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Ugbowo 2x15MVA, 33/11kV electric power distribution network.
Annual data of daily power outages from August 2019 to July 2020 of
the 11kV feeders were collated and collected from the Ugbowo
distribution network. The daily outages of the 11kV feeders were used
to compute the monthly and the yearly reliability indices of the feeders
of the Ugbowo distribution network. Both the monthly and annual
failure rates, MTTR, MTTF, MTBF, availability and unavailability
were computed and analyzed using the Load Point Indices (LPI) and
the Microsoft Excel was used for graphical interpretation in order to
estimate the reliability indices and to determine the frequency of power
outages of the network. Also, the Performance Indices (PI) of the system
were evaluated for an in-depth assessment. The annual and monthly
failure rates, mean time to repair, mean time to failure, mean time
between failure, system average interruption frequency indices (SAIFI),
system average interruption duration indices (SAIDI), average service
availability index (ASAI), etc for a period of twelve (12) months were
analyzed. The computed results obtained from the analyses of the 11kV
feeders of the distribution network showed that the power unavailability
is very high for the period under study with Ugbowo 11kV feeder having
the highest power unavailability of 76.52%, Eguaedaiken 11kV feeder
73.42%, FGGC 11kV feeder 69.50% and Uselu 11kV feeder 66.96% in
the network. The annual failure rate results revealed that Ugbowo 11kV
feeder has the highest failure rate of 46.52%, followed by Eguaedaiken
feeder 44.83%, FGGC feeder 44.27% and Uselu feeder 41.60%. This is
due to frequent power outages resulting from regimented load shedding
(scheduled outages) being practice in the network as way to manage
equipment limitations, poor energy management system, etc. and
unscheduled (forced) outages due to faults in the network. Furthermore,
the PI of the system revealed that the annual total outage duration
(SAIDI), outage frequency (SAIFI) and percentage availability (ASAI)
were 175.7504hours, 3.3780f/cu.yr and 97.99% which is a far cry from
the international acceptable standard value (IASV) of 2.5 hours, 0.01
and 99.99% respectively which showed that the power supply services
in the network is unreliable.
use of steel pipes; however, pipelines have continually failed from
corrosion as a result of the poor analysis of data recorded from the
intelligent pigging of the pipeline. In this work, two ultrasonic
Pipeline Internal Gauge PIGs and one magnetic flux leakage PIG
were used to obtain data from three phase crude (carrying a mixture
of crude oil, water and gas) from Mimbo to TMMP. The pipelines are
located in the Niger Delta region of Nigeria. The ultrasonic PIG uses
the pulse-echo principle to determine the wall thickness of steel pipe
walls while the magnetic flux leakage PIG measures the changes in
the magnetic field close to the pipe wall. A cleaning PIG” was first
run through the pipelines to ensure that the pipelines were free of
debris that may alter the signals and data collected by the intelligent
PIGs (smart PIGs). The collected data from the smart PIGs runs
were analysed using Dacon Technology Software and the results
obtained was compared with the line pipe design data obtained from
international standards and codes to determine the status of the
service condition of the operating pipeline systems. The Estimated
Repair Factor (ERF) results were the basis for determining the
service condition of the pipeline. The result showed that MFL
intelligent pigging reduced the metal loss in the pipeline transporting
three (3) phase crude from Mimbo to TMMP by 17.11% between
2005 and 2015 pigging periods. The metal loss peak data showed
that the pipeline transporting three phase crude from Mimbo to
TMMP passed through three separate environments which have
different corrosion rate on the pipeline. From the data collected and
analyzed, it was observed that the internal conditions of the pipeline
have more effect on the ERF. It was also observed from the data
obtained that the points on the pipeline from Mimbo to TMMP
transporting liquid crude with ERF ≥ 0.2 have anomalies at the
internal wall of the pipeline.
Ugbowo 2x15MVA, 33/11kV electric power distribution network.
Annual data of daily power outages from August 2019 to July 2020 of
the 11kV feeders were collated and collected from the Ugbowo
distribution network. The daily outages of the 11kV feeders were used
to compute the monthly and the yearly reliability indices of the feeders
of the Ugbowo distribution network. Both the monthly and annual
failure rates, MTTR, MTTF, MTBF, availability and unavailability
were computed and analyzed using the Load Point Indices (LPI) and
the Microsoft Excel was used for graphical interpretation in order to
estimate the reliability indices and to determine the frequency of power
outages of the network. Also, the Performance Indices (PI) of the system
were evaluated for an in-depth assessment. The annual and monthly
failure rates, mean time to repair, mean time to failure, mean time
between failure, system average interruption frequency indices (SAIFI),
system average interruption duration indices (SAIDI), average service
availability index (ASAI), etc for a period of twelve (12) months were
analyzed. The computed results obtained from the analyses of the 11kV
feeders of the distribution network showed that the power unavailability
is very high for the period under study with Ugbowo 11kV feeder having
the highest power unavailability of 76.52%, Eguaedaiken 11kV feeder
73.42%, FGGC 11kV feeder 69.50% and Uselu 11kV feeder 66.96% in
the network. The annual failure rate results revealed that Ugbowo 11kV
feeder has the highest failure rate of 46.52%, followed by Eguaedaiken
feeder 44.83%, FGGC feeder 44.27% and Uselu feeder 41.60%. This is
due to frequent power outages resulting from regimented load shedding
(scheduled outages) being practice in the network as way to manage
equipment limitations, poor energy management system, etc. and
unscheduled (forced) outages due to faults in the network. Furthermore,
the PI of the system revealed that the annual total outage duration
(SAIDI), outage frequency (SAIFI) and percentage availability (ASAI)
were 175.7504hours, 3.3780f/cu.yr and 97.99% which is a far cry from
the international acceptable standard value (IASV) of 2.5 hours, 0.01
and 99.99% respectively which showed that the power supply services
in the network is unreliable.
use of steel pipes; however, pipelines have continually failed from
corrosion as a result of the poor analysis of data recorded from the
intelligent pigging of the pipeline. In this work, two ultrasonic
Pipeline Internal Gauge PIGs and one magnetic flux leakage PIG
were used to obtain data from three phase crude (carrying a mixture
of crude oil, water and gas) from Mimbo to TMMP. The pipelines are
located in the Niger Delta region of Nigeria. The ultrasonic PIG uses
the pulse-echo principle to determine the wall thickness of steel pipe
walls while the magnetic flux leakage PIG measures the changes in
the magnetic field close to the pipe wall. A cleaning PIG” was first
run through the pipelines to ensure that the pipelines were free of
debris that may alter the signals and data collected by the intelligent
PIGs (smart PIGs). The collected data from the smart PIGs runs
were analysed using Dacon Technology Software and the results
obtained was compared with the line pipe design data obtained from
international standards and codes to determine the status of the
service condition of the operating pipeline systems. The Estimated
Repair Factor (ERF) results were the basis for determining the
service condition of the pipeline. The result showed that MFL
intelligent pigging reduced the metal loss in the pipeline transporting
three (3) phase crude from Mimbo to TMMP by 17.11% between
2005 and 2015 pigging periods. The metal loss peak data showed
that the pipeline transporting three phase crude from Mimbo to
TMMP passed through three separate environments which have
different corrosion rate on the pipeline. From the data collected and
analyzed, it was observed that the internal conditions of the pipeline
have more effect on the ERF. It was also observed from the data
obtained that the points on the pipeline from Mimbo to TMMP
transporting liquid crude with ERF ≥ 0.2 have anomalies at the
internal wall of the pipeline.