Development of switchgear life cycle cost analysis software

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Development of switchgear life cycle cost analysis software
Article · June 2010
DOI: 10.1109/PEOCO.2010.5559217
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The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
Development of Switchgear
Life Cycle Cost Analysis Software
Y. L. Loo, C. T. Yaw, A. H. Hashim, A.M.Mohamad, M.K. Faizah, A. Noormala
replaced by the use of advanced analytical tools and
techniques in an improved development process.
The development of non-oil, low maintenance, and high
performance distribution switchgear has created operational
advantages in its application. This is true of both ground
mounted and pole mounted equipment. In addition to
switching life and performance benefits the availability of
new technologies has allowed implementation of remote
operation of equipment and upgrading of existing
switchboards by the use of compatible replacement non-oil
circuit breakers. In addition to the use of diagnostics as a
development tool the use of diagnostic based condition
monitoring of switchgear is now becoming established.
The trend towards predictive rather than preventive
maintenance as switchgear becomes even more reliable is
increasing the importance of condition monitoring two
techniques. However, outages caused by switchgear cannot
be eliminated and there is a need to optimize the cost of
operating these assets.
As a result, the Life Cycle Cost Analysis (LCCA)
approach is proposed on switchgears which can help in
decision making on switchgear during its operation [2]. An
analytic equation for the LCCA was derived which can
minimized the costs for a certain economic life time were
calculated. With the aid of these design criteria, it is possible
to reduce the costs significantly.
Abstract--In today’s demanding business environment,
determining the Life Cycle Cost Analysis (LCCA) is important
in order for utilities to make the right decision when it embarks
on any form of assets retrofit or refurbishment or replacement.
The LCCA is a process of evaluating the economic performance
of an asset over its entire life. Sometimes known as “whole cost
accounting” or “total cost of ownership”, LCCA balances
initial monetary investment with the long-term expense of
owning and operating the assets. This research has developed
an LCCA model for distribution switchgears through
identification, analysis and development of switchgear life cycle
cost profiles, data and information sources from available assetbased and financial system in TNBD.
Index Terms--LCCA, Switchgears, Distribution System
I. INTRODUCTION
C
OMMERCIAL pressures are increasing on TNB to
maximize their plant utilization, improve network
performance and reduce their plant lifetime costs. All
this is happening at a time when the average age of
switchgear on many parts of the network is quite high at
about 30 to 40 years and is increasing [1]. Also of
significant is the fact that, in TNB, the available experienced
resource to operate and maintain the network and manage
the asset replacement is becoming depleted.
Significant developments have taken place in the
design of modem Transmission and Distribution Switchgear
resulting in simpler and inherently more reliable equipment.
The advances which have been achieved in switchgear
design are being accelerated not only by continuing
technological improvements but also by the ways in which
the product development process is undertaken. The
traditionally applied, purely empirical, approach is being
II. MODELLING OF LCCA
A. Formula
The first step in the completion of the LCCA of this
research is to define all the initial investment costs. Initial
investment costs are costs that will be incurred prior to the
switchgears. The second step is to define all the future
operation costs of the alternative. The operation costs are
annual costs, excluding maintenance and repair costs,
involved in the operation of the switchgears. All operation
costs are to be discounted to their present value prior to
addition to the total cost. The third step is the repair cost of
the switchgears. Repair costs are unanticipated expenditures
that are required to prolong the life of switchgear without
replacing it. The final step in the completion of LCCA is the
scrapping cost. Scrapping costs are anticipated
expenditures to major building system components that are
required to maintain the operation of switchgear. Once all
costs have been established and discounted to their present
value, the costs can be summed to generate the total cost as
follows:
This work was supported by Tenaga Nasional Berhad (TNB) Malaysia
and Tenaga National Berhad Research (TNBR) Malaysia.
