Chapter 4: Research Findings
4.2 Analytical techniques used
4.2.1 Determination of performance levels for an electricity distribution network
Reliability indexes are used to determine the level of performance of the distribution power network. These distribution reliability indices reflect the ability of sustaining service of power supply and the level of customer satisfaction (Xiao Xiangning, 2007). Traditional distribution reliability indices used in this study, only represent the impact of interruptions on customers, especially those interruptions longer 1 minute (Xiao Xiangning, 2007).
In the context of this paper specific electrical network interruption performance indexes applied are currently monitored by NERSA and incorporated in the performance compacting of utilities in South Africa. These specific indices are:
• SAIFI – Supply Average Interruption Frequency Index (Brown, New York )
SAIFI is a measure of how often a customer would experience sustained interruptions on average for a measurement period, typically a supply period of a year. SAIFI can be calculated as:
∑
Served customers Connected Total
expressed as interruptions per year.
• SAIDI – Supply Average Interruption Duration Index
SAIDI is a measure of how long a customer would experience sustained interruptions on average for a measurement period, typically a supply period of a year. SAIDI can be calculated as:
# ∑
Served customers Connected Total
T.R Khumalo (Montso) 41 expressed as hours per year.
• CAIDI – Average sustained interruption duration a customer would experience
Customer Average Interruption Duration Index is a measure of how long an average interruption lasts for a measurement period, typically a supply period of a year. CAIDI can be calculated as:
∑
Total Number of Customer Interuption expressed as hours per interruption
CAIDI can also be expressed as a function of SAIDI and SAIFI:
# #
SAIFI
• RSLI – Reticulation Supply Loss Index
RSLI is a measure of how long the capacity of the system on average was interrupted for a measurement period, typically a supply period of a year.
, - ∑ ./ 0 -
Total Connected kVA Served expressed as hours per year.
For illustrative purposes the analysis used focuses mainly on SAIDI (although our modelling also calculates SAIFI, CAIDI and RSLI).In order to effectively analyse and meaningfully understand reliability as a factor that influences design of the distribution network, the measurement and calculation of these indexes is crucial (Mavuso, et al., 2015) .
The next contrasted “benchmarking” is done to inform expected (target) performance levels as opposed to “modelled” expected (target) performance levels.
T.R Khumalo (Montso) 42 4.2.2 Benchmarking performance levels in the context of distribution networks.
As mentioned in the introduction, benchmarking is typically used to inform utilities’ executive management and the regulator as to what network performance levels a utility could be, and what reliability measures it should strive for (Cameron, et al., 2012). Typically statements are made such as that the utility aims to be in the top quartile of performance as measured e.g. by SAIDI (Distribution, 2015). In the context of the example provided in the introduction (refer to Figure 8), where power distribution utilities must offer power supply services that have quality, appropriate voltage levels and a low interruption rate (Piassona, 2016), this would imply that the utility would need to e.g. have a SAIDI of around 9 customer interruption duration hours per annum, irrespective of whether the fundamental network can actually achieve such a level of performance. A natural question that then comes to mind is - how to get to such levels of performance – what would you need to change in terms of the network topology (Siirtoa, et al., 2015) and what would it cost.
Distribution network reliability (SAIDI) is a function of:
The frequency of equipment failures per equipment type (i.e. equipment failure rate)
The exposure of the equipment to external failure causes (e.g. lightning, vegetation, pollution etc.)
The number of customers exposed to supply interruptions when faults occur The duration of supply interruptions when faults occur (Distribution, 2015) New distribution networks can be designed to minimise the above at optimal cost. For existing networks, some strategies and interventions to improve network reliability can be of a capital or operational and maintenance nature. Some examples of typical interventions are shown in Table 5.
Table 5: Network reliability optimisation strategies
T.R Khumalo (Montso) 43
The Network Reliability Planning Standard specifies the minimum reliability (Distribution, 2015) criteria for capital interventions that can be applied to existing as well as new distribution substations and lines to:
Reduce the number of equipment faults;
Limit the impact of supply interruptions in terms of customer numbers and energy interrupted; and
T.R Khumalo (Montso) 44
4.3 Case study research findings and results
The case study chosen for the purpose of this study is a project that is done in a very political sensitive area. Soweto is the largest Township situated in the south of Johannesburg, Gauteng.
