Smart Overhead Lines performance enhancement initiatives to improve distribution networks of

Abu Dhabi Distribution Company

Husain Mohamed Al Meer, Surrendra Singh Chhajta and Ali Al Uzri Assets Management Directorate

Abu Dhabi Distribution Company (ADDC), ADWEA Abu Dhabi, UAE

Husain.alaas@gmail.com, Surendrac@addc.ae, Ali.aluzri@addc.ae

Abstract—The performance of Overhead Lines (OHLs) in the Western Region of the Emirate of Abu Dhabi has the highest contribution to the distribution network reliability indices of Abu Dhabi Distribution Company (ADDC). Over the past seven years, the OHL's interruption accounted for an average share of 70% of both regional unplanned HV SAIDI and SAIFI. The transient faults of those interruptions has the major contribution with 24 % of regional unplanned SAIDI and 37% of regional unplanned SAIFI, while the other component of OHL accounted for the remaining percentages such as bad condition of insulators and jumpers, etc. This paper addresses ADDC's strategy and experience in providing systematic evaluation for its OHL network performance. This strategy considers: 1) identification of circuits, 2) prioritization of circuits and 3) potential initiatives and projects to improve OHL's performance. The developed approach and initiated projects undergo comprehensive evaluations on a periodic basis to check their effectiveness in achieving the expected performance target. A simple and practical approach has been recommended for identifying the system components that contribute significantly to the reliability indices, ranking of initiatives and continuous review of the planned actions. This approach focuses on the key contributors of the network's problems and could provide considerable benefits associated with the Capital and Operational Expenditures (CAPEX and OPEX).

Keywords—Key Performance Indicators, Mean Time To Repair, Customer Interrupted, Customer Minutes Lost, Vertical Mounted Fuse Switch, Vertical Mounted Switch

I. INTRODUCTION Abu Dhabi Distribution Company (ADDC) is

mandated to supply reliable and secure water and electricity to all consumers in the Emirate of Abu Dhabi at an optimum cost. ADDC faces a big challenge in maintaining the continuity of power supply due to various causes as similar to any other distribution utilities around the world. Subsequently, comprehensive evaluations considering all variables that contribute to the degradation of power supply reliability and quality are continuously analyzed and also actions are initiated for improving and enhancing network performance.

ADDC has adopted several Key Performance Indicators (KPIs) to map out the true status of network performance and use its outcome as a guidance to allocate future investments. Such KPIs include System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI) and Mean Time To Repair (MTTR). SAIDI and SAIFI indexes are used to evaluate the network performance in term of duration and frequency that customers are impacted, whereas the MTTR points out the effectiveness of repairing procedures and responses to failure. Historically, all Overhead Lines (OHLs) distribution networks located in both Eastern and Western Regions of Abu Dhabi Emirate have significantly impacted ADDC's network performance with overall annual SAIDI and SAIFI share of 50% and 45% respectively, as shown in Table I. As a consequence, thorough review of the root causes of OHL interruption is imperative in order to figure out possible solutions to improve network performance to improve customer satisfaction [1].

TABLE I. TOTAL UNPLANNED SAIDI AND SAIFI AND TOTAL UNPLANNED OHL SAIDI AND SAIFI

Year SAIDI

(Minutes/Customers) SAIFI

(Frequency/Customers) Total overall unplanned OHL Total overall

unplanned OHL

2006 103.84 55.15 1.22 0.52

2007 92.84 42.38 1.08 0.44

2008 132.02 71.94 1.42 0.70

2009 132.37 57.97 1.51 0.64

2010 129.93 52.63 1.60 0.67

2011 111.07 53.77 1.31 0.54

2012 142.58 87.14 1.55 0.85

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50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

2006 2007 2008 2009 2010 2011 2012

Minu

tes Pe

r Cu

stome

r

Year

OHL jumper VMS or VMSF OHL other componentLA or insulator transient tripping unknownOHL Sum

II. FAILURE ANALYSIS

The performance of OHLs could be impacted by some weather such as heavy rains, which may clean the insulators. However, there are many negative environmental impacts that can lead to power supply interruptions such as lightning, wind, pollution, trees uprooting, pole burning and rusting of contacts. Throughout the period from 2006 to 2011, the SAIDI trend of Western Region OHLs were fluctuating, as depicted in Fig. 1 due to many factors such as failure of OHL jumper, Vertical Mounted Switch (VMS) or Vertical Mounted Fuse Switch (VMSF), Lightning Arrestor (LA) or insulator, unknown and transient tripping. However, in the last two years, the trend increased significantly mainly due to transient interruptions with an average contribution of 50%. This issue has raised many concerns for ADDC, in which several ongoing and new solutions have been developed.