Y. L. Loo is with Institute of Energy Policy And Research, Universiti
Tenaga Nasional, Km7, Jln Kajang-Puchong, 43009 Kajang, Selangor,
Malaysia (email: [email protected])
C. T. Yaw is with Institute of Energy Policy And Research, Universiti
Tenaga Nasional, Km7, Jln Kajang-Puchong, 43009 Kajang, Selangor,
Malaysia (email: [email protected])
A. H. Hashim is with Institute of Energy Policy And Research,
Universiti Tenaga Nasional, Km7, Jln Kajang-Puchong, 43009 Kajang,
Selangor, Malaysia (email: [email protected])
A.M.Mohamad is with Tenaga Nasional Berhad Research, No. 1,
Lorong Air Hitam Kawasan Institusi Penyelidikan 43000 Kajang Selangor,
Malaysia (email: [email protected])
M.K. Faizah, is with College of Business and Administration, Universiti
Tenaga Nasional, Sultan Haji Ahmad Shah Campus: 26700 Bandar
Muadzam Shah, Pahang, Malaysia (email: [email protected])
A. Noormala is with College of Business and Administration, Universiti
Tenaga Nasional, Sultan Haji Ahmad Shah Campus: 26700 Bandar
Muadzam Shah, Pahang, Malaysia (email: [email protected])
978-1-4244-7128-7/10/$26.00 ©2010 IEEE
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The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
renewal or rehabilitation, financial (e.g. cost of finance) and
replacement or disposal.
Life cycle cost significantly differ from the traditional
management accounting decision making where previously,
the focus has been on the initial capital costs of creation or
acquisition, and organizations may have failed to take
account of the longer-term costs of an asset. Assets
ownership incur more costs after an asset has been
constructed or acquired, such as maintenance, operation,
disposal thus become an important consideration in
decision-making. This allows an analysis of business
function interrelationships whereby low development costs
may lead to high maintenance or customer service costs in
the future.
Maintenance expenditure can account for many times the
initial cost of asset such as switchgear. Although switchgear
may be constructed with a design life of 20 years, in reality
it will possibly perform well beyond this design life. For
assets like these a balanced view between maintenance
strategies and renewal/rehabilitation is required. The
appropriateness of the maintenance strategy must be
questioned; the point of intervention for renewal must be
challenged. The process requires proactive assessment
which must be based on the performance expected of the
switchgear, the 44 consequences and probabilities of failures
occurring, and the level of expenditure in maintenance to
keep the service available and to avert disaster.
By using whole-life costs avoids issues with decisions
being made based on the short-term costs of purchase. In
this way, the whole-life costs and benefits of switchgear are
considered and converted using discount rates into presentvalue costs and benefits. Thus optimal decision making can
be undertaken in the context of whether existing switchgear
should continue to be maintained and used or new
switchgear should replace it. It is imperative that all areas of
corporate activity need to obtain value for money especially
in today’s ever increasing competitive business environment
and dwindling resources.
Fig. 1 Total Cost of Life Cycle Cost Analysis
Following the model above, a formula for LCCA is
generated for the LC calculation as shown below;
CSG = CI + CO ± CR
Where;
CSG = Cost of switchgear;
CI = Initial Cost;
CO = Operation Costs;
CR = Removal Costs.
Using this formula, NPV of switchgears which is
represented by CSG, is being calculated. The initial cost of
the switchgear is the purchase price, transportation,
installation, import duty and other charges involved in the
initial investment of the switchgear. The operation cost of
switchgear is calculated by adding the cost of maintenance
and failure as shown below;
CO = CM+ CF
Where;
CM = maintenance costs;
CF = failure costs
Maintenance costs is calculated by adding up inspection
and routine costs, as well as preventive costs done on the
switchgear as shown below;
+
Where;
CI =inspection, routine and condition assessment costs;
CPr = preventive and testing costs.
Failure cost will share the same formula as preventive cost
formula, for it is not a scheduled events like inspection,
routine and condition assessment maintenance. Removal or
disposal cost is the costs involved in replacement, such as
cost to dispatch and send for scrapping.
