JOURNAL OF RESEARCH IN NATIONAL DEVELOPMENT VOLUME 8 NO 2, DECEMBER, 2010
RELIABILITY ASSESSMENT OF PORT HARCOURT 33/11KV DISTRIBUTION SYSTEM
A.O. Ibe, N.O. Ogbogu and A. Akhikpemelo
Department of Electrical
Engineering, University of Port Harcourt, Port Harcourt
Email: ibrahimmamudu@yahoo.com
Abstract
Consumers of electrical energy expect a network to support their apparatuses
with continuous and reliable supply.This makes reliability studies an
important task besides all the other
analyses required for assessing the system performance. The paper presents an analytical approach in the
reliability assessment of the Port Harcourt 33/11kV power distribution system.
The assessment was performed with the 2009 power outage data collected from the
monopolistic operator of the Nigerian power system, National Electric Power
Authority (now unbundled and called Power Holding Company of Nigeria, (PHCN)
Plc) on the four substations: MarineBase, U.S.T., OldGRA and PHTown injection
substations radiating from the Port Harcourt Town 132/33/11kV
transmission/injection substation. Thus, this study will enable utilities
to determine the state of reliability of each substation and hence, provide a
standard for prioritizing maintenance and upgrade of its distribution substation
facilities.
Keywords: Electrical distribution system, reliability, analytical assessment,
reliability indices, ETAP
Introduction
Electricity reliability means that electric power service should be delivered to
the customer with a high degree of assurance. Historically, most important
attention regarding reliability analysis has been put into power generation
rather than into distribution system reliability (R. Billinton et al, 1996).
Distribution systems make the greater contribution to the unavailability of
Electric Power Supply to customers and the liberalization of the power sector
would make distribution reliability of more interest now than ever before.
Customer demands for reliable power are quickly changing. Not only is more
energy being demanded, but this energy must be provided at increasing levels of
service reliability. A sustained interruption can cost certain customers
hundreds of thousands of dollars per hour. Even a momentary interruption can
cause computer systems to crash and industrial processes to be ruined. To many
customers with sensitive electric loads, reliability as well as the cost of
energy may drive decisions such as: where a new plant is to be located, whether
an existing plant will be relocated, or whether a switch to a new energy
provider will be pursued (R.E. Brown et al, 1996).
Since a majority of customer reliability problems stem from distribution systems
(R. Billinton et al, 1998), utilities must focus on distribution systems if
substantial improvement in customer reliability are to be gained. Deregulation
of the industry has also made it critical for utilities to provide this level of
reliability at the lowest possible cost. To do this, reliability assessment are
needed.
There are two main techniques to assess
electric power distribution reliability: Simulation and analytical techniques.
In the Monte Carlo simulation, it is highly time consuming and expensive because
it has to simulate a huge number of failures. Also, since the simulation of
probabilistic events generate variable results, in effect generating the
variable of real life, it is usually necessary to perform a number of runs in
order to obtain estimates of means and variance of the output parameters of
interest, such as availability, number of repairs arising and repair facility
utilization (O’Connor, 2002). The
Analytical techniques represent the system by mathematical models and evaluate
the reliability indices from these models using mathematical solutions.
Generally, there are five main procedures in analytical approach: State space
diagram generation, system state enumeration, system state analysis, remedial
action, reliability indices.
Reliability indices are numerical parameters that reflect the capability of the
system to provide its customers by an acceptable level of supply. They estimate
the system reliability by providing the quantitative measures at each individual
load point or for the whole system. In composite power evaluation, as described
before, two sets of indices which indicate the performance of the whole system
or the performance at each individual load buses within a system may obtain. The
main reliability indices in the power distribution system evaluation are
frequency of interruption and the associated duration. These two indices are
important as they indicate the expected frequency and duration of load supply
interruption (Roy Billinton et al, 1998). The above assessment method has been
implemented in the engineering tool ETAP.
Reliability indices
In the context of distribution systems, reliability has historically been
associated with sustained customer interruptions (interruptions lasting more
than a few minutes). The basic reliability indices (load point indices) used to
assess the reliability of a distribution system are; load point average failure
rate,
λ_{s},
average outage duration, r_{s}, and annual unavailability, U_{s}.
Component failure rates and repair times are obtained by observation of a
population. The average annual failure rate,
λ,
is calculated as (F. Roos et al, 2004).