The community has been experiencing a number of power outages in the Jabulani area (chosen for the study purpose) see fig 9. The power outages experienced are a result of many factors such as:
illegal connections, non-payment of electricity, constrained network and theft of infrastructure, to mention the least.
The City of Johannesburg in conjunction with the Johannesburg Development Agency are doing a project of developing mix residential dwellings to cover up most of the empty land in Soweto due to the reported criminal incidents , so this increases the notified power demand in the area.
The problem is that new developments cannot be connected to the existing power distribution infrastructure since its reliability to constantly supply electricity would be compromised. Thus, reliability based planning method has to be factored in the planning and design stage of the network in order to ensure that the network will be effectively optimized once in operation. This should ideally result in the distribution network performing well, even under maximum load.
In this new development called “Jabulani Precinct” a maximum of 25MVA will be required by 2020, where as an initial of 8MVA will be required by end of 2016. This is an important node of development for Soweto, which will comprise of a mixture of residential, commercial and light industrial loads. The development will positively contribute to the economy.
T.R Khumalo (Montso) 45 Figure 8: Geographical layout of Jabulani in Soweto
This area is currently supplied by Zola 132/11kV substation which is currently unfirm; thus there is not enough power available to supply the precinct in total.
The network needs to be strengthened and its performance can only be optimised by building a new substation (Jabulani 132/11kV substation) that will have new power lines and cables(built at a contingency condition of N-1) of which will deload Zola 132/11kV substation and supply the precinct. Fig10 indicates the sub-transmission network that is integrated in order to supply the whole of Soweto which includes the Zola and Jabulani area with the necessary electricity.
T.R Khumalo (Montso) 46 Figure 9: Current sub-transmission network that feeds the distribution network for Soweto
Currently there in no 132/11kV substation in Jabulani, hence the Zola Substation is performing under pressure due to the city developments happening in the area. Fig 10 indicates the new substation to be constructed that will deload the Zola area by supplying an additional 40MVA.
T.R Khumalo (Montso) 47 Figure 10: Future network indicating the proposed Jabulani 132/11kV substation
(Source: Eskom Distribution - Soweto Network Development plan 2015)
T.R Khumalo (Montso) 48 The Jabulani substation will be built by a developing company in line with Eskom’s standards and then handed back to Eskom to manage and operate. This was done so that development in Jabulani is not delayed due to power constraints issues.
The discussed performance indices have been measured and below are the results:
Figure 11: SAIDI for the Zola CNC
The SAIDI target for period of January 2015 to January 2016 was marked or capped at 14.61 hours.
This means that acceptable number of hours that in a year that the customer can be without power.
The Jabulani substation is not in operation yet thus figure 11 indicates that the Zola CNC failed to meet the target since the highest number of hours were 30.36.
T.R Khumalo (Montso) 49 Figure 12: SAIFI for the ZOLA CNC
The SAIFI target for period of January 2015 to January 2016 was marked or capped at 6 power interruptions/customer. The results indicate that the average frequency of 6 interruptions/customer was not realised since the highest average frequency was recorded to be 7, 07.
Figure 13: RSLI for Zola CNC
T.R Khumalo (Montso) 50 The SAIFI target for period of January 2015 to January 2016 was marked or capped at 2.68 per incident of supply loss. Results show that target was not reached since SLI was at 2.74. Thus the performance of the distribution network in Jabulani Soweto is within the stipulated limits.
Table 6 below indicates the worst events that impacted on the SAIDI for the financial 2015/16 in the area. The second fault which caused a power outage was due to adverse weather conditions. Section 4.4 of this chapter will indicate how reliability is managed when such unforeseen circumstances occur.
Table 6: Worst events in the supply area.
The graph in figure 14 indicates the impact of the worst power failure events in the area. 57.24%
was due to a cable fault, cable was overloaded because of illegal connections. 38.27% was due to a storm that occurred during November 2015 and 4.49% was due to a fuse failure in the transformer.