The development of life cycle costing has its origin in the
normative neoclassical economic theory which states that
firms seek to maximize profits by always operating with full
knowledge [3]. According to the theory, decision makers
must have consistent preferences; they also have to know
their preferences as well as the available alternatives [4].
Thus they must have access to the information about the
consequences of selecting each alternative and be able to
combine this information with the expected utility, which in
turn discounts or weighs outcomes by the probability of their
occurrence [3]. Therefore the total costs of assets ownership
can be optimized by providing the total life cycle cost of an
asset through quantifying and identifying all significant net
expenditure arising during the ownership of an asset. Life
cycle cost commonly referred to as "cradle to grave" or
"womb to tomb" costs. Typical areas of expenditure which
are included in calculating the whole-life cost are planning,
design, construction or acquisition, operations, maintenance,
B. Database
The database attached to the software is a relational
database formed on the platform of Microsoft Excel to
ensure the ease of access and updating the data, and
robustness of the database, where the data could be imported
in every computer that has the software and Microsoft Office
applications.
The users are required to import or key in data into the
software database, if the software database does not have the
data that the user would like to analyze. The data that the
software captures consist of two categories, which are
general information of the switchgear and the costs incurred
for the switchgear. Cost data, which consists of the initial
cost, installation cost, maintenance costs and scrapping cost
(if there is any) of the switchgear are the main data that
would contribute to the analysis of the life-cycle cost of the
switchgear. General information of the switchgear would
consist of functional location of the switchgear, which is
crucial, for it is the primary key for that indicates nonredundancy switchgear data inside the database. Then, the
substation name of the functional location is to indicate that
the switchgear is stationed in the particular substation.
Information of switchgear brand, configuration and age are
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The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
the primary input for the further analysis to be done, that
would later induce the correlation of life-cycle cost with the
brand, configuration and age of the switchgear. Historical
record is also one of the primary inputs, where the
switchgear is indicated whether it had been retrofitted,
upgraded or refurbished or failed, which will contribute to
the analysis result. Secondary data inputs, which are
substation type, domestic, commercial and industrial load,
loading of the feeder, insulation medium, voltage of the
switchgear, that would enable user to choose the different
categories of switchgear that the user wanted to analyze.
Data Type
General
Switchgear
Data
Data
Initial Cost,
Installation Cost,
Maintenance
Costs, Scrapping
Costs.
Functional
Location and
Substation Name
Brand
Age
Configuration
Historical
Data
History (Retrofit,
Refurbishment,
Minor
Replacement)
Failure
General
Switchgear
Data
Manufacture
Year and Model
No
Substation Type
Loading Type :
Domestic,
Commercial and
Industrial
Loading (MD)
Insulation
medium
Voltage
Significance
done on particular switchgear
voltage.
C. Software
The software is developed according to the software
development methodology of iterative development or
evolutionary model [5], where rapid prototyping strategy is
used. The software is developed in prototypes and released
to client to gain and confirm user requirements for a few
times, until the client is satisfied with the software prototype,
before it is being delivered in a package. This software
development methodology is chosen for the client is unsure
of the full requirement they want for the software and this
methodology enable the iterative additional requirement to
be developed in the software.
The database attached to the software is a relational
database formed on the platform of Microsoft Excel to
ensure the ease of access and updating the data, and
robustness of the database, where the data could be imported
in every computer that has the software and Microsoft Office
applications.
The programs involved for the programming of the
software are Microsoft Visual Basic 2008 and Crystal
Report 2008 Basics. These programs are chosen for the
object-oriented development of the software, for the
software includes some graphical elements and the
generation or simulation of analysis is by Crystal Report.
The programming language used to code the software is
Microsoft VB.Net, an object-oriented programming
language that is easier to be understood for the purpose of
software reuse, if there is any.