Where;
f is the number of failures
n
is the number of components
considered
N
is the number of years of recorded data
To reflect more actual system severity, additional reliability indices called
system indices are used. The most common of these additional indices are; System
Average Interruption Frequency Index (SAIFI), System Average Interruption
Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI),
Average Service availability Index, (ASAI).
SAIFI
provides information about the average time the customers are interrupted. These
values represent the number of sustained interruptions.
λ_{i}
is the failure rate and N_{i} is the number of customers of
load point i.
SAIDI
provides information about the average time the customers are interrupted. These
values represent the number of interruption hours that an average customer
experiences in a given year
U_{i},
is the annual outage time and N_{i} is the number of customers of
load point i.
Customers
Average Interruption
Index, CAIDI (h/int.):
Average
Service Availability
Index, ASAI (%)
Where: 8760 is the number of hours in a calendar year.
The reliability indices ASAI and CAIDI are also widely used, but they can be
directly computed from SAIFI and SAIDI and offer no new information. Although
sustained interruptions have historically received the most attention, the
growing sensitivity of electronic loads has made the inclusion of other voltage
disruptions necessary when considering customer reliability. The first of these
to emerge is momentary interruptions, which is already an indispensable aspect
of distribution reliability [R.Brown
et al, 1996]. Voltage sags
are quickly making the transition from a power quality issue to a reliability
issue, and voltage spikes and voltage flicker may be soon to follow.
The reliability aspects considered in this paper are: sustained interruption
frequency, and sustained interruption duration.
Application study
The state of reliability of four 33/11kV distribution substations located in
Port Harcourt City, Rivers State of Nigeria were evaluated in order to assess
the reliability of electrical energy distribution in Port Harcourt City. The
analytical technique described in the previous section was used to analyze the
system of figure 2. The system has 16 load points and its reliability data and
system data are given in Table 3, where λ
= failure rate (f/yr), r = repair time
(h), N_{i} = number of
customers connected to the load point.
Figure 1 shows a small test system implemented in ETAP 4.0 in order to clarify a
reliability calculation approach. It is a 33/11kV substation, containing of
primary as well as secondary side breakers and bus bars and two parallel
transformers. The hand calculation for part of the system has been illustrated,
to demonstrate how the software calculates the indices.
Calculation of Load point indices
As noted previously in this part reliability indices for part of the sample
system which supplies the load point 5, 6, 7 and 8 will be calculated by hand
.
Figure
1: Test System
Table 1: Load point indices for system illustrated in fig.1
Load Point 
Failure Frequency
[f/yr] 
Failure Duration [h] 
LP5 
0.163 
12.51 
LP6 
0.364 
13.51 
LP7 
0.564 
37.01 
LP8 
0.465 
32.01 
System indices:
System indices can be calculated by applying equations 24. Applying these
equations yield:
SAIFI = 0.455 (int/yr.cust)
SAIDI = 14.253 (h/yr.cust)
CAIDI = 31.325 (h/int)
ASAI = 99.84 (%)
Where: 1761, 2088, 5638, and 5017 are the number of customers along the load
point LP5–LP8 respectively.
Overall system indices
Table 2: Overall system indices for system illustrated in figure 1
Index 
Unit 

N 

14504 
SAIFI 
[int/yr] 
0.455 
SAIDI 
[h/yr] 
14.253 
CAIDI 
[h] 
31.325 
ASAI 
% 
99.830 
Analytical studies
Figure 2: The PH Town 33/11kV Distribution Network.
Table 3: Load point indices
Load Point 
Failure Frequency
[f/yr] 
Failure Duration [h] 
LP1 
0.8500 
13.01 
LP2 
0.6200 
11.41 
LP3 
0.4300 
7.81 
LP4 
0.3300 
6.00 
LP5 
0.1630 
12.51 
LP6 
0.3640 
13.51 
LP7 
0.5640 
37.01 
LP8 
0.4650 
32.01 
LP9 
0.5421 
29.81 
LP10 
0.1921 
26.26 
LP11 
0.5821 
34.61 
LP12 
0.3661 
20.48 
LP13 
0.3951 
24.00 
LP14 
0.1161 
17.81 
LP15 
0.3401 
24.13 
LP16 
0.2961 
19.01 
Figure 4: Failure Rate
for MarineBase Substation
Figure 5: Failure Rate for U.S.T. Substation
Figure 6:
Failure Rate for Old GRA Substation
Figure 7: Failure Rate for PHTown Substation
Results shown in Figure 47 shows the weak points in the various Substation e.g.
in the U.S.T. substation system, the result shows that load point 7 and 8
(Wokoma and Federal) are the weakest point in the system. The failure frequency
and the interruption duration at this load points are much higher when compared
to load point 5 and 6 (U.S.T. and Ojoto).