Event No Event Date NOFLEC Area Description Est Dur Cust_Hrs Cust_Int DSLI % Impact
2002056381 07/01/2016 1:57:52 AM FAULT Zola CNC MEADOW / MDLE4/48 1 11kV MV Feeder Underground Cable - MEADOW / MDLE4/48
1 11kV Feeder from MDLE4/185 to MDLE4/T3 Cable - Cable Problem - Cable Faulte 0.78 1 093.16 1 398 57.24%
2002059129 08/01/2016 3:05:31 PM FAULT Zola CNC NALEDI / JACQUELINE 1 11kV Rack-out Breaker - Adverse Weather - Storm
(Combination of Eliments) 1.00 730.82 729 38.27%
2002062757 10/01/2016 7:45:44 PM FAULT Zola CNC
MEADOW / MDLE5/169A 1 11kV MV Feeder Underground Cable - MEADOW / MDLE5/169A 1 11kV Feeder MDLE5/1030 11kV/400V Trfr - Drop-Out Fuse Link
Problem - Fu
0.73 85.74 117 4.49%
T.R Khumalo (Montso) 51 Figure 14: Power interruptions root cause for the Zola CNC
In optimizing and strengthening the reliability of the network for this specific project, the following hi level scope of work of is a considerable option:
The main developer opted to do a self-built for the new 132/11kV Substation in Jabulani Ext1 due to the time targets that they have to meet.
Eskom is to do the following:
Install, test and commission new control plant at Jabulani 132k/11kV substation; Provide Clerk of works (ensure that the scope of work is properly followed); Build approximately 2 x 300m 132kV Tern line from Protea/Mofolo 132kV to the proposed Jabulani substation ;Install tariff metering for bulk supplies (sectional titles) ; Install 1x11kV breaker panel at Civic Centre switching station
T.R Khumalo (Montso) 52 The developer is to do the following:
Provide a substation site, approximately 100x100m; Install 2 x 132kV feeder bays; Establish a 132kV busbar with 2x bus section using Bull conductor; Install 1 x 132/11kV 40MVA transformer bays; Build an 11kV switch room large enough for 24 feeders, 2 bus sections, 3 11kV incomers and 3 x 11kV VT panels; Install MV 2500 A 11kV busbar with 2 feeders, 1 incomers, 1 bus section and 1 VT panels;
Install +- 600m of 185mm2 XLPE cable from Civic Centre to Jabulani Substation( this is the contingency cable to supply the precinct on a contingency situation of N-1, as a backfeeding cable should the main one fail ). The developer will then hand the network over to Eskom. Once the above is done and commissioned, simulation indicates that the power constrains in the network will be alleviated and less interruption will be realized.
A geographical load forecast was considered in order to accommodate future load in the new substation, this was done to ensure that reliability standards are met once the substation is in operation. The initial load of 8 MVA which is needed by end 2016 has been factored. The focus is on ZOLA MAIN 132/11kV substation which falls under the Zola CNC. Table 7 indicates the base load forecast before the Jabulani substation project is implemented. The fields highlighted in red are an indication on constrained feeders while the fields highlighted in yellow indicate that the loading of these cables will still operate at a safe state.
Table 7: Soweto main substation's base load forecast.
>= 60% of Normal Rating
>= 80 % of Normal Rating
> 100 % of Normal Rating
T.R Khumalo (Montso) 53 Table 8 indicates the result of the future load forecast once the Jabulani 132/11kV substation is built and supplying power to the Jabulani Precinct and neighboring area.
Table 8: Soweto main substation's future load forecast.