TABLE 1
Categorization and Significance of Data Input For LCCA Switchgear
Software
Data Type
Costs Data
Data
Significance
Main data that will be implied in
the LCCA model for calculation
and analysis.
To ensure non-redundancy of data
in database.
To enable user to know the
location of the switchgear, in terms
of state and area.
To be implied in analysis of least
cost and fault switchgear brand for
procurement and asset
management.
For the correlation analysis,
between duration of service with
the life-cycle cost of switchgear, to
visualize the brief range of age,
when procurement is necessary.
To be implied in analysis of least
cost and fault switchgear
configuration, and combination of
configuration and brand for
procurement and asset
management.
To know whether the switchgear’s
high life-cycle cost is induced by
the preventive maintenances of
retrofit, refurbishment and minor
replacement because of faultiness.
To know whether the switchgear
had failed. This might induce the
usual failure age of switchgears in
the analysis population.
For user to have the idea of which
batch of manufactured switchgear
is faulty that would induce the
same manufactured year of
switchgears might share the same
faults.
For filter before the analysis,
where users could see LCCA being
done on particular substation type.
For the correlation finding,
between different consumers load
to the faults of switchgear.
D. Analysis
The analysis of the life-cycle cost of switchgears will be
simulated into two forms, which are tabular and graphical
form. The tabular form tabulates the switchgears into
different brand and configuration with the life-cycle costs.
This analysis might suggest to the users the different
reliability of switchgear brand and configuration
combinations, by the maximum total years of service and
costs incurred throughout the service. This analysis also
suggests decision of purchasing the least cost switchgear
combination of brand and configuration.
The analysis in graphical form enables users to visualize
the “faulty point”, a point of time where the switchgears start
to acquire more cost because of failures or breakdowns. This
might give a general idea to procurement users that the
switchgear in the historical data, seems to fail at the age
where the faulty point is, and procurement of new
switchgear purchase could be done before that point, to
prevent additional cost spent on switchgear that is failing.
Furthermore, from the graphical visualizations, users are
able to draw a conclusion of unreliable switchgear brands by
looking at which switchgear might cost more because of the
preventive maintenances done for the faulty switchgears, and
yet, still fail the most in the population of switchgears being
analyzed.
For the correlation finding
between loading that might vary
the life-cycle cost of switchgear.
Users could compare the failure
rates between different insulation
medium used by different
switchgears.
For filter before the analysis,
where users could see LCCA being
TABLE 2
Summary of Analysis of Switchgear LCCA Software
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The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
Analysis
Forms
Tabular
Graphical
Analysis
Outcome
Brand and
Configuration
versus Age
Shows life-cycle costs of different
combination of switchgear brand and
configuration, which allows users to
compare and visualize the combination
that cost least to the company.
Users could visualize the trend of
costs, whether they are dropping or
rising according to time.
This analysis enables the user to
visualize the trend of declining or
mounting costs at certain range of age.
The analysis also depicts the
comparison of life-cycle cost of
switchgears of different brand.
This analysis enables user to visualize
the increasing or constant percentage
of failed switchgear at certain range of
age.
The suggestion of procurement of
switchgear at a range of age is depicted
at this analysis.
This analysis describes the percentage
of failed switchgear for switchgear
brands, in the population of
switchgears chosen for the analysis.
Comparison of switchgear reliability
from different brands is depicted in
this analysis.
This analysis visualizes costs invested
in the different maintenances for
different switchgear brands.
This analysis is inter-related with the
above analysis, to depict the reasons of
low failed switchgear percentage,
whether is because of more costs
invested in preventive maintenances or
it is because of the high reliability of
switchgear.
This analysis visualizes the percentage
of failed switchgear for different
configurations.
This analysis suggests in depth
decision of consideration for
switchgear configuration, after
depicting the reliability of different
brands.