Fig. 1. The Western Region SAIDI per OHL components

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0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

2006 2007 2008 2009 2010 2011 2012

Freq

uenc

y P

er C

usto

mer

Year

OHL jumper VMS or VMSF OHL other comp. LA or insulatortransient tripping unknown OHL Sum

Fig. 2. The Western Region SAIFI per OHL components

The overall unplanned SAIFI followed the same trend of SAIDI as the contribution of transient tripping is more than 30% during the same period as indicated in Fig. 2. Therefore, the root cause of transient tripping needs to be established in order to formulate robust business case to mitigate such failures.

For better evaluation of power supply interruption and setting up an credible investment plan, all attributors that impact OHL Network Performance are classified into [2]:

• Controllable causes/failures: ageing installation, loose contact, vegetation, quality of installation, network design and configuration.

• Uncontrollable causes/failure: lightning, winds, rain (environmental phenomena), animals and damage by others (external)

As described in the Table II, the controllable causes account for 96%, while the remaining 4 % are uncontrollable causes that are mainly due to birds, animals and damages by others, which can be also controlled over a long term by appropriate engineering interventions.

TABLE II. THE ROOT CAUSE CLASSIFICATIONS FOR INCIDENTS OCCURRED IN 2012

Serial No.

Root cause evaluation

Root Cause Controllable causes

Uncontrollable causes

1 Accident, damage 2 0

2 Age 37 0

3 Birds and animal 0 0

4 Defective equipment 12 0

5 Incorrect protection setting 3 0

6 Fire 71 0

7 Flashover 7 0

8 Overload 22 0

9 Switching error 2 0

10 Trees 18 0

11 Weather 251 0

12 Unknown 109 0

13 Bad condition 9 0

14 Damage by others 0 17

15 Total 543 22

III. OBSERVATIONS This section describes several observations identified

during OHLs performance evaluation. These observations help in identifying solutions to enhance the performance.

A. Observation 1 Weather is mostly blamed for high number of

transient interruptions with a share of 251 incidents occurred in 2012 as shown in Table I. This was taken as the root cause, which often led to a belief that transient interruptions are uncontrollable [2]. However, further investigation on the interruption root causes are essential to understand the pattern of failure and the suitability of

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equipment to work under the current operating condition. Elimination of such causes by taking pro-active actions can prevent its occurrence in OHL circuits. Therefore, It is useful to carry out post fault investigation to identify immediate actions to avoid repeated faults/tripping. The recommended steps for the investigation are:

• Identification fault current magnitude, faulted phase and protective element trip (e.g. earth fault and over current)

• Carrying out line patrolling to visually check if any defects/source of fault can be detected (e.g. contaminated insulator, loose jumper, trees, hot spot, defective equipment, etc.)

• Determination of weather condition at the fault zone (e.g. fog, strong wind, sand storm, humidity, strong rain, etc.)

• Review of Protection settings of protective device to identify if the cause is related to nuisance or others.

B. Observation 2 Porcelain insulators with lower creepage distance

(25mm/kV) are used in many OHLs as shown in Fig.3. Such insulators have adverse impact in overall OHL network performance due to atmospheric condition and high pollution level under which these insulators are operating. Thus, accumulation of dusts, sands and humidity result in frequent insulator flashovers.

Fig. 3. Some removed porcelain insulators from ADDC network as a result of very less creepage distance

C. Observation 3 There are 68 incidents due to fire particularly on

jumpers and poles. The common reason for these incidents is that the used porcelain insulators in the coastal area that are not suitable for such environment and also are aggravated by non-integrity of earthing systems. Fig. 4. shows an example of fire caught part of 11kV wooden pole in Western Region of the Emirate of Abu Dhabi.