2015 MD DIEPKLOOF MAIN 132/11kV 140000 105000 95236 98964 104030 107415 110361 113112 115654 117392 118711 119783 120622 121270 121773 122166 122474 122647 122778 122876 122943 122981 125440 MAPETLA MAIN 132/11kV 110000 70000 72340 76482 81310 84628 87371 89750 91691 93451 95078 96546 97805 98840 99655 100277 100743 101080 101307 101469 101585 101659 101693 MOFOLO MAIN132/11kV 140000 105000 96515 102769 104080 109481 114065 117963 121145 124073 126683 128938 130797 132259 133391 134246 134890 135378 135740 136032 136261 136428 136536 MOROKA MAIN 132/11kV 145000 105000 121979 127747 132882 136729 140372 143955147115 148882 151278 153302 154943 156155 157062 157738 158237 158610 158886 159105 159276 159399 159467 ZOLA MAIN 132/11kV 185000 145000 133740 139059 151943 157299 163728 168873 174647 178895 182798 186507 191057 192276 193264 194039 194638 195079 195391 195618 195783 195893 195965 MEADOW 132/11kV 80000 40000 67498 68313 72998 73910 74706 70304 70879 71358 71759 72083 72332 72519 72653 72740 72791 72813 72819 72821 72823 72823 72823 CROWN SANDS 44/6.6kV 20000 15000 6756 6197 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 ROBINSON DS 44/22kV 80000 40000 15839 13227 18995 19995 19995 23994 23994 23994 23994 23994 23994 23994 23994 23994 23994 23994 23994 23994 23994 23994 23994
SAR CANADA 44kV Traction N/A N/A 6377 6250 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100 7100
SAR CROWN 44kV Traction N/A N/A 3492 3643 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000
SAR LANGLAAGTE 44kV Traction N/A N/A 5844 5674 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844 5844
CROWN DS 132/44kV 180000 90000 40964 38030 49172 50171 50171 54167 55233 55326 55405 55478 55561 55664 55792 55959 56204 56577 57100 57742 58443 59136 59772
CROWN MINES 12 44/11kV 5000 0 2642 2496 300 300 300 300 2072 2233 2368 2492 2634 2811 3030 3314 3733 4370 5261 6354 7544 8718 9795
CROWN MINES 44/6.6kV 1247 1412 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500
FOURTEEN SHAFT 22/6.6/2.2kV 5000 2700
2500
1350 2222 1848 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300
SAR NEWTOWN 22kV Traction N/A N/A 5853 5930 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000
SAR BRAAMFONTEIN 22kV Traction N/A N/A 7919 5835 8000 9000 9000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ESKOM BRAAMFONTEIN 22/6.6kV 5000 2500 1764 880 1500 1500 1500 13655 13655.1 13655 13655 13655 13655 13655 13655 13655 13655 13655 13655 13655 13655 13655 13655
SAR BOOYSENS 22kV Traction N/A N/A 0 0 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000
SAR MARAISBURG 22kV Traction N/A N/A 1477 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726
BANTJES D.S. 44/22kV TRFR 1 N/A N/A 1477 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726 1726
CROWN DS TRFR 1 AND 4 132kV
FDR (CITY POWER) N/A N/A 12662 10189 11092 14879 18710 22587 26483 26795 27106 27416 27724 28028 28329 28626 28919 29168 29374 29538 29660 29741 29781 Firm KVA DIEPKLOOF MAIN 132/11kV 120000 80000 95236 98964 104030 107415 110361 113112 115654 67093 68124 68996 69709 70273 70713 71057 71327 71541 71656 71743 71806 71847 71871 MAPETLA MAIN 132/11kV 150000 110000 72340 76482 81310 84628 87371 89750 91691 93451 95078 96546 97805 98840 99655 100277 100743 101080 101307 101469 101585 101659 101693 MOFOLO MAIN132/11kV 140000 105000 96515 102769 104080 109481 114065 89858 92281 94077 95672 97045 98167 99057 99727 100228 100599 100879 101091 101268 101417 101532 101614 MOROKA MAIN 132/11kV 145000 105000 121979 127747 132882 136729 140372 143955 105115 107169 108993 110590 111933 113898 114723 115347 115805 116132 116367 116556 116706 116828 116920 ZOLA MAIN 132/11kV 185000 145000 133740 139059 151943 157299 163728 168873 116399 118033 119510 120688 121687 106246 106848 107315 107668 107914 108070 108185 108266 108326 108368 MEADOW 132/11kV 80000 40000 67498 68313 72998 73910 74706 70304 70879 71358 71759 72083 72332 72519 72653 72740 72791 72813 72819 72821 72823 72823 72823 ARMITAGE 132/11kV 80000 40000 0 0 0 0 0 25423 27191.7 28955.9 30598.5 32055.37 33286.05 34282.83 35059.23 35643.81 36077.8 36402.99 36651.71 36853.7 37014.26 37137.64 37226.1 JABULANI 132/11kV 120000 80000 0 0 0 3371.9 3387.6 3397.4 3399.72 44250.99 46519.9 48770.59 49999.18 50204.34 50388.43 50549.97 50690.14 50802.68 50888.05 50952.9 51004.56 51044.46 51071.57 NOMZAMO 132/11kV 120000 80000 0 0 0 0 0 0 0 55788.1 56991.1 58198.08 59428.08 60707.38 62071.48 62835.97 63519.76 64129.77 64649.24 65100.8 65388.74 65608.99 65766.02 PIMVILLE MAIN 132/11kV 120000 80000 0 0 0 0 0 0 0 0 0 53310.43 53762.34 54103.52 54349.89 54529.97 54665.39 54767.64 54841.92 54901.3 54946.28 54976.96 54989.64
PROTEA 132/11kV 80000 40000
THULANIVILLE 132/11kV 40000 0 0 0 0 0 0 0 25657 26316 26948 27145 27310 27443 27547 27622 27674 27713 27746 27774 27795 27812
TONKI 88/11kV 80000 40000 0 0 0 0 0 0 0 0 0 0 19769 19868 19943 19998 20035 20058 20070 20076 20080 20083 20085
Substation
T.R Khumalo (Montso) 54 The feeders highlighted in yellow are an indication that there will be capacity for additional load to be connected and will not be constrained after connection. The feeder in the orange indicates that it will be partially constrained (since connection on the feeder is above 60%). Reliability based planning method has be factored on this project to expand the distribution network which will increase the power supply as forecast indicates that Jabulani, Soweto area will not have power issues in the 10 years to follow the commissioning of the new Jabulani 132k/11kV.