LCCA versus
Age and
LCCA versus
Age (Trend
View)
Switchgear
Failure versus
Time
Switchgear
Failure versus
Brand
Maintenance
Cost Details
Switchgear
Failure versus
Configuration
are based on a fictitious scenario, to show how the analyses
are being done by the software and why the analyses were in
the respective forms. For personal information purposes,
fictitious names were used to illustrate the different brands
of switchgear.
TABLE 3
Comparison Between Base Case And Generated Scenario Data
Properties
Failures
Base Case
No failure
Generated Scenario
Major failures begin from 16
years and above
Maintenance
Cost
Totally failed
switchgear
Constant
Increase drastically from 16
years and above
Totally failed at the age of 25
No totally failed
switchgear
A. Life Cycle Cost Analysis For Different Brand And
Configuration In Proportion To Switchgear Age
This analysis is shown to illustrate the different life-cycle
cost that the combination of brand and configuration has,
which will be shown within ranges of age. This will enable
user to compare the actual amount of life-cycle costs
between brands and configurations, and conclude the least
and most life-cycle cost switchgear with certain combination
of brand and configuration.
TABLE 4
Screenshot Of Analysis Table Showing The Average Life Cycle Cost For
Different Configurations For Four Switchgear Brands
III. RESULTS
LCCA had been done on the original data from Ipoh,
Perak, Malaysia. However, this base case data did not have
the complete costs data, where all the maintenances data
were just an assumption, that maintenance schedule is being
followed and the costs are the same. Real data were all the
general data, initial and installation costs. Historical data,
such as whether switchgear failed before and had been
retrofitted, refurbished, upgraded or replaced were not found
as well. Thus, the switchgears were assumed that there were
no failures happened and no extra costs spent for corrective
maintenances. Therefore, for the whole data population,
there were no sudden increases of cost in the LCCA because
of the assumptions. From this, it was found that the data did
not produce a comprehensive analysis which could show the
abilities of the software.
Thus, a fictitious scenario is created in a new database, to
allow illustration of the full capabilities of the switchgear
LCCA software. This also allows a demonstration of how
the software can cope with the different data inputs. The
software outputs in following tabular and graphical sections
This analysis will help user to determine, which
configuration and brand for future procurement, by
comparing LCC of different switchgear brand and
configurations.
In this case, generally SG5 seemed to be more feasible for
future procurement (see A), as the overall average of LCC is
less, compared to the other brands. However, for 2S+2
configuration switchgears, SG4 might look to have better
LCC than SG5 2S+2 (see B). Therefore, this analysis can be
taken as an overview of the general analysis on the data.
B. Life Cycle Cost Analysis On Different Switchgear
Brands Over Time
This analysis is to illustrate Table 4 in a line graph, to
clearly portray the trend of life-cycle cost of switchgear in
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The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
proportion with time. This analysis is to give an illustration
of comparison of various LCC of switchgears from different
brands in a clearer picture. The analysis also gives an
illustration of trend of increasing life-cycle costs. The range
of age where the increase of cost happens might suggest the
future procurement can be done for switchgears to avoid
investing on switchgear that will costs more in future.
factor of failure in the analysis before any decision is made
upon procurement and asset management. The record of
failed switchgear is important, for it will reflect the range of
years where switchgear will usually fail, where switchgears
would cost more.
Fig. 4 Screenshot of analysis pie showing the trend of switchgear failures
in graphical visualization.
Referring to Fig. 4, the rise of percentage of failed
switchgear is obvious at the age range of 16-20 years old.
This pie chart interrelates with the analysis done in Section
B. The analysis in Section B, is unable to show clearly the
age range of future replacement as cost is the only factor
being considered. The real cause of LCC increase was yet to
know, which will be further discussed in Section B. From
this graph, it is indicated that failure which rise drastically
for switchgears age 16 years and above, might contribute to
the drastic rise of the switchgears’ LCC. The analysis
suggests that replacement age of switchgear should be 16
years and above. This is to prevent additional costs of being
spent on switchgears that would fail more at the age range of
16 years and above.