Table 4: Overall System Indices
substations 
SAIDI (h/yr) 
SAIFI (int/yr) 
CAIDI (h) 
ASAI (%) 
Marine Base 
6.30 
0.58 
10.86 
99.92 
U.S.T 
14.22 
00.45 
31.43 
99.83 
Old GRA 
15.75 
0.49 
31.60 
99.82 
PH Town 
6.43 
0.29 
21.75 
99.92 
Figure 8: Variation of SAIDI With Respect to Substations. Figure 9: Variation of SAIFI With
Respect to Substations
Figure 10: Variation of CAIDI With Respect to Substation. Figure 11: Variation of SAIFI
With Respect to Substations
Examples of reliability standards used by some utilities are:
·
ASAI ≥ 0.9998, SAIFI < 1, CAIDI < 2
hours;
·
ASAI ≥ 0.99975 for urban, ≥ 0.99935
for lowdensity rural, CAIDI ≤ 270 min, SAIDI ≤ 187 min,
·
SAIFI 0.75 for residential, 0.6 for
commercial, SAIDI 65min for residential, 45 min for commercial, at most one
outage/year and 80min for very large commercial.
Figure 8
10 shows the indices for the overall system. The results show that the
availability of the system at Marine Base and PHTown substation is high and
almost near to 100% (ASAI= 99.92%). This implies that the system is more
reliable at this point. The system average interruption duration index (SAIDI)
is 6.3 and 6.4
[h/yr]
which itself demonstrate the high reliability of
the system at this point when compared to U.S.T. and OldGRA substation (14.2
and 15.7 [h/yr] respectively).
From the ASAI shown so far, we see that the overall system has an average
availability of below 99.99%. Some utilities have set an ASAI goal of
“fournine” or 99.99% reliability. A “fournine” reliability value translates
into a SAIDI of 52 minutes or 0.866 hour per year.
Conclusion
Reliability assessment studies are
crucial for distribution systems.
The results obtained from
reliability studies, provide an appropriate benchmark for assessing the system
performance and identifying the weak point of the system. Verifying the weak
point of the system may make the planners to increase the investment at a
certain load point during the planning phase and consequently reduce the further
costs due to supply interruption in operation stage.
The results presented in figure 411, not only
showed the weak points of the system, but also indicated the variation in the
adequacy of the system at each individual load point.
Acknowledgement
The authors would like to thank the members of staff of the 33/11kV U.S.T.,
MarineBase, OldGRA, and Port Harcourt Town injection substations of PHCN for
making data available for this research. The valuable input from Mary Beal of
Operation Technology Inc., especially on the ETAP tool is gratefully
acknowledged.
References
Billinton, R. and Allan, R.N. (1988) ‘Reliability Assessment of Large Electric
Power Systems’. Boston, US : Kluwer Academic Publishers.
Billinton, R. and Jonnavitihula, S.
(1996) ‘A Test System for Teaching Overall Power System Reliability Assessment,’
IEEWPES 1996
Winter Meeting, Baltimore, MD, IEEE.
Billinton, R., Reppen, N.D. Phavaraju, M.P. (1998) ‘Requirements for composite
system reliability evaluation models,’ IEEE.
Brown, R. Gupta, S. Venkata, S.S. Christie, R.D. and
Fletcher, R. (1996) ‘Distribution System Reliability Assessment: Reliability and
Cost Optimization,’ 1996
IEEE Transmission and Distribution Conference
Proceedings,
Los Angeles, CA, September.
O’Connor, P.D.T., (2002) ‘Practical Reliability Engineering’. (4th Edn),
England: John Wiley and Sons Ltd.
PHCN (2009) ‘Daily Dispatching and Operational Logbooks’ of MarineBase, U.S.T.,
OldGRA, and PHTown injection substations, Port Harcourt, Rivers StateNigeria.^{
}
Roos, F. and Lindahl, S. (2004) “Distribution System Component Failure Rates and
Repair Times – An Overview” Nordic Distribution and Asset Management Conference,
Espoo, Finland.