D. Observation 4 The design of Vertical Mounted Fuse Switch (VMFS)

and Vertical Mounted Switch (VMS) needs to be re-evaluated as frequent tripping occurred due to its bad condition, while the remaining life of other components is not over yet. Consequently, inspection regime and condition monitoring activities needs to be evaluated.

Fig. 4. The burnt 11kV wooden pole in Western Region

E. Observation 5 The cold line restoration for HV fuse failure could

positively contribute 60% to 80% of SAIDI and SAIFI of HV fuse component.

F. Observation 6 MTTR of OHL faults is about two hours. However,

restoration of power supply could be much longer for the cases, where OHL poles are fallen down. Therefore, evaluation of foundation, power supply arrangement along with the time taken to restore the supply is to be reviewed and an improvement, if possible, is to be undertaken. G. Observation 7

Approximately, 56% of transient faults are due to spurious operation of earth fault relay only. Therefore, this leads us to evaluate the following issues:

• Review of protection settings

• Review of earthing/grounding system

• Conduct power quality surveys

IV. NETWORK PERFORMANCE INTIATIVES The initiatives for improving network performance

are classified into three main categories, as follows:

• Assets performance improvement initiatives

• Maintenance program improvement initiatives

• Network design improvement initiatives

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A. Assets performance improvement initiatives It focuses on improving the performance of OHL

circuits by initiating the identified network performance improvement projects. This process starts with: 1) Prioritization of all OHL circuits based on the

following criteria: a) Rank top 20 incidents by number of faults

b) Rank top 20 incidents by CML and faults more than 3 times

c) Rank top 20 incidents by number of customer interrupted

d) Rank top 20 incidents by SAIDI and SAIFI 2) Obtain observation and recommendation of

Condition Monitoring Survey for all works required to be carried out (e.g. bad condition equipment).

3) Quantify Percentage improvement of SAIDI and SAIFI per identified initiative and compare the impact to western region and overall ADDC through sensitivity analysis.

The methodology for calculating the expected percentage Improvement of SAIDI and SAIFI is basically based on “What If Analysis”, which is mainly based on analysis of historical Network Performance Improvement Projects in term of historical failures of specific circuits, groundwork executed for improving these circuits and the percentage improvement gained. The steps, including equations used to achieve the percentage improvement are described below:

A. Step 1:

Calculate the overall ADDC OHL unplanned SAIFI and SAIDI

Customer ADDC ofNumber TotalCI ADDC unplanned Total=SAIFI

Customer ADDC ofNumber Total

CML unplanned ADDC Total=SAIDI

B. Step 2:

Calculate and rank the regional and overall SAIDI and SAIFI of each circuit based on its contribution to region wide and overall.

C. Step 3: The identified problematic circuits as per the defined

criteria are segregated, based on fault such as transient tripping, Jumper, Pole, Insulators, VMS, and further used as a basis for defining optimum initiatives to improve network performance. This also includes the observation of Condition Monitoring Assessment to formulate the final conclusion. For example, utilizing Auto-Recloser to reduce transient tripping and increasing maintenance frequency to capture deteriorated equipment such as insulators, poles and VMS.

D. Step 4: Predicting SAIDI and SAIFI percentage improvement

for the proposed assets replacement. The methodology used for prediction is based on historical data about assets performance, previous implemented projects and problems observed. Statistically, utilizing auto-recloser in OHL in Eastern Region Network of Abu Dhabi Emirate has resulted in 60% reduction of Customer Interrupted (CI) and Customer Minutes Lost (CML) in all problematic circuits that had significant transient failure. In addition to this, carrying out refurbishment in other defined circuits by replacing insulators and other bad condition equipment in Western Region resulted in 60 % reduction of CI and CML of those circuits that had experienced transient failure as well as 80% reduction of CI and CML of those circuits that had sustained failure. All equations used to arrive the SAIFI and SAIDI Improvement are explained below:

1) The equations used for calculating SAIDI and SAIFI

of transient fault component of the identified circuits as an example are:

Customer ADDC ofNumber Totalfault) (transient dInterrupteCustomer =SAIFI

Customer ADDC ofNumber Totalfault) (transientLost MinutesCustomer =SAIDI

2) The equations used for calculating SAIFI and SAIDI improvement that are related to transient fault component of the identified circuits with an assumption of x reduction. X can be figured out from historical data as defined earlier as well as can be used for generating sensitivity analysis by varying x from 0 % to 100% :

Customer ADDC ofNumber Totalx)-(1 x ])fault [transient (CI=SAIFI

Customer ADDC ofNumber Totalx)-(1 x )fault]ient (CML[trans=SAIDI

3) The equations used for calculating the SAIFI and SAIDI improvement:

)() ( projectwith projectwithout SAIFISAIFISAIFI timprovemen −=

(1)

(2)

(3)

(4)

(5)

(7)

(8)

(9)

(10)

(11)

region in theCustomer ofNumber Totalcircuit theof CI unplanned Total=SAIFI

region in theCustomer ofNumber Totalcircuit theof CML unplanned Total

=SAIDI

Customer ADDC ofNumber Totalcircuit theof CI unplanned Total=SAIFI

Customer ADDC ofNumber Totalcircuit theof CML unplanned Total=SAIDI

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100x )(

ADDCunplanned

entimprovememtimprovemenpercentage SAIFI

SAIFISAIFI =

)() ( projectwith projectwithout SAIDISAIDISAIDI timprovemen −=

4) The equations used for calculating the Percentage improvement:

100x )(

ADDCunplanned

entimprovememtimprovemenpercentage SAIDI

SAIDISAIDI =

E. Step 5:

Formulate optioneering and rank the engineering solution options using Cost Benefits Analysis as shown in Table 3. For example, evaluating two alternatives such as carry out refurbishments work on the identified circuits and installation of Auto-Recloser on the same lines would result SAIDI % improvement of 2.69% for option 1 and 1.40% for option 2 [5].

TABLE III. OPTIONEERING TO IMPROVE NETWORK PERFORMANCE

Techno-economical evaluation

Proposals NPV cost

(AED)

NPV benefits (AED)

SAIDI Improvement

%

SAIFI Improvement

%

Option 1 4,118,982.90 74,014.30 2.69% 2.81%

Option 2 1,498,982.90 34,574.86 1.40% 2.41%

F. Step 6:

Carry out sensitivity analysis after formulating all proposals and selecting best options as defined in the previous step in order to determine how overall performance is affected by changing the assumptions such as the reduction of CI and CML, reduction of jumpers failure, reduction of insulator failure, reduction of transient tripping after implementation of the identified proposals. In addition to this, such analysis helps performance engineers to run multiple scenarios to improve overall decision making process. Both Fig. 5 and Fig. 6 show the sensitivity analysis results, in which it indicates that the 100% reduction of failure caused by transient faults on the identified circuits would result in an improvement of overall HV unplanned SAIDI and SAIFI of about 9.47% and 12.74% respectively.

0%2%4%6%8%

10%12%14%16%18%20%

100% 80% 60% 50% 40% 20% 0%

SAIF

I Im

prov

emen

t in

Perc

ent

Percentage Reduction of Failure

Insulator/LA Transient Jumper Other Conductor/Pole/Other OHL VMS/VMSJ Total Fig. 5. Sensitivity analysis of unplanned SAIFI improvement of overall

ADDC

0%2%4%6%8%

10%12%14%16%18%20%

100% 80% 60% 50% 40% 20% 0%

SAIF

I Im

prov

emen

t in

Perc

ent

Percentage Reduction of Failure

Insulator/LA Transient Jumper Other Conductor/Pole/Other OHL VMS/VMSJ Total

Fig. 6. Sensitivity analysis of unplanned SAIDI improvement of overall ADDC

B. Maintenance Program Improvement Initiatives

It aims to improve the effectiveness of maintenance program and root cause analysis. The following points are recommended:

• Decrease inspection interval for critical lines from once in five years to once every year due to huge impact of the OHL lines in overall Network Reliability.