Fig. 2 Screenshot of analysis graph showing summary of the average life
cycle cost for different switchgear brands in graphical visualization.
Referring to Fig. 2, the overview of switchgear brand are
viewed with their average LCC. From here, the graph briefly
tells user that SG1 did not rise rapidly in its LCC. This
information suggests the brand to be the higher future
purchase brand consideration, as well as SG5, which line is
the lowest compared to SG4, SG2 and SG3. This illustration
contributes to the user, that SG5 is more feasible as future
procurement.
From the rise of most of the switchgears’ LCC, as shown
in the graph at the range of age of 11-15 years, it is
suggested that the switchgear might have a higher failure
rate when it reaches 11 years and beyond. This is shown in a
clearer illustration in Fig. 3, where the trend of average
LCCA is being viewed for every brand with separated lines.
From the figure, it is suggested that the switchgear
replacement age, should be when it is 11-15 years in use and
above. This is to avoid more cost to be spent on switchgears
that will cost even more in the future.
D. Analysis Of Failed Switchgear In Accordance Of
Brands
Complementing analysis in Section C, this analysis focused
on determining which switchgear brand is more reliable.
This is done by looking at failures based on brands. The pie
chart shows the switchgear brands which tend fail and need
to be replaced. Thus, with this analysis, the result can help
the utility to select the brand that would be most reliable.
Fig. 3 Screenshot of analysis graph showing the trend of the average life
cycle cost for different switchgear brands in graphical visualization.
C. Analysis Of Switchgear Failure In Proportion To Time
Fig. 5 Screenshot of analysis pie showing the number of failed switchgears
in different brand of switchgears in graphical visualization.
This analysis illustrates the percentage of failed switchgears
in proportion to the years of service. This will include the
From Fig. 5, user is able to know which brand might have
more failed switchgears in the sampling population. From
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The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
brand for future procurement, the software provides another
analysis to suggest switchgear configurations to be taken
into consideration.
this example, SG1 is shown to have no failed switchgear at
all while SG5 have the most failed switchgear. This analysis
is however, affected very much by the sampling size. The
amount of sample for SG1 might be just one, and this one
switchgear did not fail, while SG5, might have 100 samples
but had failed 30 switchgears which contributes to the
approximate 30% failures. Assuming that the sample size is
non-biased, SG1 might be the more feasible option
compared to SG4. While the consideration of failure is
taken, the consideration of factors causing the low failure in
switchgears also needs to be considered. This is because,
high preventive maintenance might contribute to low
percentage of failures. Therefore, maintenance cost
distributions on the different brands of switchgear need to be
further discussed in the analysis in Section E.
Fig. 7 Analysis pie showing percentage of failed switchgears with
configurations using graphical visualization.
E. Analysis On Maintenance Cost Distributions On
Different Switchgear Brands
Referring to Fig. 7, user is able to know which of the
configuration have more failure throughout the service.
However, the numbers of samples have to be brought into
consideration, there might be insufficient data samples in the
database. For example, the 100% failed switchgear
configuration of 3S might just have 1 sample, and that
sample just happened to have failed. This is biased if
compared to 5S+1 configuration, which might also have 1
sample, and that sample happened to did not fail. Assuming
the sample size of the example is not biased, the result
shows that 4S+1 and 5S+1 will be the most feasible
configuration, followed by 2S+2 and 2S+1.
As a conclusion, the software enables the users to
compare the costs between switchgear brands and
configurations which would conclude the least cost
switchgear brand and configuration. Then, the software led
the users to see the range of age were the increases of costs
happened which would bring the conclusion of the age range
of procurement. The software took failure into the account,
to validate whether the cost increase was caused by
switchgear failures or maintenance that prevents switchgear
failures. This helps the users to conclude the brands of
switchgear that needs more maintenance to prevent failures
and the ones that did not. The software then analyzes
different configurations, which contributes to the decision of
switchgear configuration for the future procurement.