Table 9: Load forecast for the new Jabulani 132/11kV Substation
4.4 Employee survey results
A questionnaire relative to the case study was sent to 12 respondents who work in Eskom Distribution. The population of respondents work within Asset Creation -Engineering departments which are: Network Planning, Network Engineering and design, Survey, Land & rights, Project Execution and Operation & Maintenance. Further, the participants are geographically based in
Network Conductor Normal Rating
o1_Bolani1_Fdr 300 XLPE Cu 8516 10041 5269 5521 5691 5872 6012 6116 6179 6222 6243 6253 6258 6263 6263 6263 6263
o1_Bolani2_Fdr 300 XLPE Cu 8516 10041 5269 5521 5691 5872 6012 6116 6179 6222 6243 6253 6258 6263 6263 6263 6263
o1_Bolani3_Fdr 300 XLPE Cu 8516 10041 5269 5521 5691 5872 6012 6116 6179 6222 6243 6253 6258 6263 6263 6263 6263
o1_CIVIC4_Majola_Fdr 185 XLPE Cu 6707 7914 5732 5785 5828 5862 5857 5850 5850 5847 5845 5844 5843 5843 5843 5843 5843
o1_Mvulani1_Fdr 300 XLPE Cu 8516 10041 4385 4675 4806 4992 5186 5300 5373 5419 5449 5468 5480 5491 5498 5504 5504
o1_Mvulani2_Fdr 300 XLPE Cu 8516 10041 4385 4675 4806 4992 5186 5300 5373 5419 5449 5468 5480 5491 5498 5504 5504
o1_Mvulani3_Fdr 300 XLPE Cu 8516 10041 4385 4675 4806 4992 5186 5300 5373 5419 5449 5468 5480 5491 5498 5504 5504
o1_ZON1_Tswali_Fdr 185 XLPE Cu 6707 7914 4147 4342 4148 4159 4360 4349 4344 4341 4339 4338 4337 4337 4337 4336 4336
o1_ZON2_Vakalisa_Fdr 185 XLPE Cu 6707 7914 4087 4075 4080 4091 4084 4075 4071 4069 4067 4067 4066 4066 4066 4066 4066
o1_ZON3_Sobhuza_Fdr 185 XLPE Cu 6707 7914 2422 2531 2416 2420 2534 2528 2525 2523 2522 2522 2521 2521 2521 2521 2521
o1_ZOL2_Shenge_Fdr 185 XLPE Cu 6707 7914 3781 3818 3804 3814 3848 3991 4027 4044 4048 4047 4047 4047 4047 4047 4047
Jabulani Installed Substation Capacity 80000 80000 80000 80000 80000 80000 80000 80000 80000 80000 80000 80000 80000 80000 80000
Jabulani Firm Substation Capacity 40000 40000 40000 40000 40000 40000 40000 40000 40000 40000 40000 40000 40000 40000 40000
Jabulani Coincidence at MV Bus Bar 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98
Jabulani Undiversified Maximum Demand 49131 51139 51767 52938 54277 55041 55473 55747 55897 55981 56028 56076 56097 56114 56114
T.R Khumalo (Montso) 55 Johannesburg and service the Soweto –Jabulani area mentioned in the case study. The participants were selected based on their ability to supply information on improving the network performance of the sampled network.