• Aggressive Condition Monitoring, which includes also the utilization of Thermal Image Technology as a way to identify hotspot followed by proactive actions for rectifying all identified problems.

• Comprehensive Root Cause Analysis and to improve the quality of data submitted

• Enhance manpower skills to improve maintenance compliance.

• Adopt Reliability Centered Maintenance (RCM) concept for Overhead lines as already applied to distribution substation and primary substation components. This would definitely improve the efficiencies and effectiveness of maintenance program.

C. Network Design Improvement Initiatives It is targeted to improve design of network topology

for better network reliability. It is recommended to:

• Use of Auto-Recloser technology as most of faults are transient in nature [3]

• Installation of Fault Passage Indicators to improve MTTR as detection of faulty section would be faster, in which SAIDI will be improved. It is also improve SAIFI as it reduces the stress on equipment because of trial charge of OHL circuits is used to figure out the faulty section, which increase the probability of failure of OHL components. This also helps in identification of frequently failing sections based on which improvement actions can be initiated [4].

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• All OHL components shall undergo comprehensive review to improve its availability

• All Network Design to be reviewed in term of:

1) Distribution system topology (e.g. Open ring or radial)

2) Insulators type and creepage distance (e.g. Porcelain, Composite Insulators)

3) High voltage fuse size 4) Conductor size and characteristic 5) Span and sag 6) Type of towers (e.g. lattice, wood, composite, etc.) 7) Pole/ tower configuration 8) Jumpers (e.g. insulated jumpers) 9) New connection procedure 10) OHL Foundation

• Conduct Protection System review to reduce HV fuse failure/nuisance tripping

• Conduct earthing system review

V. CONCLUSION AND FUTURE WORKS

In conclusion, we presented ADDC's approach in evaluating its network performance and in identifying problematic circuits including prioritization of these circuits for investment decision making process. After carrying out the network performance improvement project, the techniques used in calculating the expected SAIDI and SAIFI will be verified.

Our analysis showed that there were several contributors to the degradation of OHL network performance at various impact level. As a consequence, the improvement of OHL performance is expected to be achieved through multiple solutions that target the entire life cycle of OHL, which starts from planning and design phase up to execution and commission of the lines.

The approach that we introduced can be easily extended and enhanced by integrating asset conditions and RCM to predict the failure rate and determine the appropriate maintenance regime to be used. As a result, the reliability analysis using powerful software can be conducted, which could significantly improve the overall analysis.

ACKNOWLEDGMENT The authors would like to thank Mr. Abdulla Saif Al

Nuaimi, Director General of Abu Dhabi Water and Electricity Authority and ADDC Chairman, Eng. Mohamed Bin Jarsh Al Falasi, ADDC Managing Director, Eng. Abdulla Al Khemieri, ADDC Electricity

O&M Director and his teams from Western Region Department, OHL Department, Maintenance Planning Section and Reporting Section for their help, encouragement and support. Special appreciation is due to our engineers in Electricity Assets Performance Section, Eng. Sultan Al Kaabi from AADC and Dr. Charles Su from Charling Technology, Australia for their reviews and comments to this paper.

REFERENCES [1] Abu Dhabi Distribution Company's network interruption database from

2006-2012 [2] M. Faris, M. Hashimi, L. Suiani, Z. Bishmi and W. Nazmy, “Managing

Transient Interruption on aged 22kV overhead lines in TNB distribution network through engineering practices assessment and insulation coordination guidelines” CIRED Conf. Frankfurt, 6-9 June 2011, pp. 1,4.

[3] S.Hannan, Iskandar N., D. Embang, “Overhead line reliability indices improvement using self –feeder automation,” CIRED Conf. Frankfurt, 6-9 June 2011, pp. 1,4.

[4] Bjerkan, Eilert, “Efficient fault management using remote fault indicators” CIRED Conf. Prague, 9-11 June 2009, pp. 1,4.

[5] Malcolm, V. Harte., C. Carter, “The application of reliability methodology to select and prioritise Eskom Distribution Networks” CIRED Conf. Vienna, 21-24 May 2007, pp. 1,4.

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