In this analysis, users could compare the maintenance cost
distributions on different switchgear brands. From this
analysis, user might draw conclusions that some of the low
percentage of failed switchgear brands might be because of
the higher volumes of preventive maintenance actions. This
will also cause increase in the LCC of switchgear.
Fig. 6 Screenshot of analysis pie showing the maintenance cost details for
the switchgears in graphical visualization.
From the figure above, user is able to know what might
have caused the low percentage of switchgears failures in
some of the brands in the section D analysis. Among all,
SG4 switchgears had the most preventive maintenance done
on it compared to others. This suggested the cause for low
failed switchgears as shown in Fig. 4. However, SG6, with
just 29.39% of LCC used for preventive maintenance,
maintained to have just 2% more failed than SG4. This
might suggest that although SG4 might have less failed
switchgears, but more maintenance costs were incurred,
compared to SG6. This analysis helps the user see the brands
of switchgear that might need more preventive maintenance
to prevent failure and those that do not need. From this
analysis, SG6 might be suggested to be the future brand
procurement, for it does not have to have a high number of
preventive maintenance to keep the it from failing.
IV. CONCLUSION
From this research it is found that the use of life cycle
costing will optimize value for money in the physical
ownership of switchgears, by taking into consideration all
the cost factors relating to the switchgear during its
operational life. It is important for the management to realize
the impact of life cycle costs so that effective action can be
subsequently being taken to control them for saving
purposes. It is found from the LCCA that this analysis helps
TNB to save from maintaining a switchgear that will fail
more, as the practice of maintaining until total breakdown is
being adopted.
In this research, the LCCA generated would help users to
visualize the costs incurred for the switchgears parallel to
the duration of their services. From this, users could
compare the LCC of different brands and configurations,
F. Analysis Of Failed Switchgears In Accordance Of
Configuration
This analysis is a continuation of analysis from result of
analysis in Section E. After the suggestion of switchgear
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The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
Engineering, UNITEN. His work fields are
power distribution system and energy
business.
where the users could know which brands and configurations
operates in low LCC. The analysis also enables users to the
study of reliability between brands and configurations by
generation of switchgear failures comparison between
brands and configurations. This contributed to the decision
of replacing more reliable switchgear. The analysis also
enables users to identify the trend of switchgear failures and
LCC increase. This contributed to the decision replacement
age range of switchgears to save from maintaining
switchgears that will fail or cost more in the future.
The results of LCCA are highly dependent on the input
variables. Many times these inputs are only best estimates.
For this research, fictitious data need to be used, to generate
analysis that will vary and to enable comparison of LCC to
be done. This is as the result of real data being uniform and
following standard costs, which made the analysis did not
vary much for effective comparison. Therefore, every effort
is needed to obtain accurate and real values for the input
variables. Data should be kept and updated as soon as there
are changes, such as costs involved for each of the
switchgears and replacement or failures occurred. This is to
make sure the accuracy of LCCA generated from the
database of switchgear records.
Prof. Madya. Dr. Amir Hisham Hashim is
the Director of Institute of Energy Policy
and Research (IEPRe), Universiti Tenaga
Nasional where his main task is to promote
and market research and consultancy
activities of the university. He also teaches
at the College of Engineering, UNITEN and
supervises several MSc and PhD students.
Dr. Amir holds 2 postgraduate degrees in
Power Engineering, an MSc. from the
University of Manitoba, Canada and a PhD
from the University of Strathclyde, UK. He
has more than 10 years experience in
industrial research and consultancy. His
interest lies both in the technicality of power
engineering and also its economic operation.
His research activities are in distribution and
transmission systems and its optimisation,
economic analysis and risk assessment. His
other main research area is in analysing
electricity markets. He is currently working
on multiple projects with TNB Research,
TNB Transmission, TNB Distribution,
Sabah Electricity and the Energy
Commission.
