of
samples
for
accurate
results.
-
Expensiv
e sensors
used
which
increased
the
overall
system
cost
Mathematically analysis 2007 -Temperature -Reduce heating by variation -Lab
of temperature distribution in of air duct width with environm
distribution using Finite windings temperature rise. ental
Difference Method by -Eddy current -Eddy current reduces by condition
Rahimpour et. al [13] losses. modification of cross-section s such as
-Radiation from of conductors. air
the outer - The external cooling medium displace
surface lowers the amount of ment
-Heat transfer radiation emitted from the creates
from top and outside surface. inaccurac
bottom surface y.
-Error in
temperat
ure
reading
due to
magnetic
field
impact
on
infrared
periscope
.
-Errors
due to
computat
ional
operation
s while
rounding
and
estimatio
n
3. Miscellaneous Techniques
3.1 Study of the Internal Winding Temperature Distribution using Simulation
Models.
Basic heat transfer theory is used to analyze the internal structure of dry type transformer and
simulate via Analysis of Systems (ANSYS) simulation software [14]. The highest temperature location
separately for iron core, low voltage winding and high voltage winding was determined. For analyzing
the temperature field distribution of the dry type transformer, a 2-D model of dry type transformer is used
to locate the positions of the hottest temperature of the iron core, HV winding and LV winding. Results
of ANSYS simulation used as a reference and compared with the experimental results.
The prognosis system is based on changing the ambient temperature changed the thermal modelling
of a cast-resin dry transformer. The developed system was utilized to keep the transformer aging rate
within a specific value during the transformer operation [15]. The short-circuit withstand test was
performed on the dry type transformer unit of 72.5kV. Schering Bridge with a reproducibility of 0.1%
was used to measure the short circuit inductance of transformer [16]. A 3D finite volume based
Computational Fluid Dynamics (CFD) model produced in the ANSYS CFX software and a Finite
Element Modeling (FEM) based model that employs an experimental analytical method for vertical air-
cooled ducts. [17].
For this 800 kVA transformer, the CFX result are accurate, but the proposed FEM model was accurate
enough for practical design applications [17]. Form these two models, the losses increased, due to the
temperature decreases as their air ducts width decreases, and increases as the width of the air duct
increased to a certain limit.
3.2 Advance Methods used in Dry Transformers Diagnostics
Dry type transformers are designed in a certain way which involves the huge number of factors in
estimations and the relationships between these factors. The concept of multi-insulation design and the
solid insulation system bolstered by precision design and defect-free manufacturing is discussed in [18].
The safety and the dependability of the dry-type transformer are considerably improved, the logical
inconsistency of the insulation protection safety and energy-saving design are handled, and the dry-type
transformer's performance is enhanced further. The manufacturing process is straightforward and
dependable, the prior process includes complex pouring cycle which was totally kept away, and the
manufacturing effectiveness and item quality are ensured. The finite element analysis is an efficient tool
for determining the temperatures of a dry-type transformer under load.
In [19] another strategy of a thermal simulation utilizing finite element theory hypothesis is being
proposed. The heat diffusion equation was utilized, with the accompanying limit conditions: convection
and radiation conditions, qualities of the materials utilized, the estimation information and the elements
of the transformer. The position of the hotspot temperature, cycle of thermogenesis and thermolysis of
transformer was investigated to anticipate the insulation life loss of dry-type transformer [20]. Artificial
Neural Network (ANN) assists with seeing the subtleties that are practically difficult to be seen utilizing
traditional tools, inferable from the intricacy in mathematical simulations. The use of artificial neural
networks in modelling influences various design factors on the internal temperature of dry type
transformers. The ANN results are contrasted with 300 transformers were put through their paces in a
real-world test. [21]. The outcomes of ANNs were shown to be capable of modelling, with incredible
exactness, the connection between different design parameters, losses and internal temperature increase
in dry‐type transformers. Temperature rising forecast of transformers (dry-type) by coupled temperature-
dependent power loss and thermal fluid filed methodis one of the critical thoughts that utilized
mathematical tests [22].
In Electrical Partial Discharge (PD) localization method of a dry type power transformer an adjustment
signal was infused through a plate sensor set along the windings on the outside surface of the transformer
coil in various positions [23]. After that PD signals are recorded from both coil terminal sites utilizing
digitizer. Measured PD signal during test assessed and contrasted with calculated transfer function. The
depicted strategy empowers not just localization of the PD source additionally assurance of the apparent
charge. This assessment can be more exact than ordinary PD measurements by utilization of quadruple
and coupling capacitor adjustment and can be utilized during manufacturing process as well as diagnostic
in field. In thermal model for foil winding, the temperature distributions were dictated by the finite
element method (FEM). FEM limits the non-consistency of the heat fluxes transitions in the foil
windings because of induced currents, diverse convection coefficients and shifting air temperature along
the vertical height of the foil winding [24].
4. Hotspot Temperature Prediction of Dry-Type Transformers using IoT
The internet of things (IoT) is an active scientific study field in identifying research issues
connected with its application in a number of industries, including consumer convenience, smart energy,
and energy conservation, as well as IoT organizations, as an emerging technology. Sensors are important
IoT components that send data in the form of a data stream for further processing. It can be used to store
information or communicate with each other across the globe of dry transformer. In [25] systematic
review of integration of semantics into sensor data for the IoT was discussed and found the
interoperability of diverse connected digital resources a major issue. The proposed techniques employed
the use of sensor data which is always changing and real-time semantic annotation is required to store the
data in data store as static data and subsequently merged with semantics. A Particle Filter Support Vector
Regression (SVR) technique was used in hotspot temperature prediction of dry transformer in [26]. The
particle filter may dynamically track fresh data and provide the system with the best SVR parameters.
IoT can improve real-time approaches for integrating and interpreting semantic annotations in dry
transformer future work.
5. Conclusion
From the literature survey, it is observed that various methods were proposed by different researchers
in order to eliminate the different types of failures in Dry Transformer. However, most of the researchers
worked on simulation-based models and not much work is done with the real-world datasets. Various
techniques proposed by researchers, to minimize the temperature rise and hot spot temperature prediction
we find that there is scope of improvements in these methods to detect the fault at early stage using
advanced real-time methods for integrating and interpreting semantic annotations including Artificial
Intelligence (AI) with IoT real time monitoring.
6. Acknowledgement
The authors are thankful to NITTTR (National Institute of Technical Teachers Training and
Research), Chandigarh for providing necessary support and infrastructure for the work.
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