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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

E-mail: 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: Marine-Base, U.S.T., Old-GRA and PH-Town 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, λs, average outage duration, rs, and annual unavailability, Us. 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.

 

λi is the failure rate and Ni 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

Ui, is the annual outage time and Ni 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), Ni = 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 2-4. 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 Marine-Base Substation                   Figure 5: Failure Rate for U.S.T. Substation

Figure 6: Failure Rate for Old GRA Substation              Figure 7: Failure Rate for PH-Town Substation


Results shown in Figure 4-7 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 low-density 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 PH-Town 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 Old-GRA 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 “four-nine” or 99.99% reliability. A “four-nine” 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 4-11, 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., Marine-Base, Old-GRA, 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 Marine-Base, U.S.T., Old-GRA, and PH-Town injection substations, Port Harcourt, Rivers State-Nigeria.  

 

Roos, F. and Lindahl, S. (2004) “Distribution System Component Failure Rates and Repair Times – An Overview” Nordic Distribution and Asset Management Conference, Espoo, Finland.