Faizah Bte Mohd Khalid is a lecturer in
the department of Accounting in Universiti
Tenaga Nasional. She graduated B. Acc
(Hons.) from UiTM and MBA from from
UiTM. She had working experience of
accounts trainee in Progressive Impact
Corporation Sdn Bhd (1995), accounts
executive in Progressive Impact Corporation
Sdn Bhd (1996-1997), a teacher in Sek.
Raja Perempuan Taayah, Ipoh (2000) and
lecturer, Universiti Tenaga Nasional (2002present). Her work fields are business
administration and accountings.
V. REFERENCES
Papers from Conference Proceedings (Published):
[1]
[2]
Richard Blakeley & Chris Jones, “A UK Manufacturer’s View - New
Switchgear And Aftermarket Options”, I.E.E. Colloquium 21st Oct.
1997.
CJ Jones, T Irwin, A Headley, and B Dakers, “The Use of Diagnostic
Based Predictive Maintenance to Minimize Switchgear Life Cycle
Costs”, 9th CEPSI Conference, Hong Kong 1992.
Books:
[3]
[4]
[5]
Cyert RM & March JG. (1963), A Behavioral theory of the firm.
Englewood Cliffs, NJ: Prentice-Hall.
Caroll JS & Johnson EJ (1990), Decision research-a field guide.
Newbury Park, CA; SAGE Publications Inc.
W.Edwards Deming, C.V.Ramamoorthy, “Software Development
Methodology Today”, Chapter 1, Pearson.
Noormala Binti Ahmad is a lecturer in the
department of Accounting in Universiti
Tenaga Nasional. She graduated B.A.(Hons)
in Accounting from University of Kent at
Canterbury, United Kingdom in 1985, and
M. Acc from Universiti Kebangsaan
Malaysia in 2001. She has working
experience of being a management trainee in
Pejabat Pengarah Tanah & Galian
N.Sembilan (1986-1987), management
trainee in Petronas (1987-1988), senior
lecturer in Kolej Yayasan Pelajaran Mara
Kuala Lumpur (1988 – 2002) and lecturer in
Universiti Tenaga Nasional (2002 –
present). Her work fields are management
and accountings.
VI. BIOGRAPHIES
Loo Yim Ling was born on 9th May 1985,
in Sabah Sandakan. She graduated from
Sekolah Kebangsaan Sung Siew and had
Bachelor degree of Computer Science
(Software Engineering Hons.) in Universiti
Tenaga Nasional. She is currently a research
assistant in Institute of Energy Policy and
Research (IEPRe), Universiti Tenaga
Nasional and doing Masters in Information
Technology. Her work field is related to
artificial intelligence, software engineering
and object-oriented programming.
Ir. Hj. Abdul Malik Mohamad is a Senior
Manager in TNB Research and also the Coordinator for the Power Engineering Centre,
UNITEN. He holds a B.Eng.(Hons) in
Electrical Engineering from the University
of Brighton, UK. He has more than 25 years
of field and management experience in the
electricity industry. He has led numerous
Distribution projects in TNB particularly in
Information
Systems
and
Metering.
Currently he is a Project Director of 7
research groups in TNB Research and
UNITEN dealing with clients in Sabah
Electricity and TNB Transmission and
Distribution Divisions. Ir Hj. Abdul Malik is
a Professional Engineer (P. Eng.) registered
Yaw Chong Tak was borned in Kuala
Lumpur, Malaysia on 21st February 1984.
He was graduated in 2004 with Bachelor of
Electronics and Electrical Engineering
(Hons.), Universiti Tenaga Nasional
(UNITEN). Currently, he is working as
research assistant in Institute of Energy
Policy and Research (IEPRe), UNITEN
while pursuing his Master in Power
76
The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
with the Board of Engineers Malaysia
(BEM), a Corporate Member of the Institute
of Engineers Malaysia (IEM) and a certified
Competent Engineer (33kV) of Energy
Commission Malaysia.
77
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