The questionnaire was generated using Microsoft word format. The response from the participants was good and received back in time since the questionnaire generated data that supported the result analysis in the study as it was systematic, orderly and categorised to this research statistics.
The questions were close ended making it easy to analyse and present the results in percentages.
The participants had to tick the correct answer based on their input and experiences of the distribution network reliability in Soweto.
4.5 Response to the questionnaire.
Out of the 15 hard copies of the questionnaires which were sent out, only 10 were answered and received back by the researcher. The distribution of the respondents is indicated in figure 22 below:
Figure 15: Respondents Distribution
30% are from Network Planning , 10% form Survey, 20% from Project execution, 10% from Operation & Maintenance, 10% from Land & Rights and 20% from Network engineering & Design.
The participants were Eskom employees that are working on the Soweto distribution network.
30%
T.R Khumalo (Montso) 56 4.5.1 Response graphs and discussion
The answers from the respondents are analysed and presented in the graphs below:
What is your Occupation?
Figure 16: Occupancy of participants
The participant’s occupancy is as follows: 30% are engineering managers, 50% Professional engineers and 20% are Technicians. This indicates that the employees are qualified individuals were identified to participate in the survey.
Are you part of the reliability planning/maintenance team?
Figure 17: Response on being part of the Reliability planning/maintenance team.
0%
T.R Khumalo (Montso) 57 100% of the respondents are part of the reliability team. This indicates that the respondents are knowledgeable on the reliability challenges presented in the case study.
Is network reliability management part of your work outputs?
Figure 18: Respondents answers on being involved in distribution reliability outputs.
80% answered yes and 20% answered no. This means that the majority of the participants are directly involved in the reliability project of Soweto and thus have to ensure that the reliability of the network is at its best.
Are you aware of any power interruptions in Soweto?
T.R Khumalo (Montso) 58 Figure 19: Respondents answers on power interruptions in Soweto.
100% of the respondents are aware of power outages in Soweto. This indicates that the distribution network is experiencing failures at sometimes due to different causes and that improving the performance of the network remains a priority. Strengthening the network with adding better power infrastructure such as indicated in the case study will help with minimising the power failures in the area concerned.
Have you experienced any power outages in the last 3 months?
Figure 20: Respondents experience of power outages in the past 3 months.
0%
T.R Khumalo (Montso) 59 This question was asked in general since not all the participants reside in Soweto, but they all do reside in other parts of the greater Johannesburg. The distribution network of Johannesburg is integrated hence 90% indicated that they have experienced power outages in the past 3 months.
Only 10% indicated that they had power outages in the last 3 months. This indicates that distribution power reliability challenges still exist in Johannesburg and we haven’t reached the ideal network performance of having zero power outages.
Do you know what SAIFI is?
Figure 21: Respondents knowledge of SAIFI.
The results indicate 100% of the participants know what SAIFI is. Each department that the participants work in, have an input on the performance of the network. SAIFI is monitored and the SAIFI results indicate the level of performance of the distribution network. This will determine the improvements that need to be done to the network.
Are the standards used in Eskom Distribution to improve reliability effective?
0%
T.R Khumalo (Montso) 60
Figure 22: Answer on the effectiveness of Eskom Reliability standard
The results indicate that 80% of the participants say the Eskom distribution reliability standards 240-76613395 to improve reliability are effective, where as 20% of the participants responded by saying that they are not effective. This means that the implementation of the reliability standard has improved reliability of the network but since there work is in progress in the whole of Soweto, the model used in the case study to improve the performance must continue since we still experience unplanned power failure due to events such as theft.
Would you say the reliability of the distribution network is improving?
Figure 23: Response on the improvement of the Soweto distribution network.
The results indicate that 70% say the Soweto distribution network is improving, whereas 30% say that the network is not improving. During the period of the research, several protests in the sampled area of the distribution network were reported. Investigations indicated that these were due to the
0%
T.R Khumalo (Montso) 61 power failures experienced by the residents and call outs received by the maintenance team. The frequency of theses failures have decrease since the implementation of the preventative method done in the network and quick response in restoring power to minimise the time that the customer is without power .
How would you rate the reliability of the power network in Soweto?
Figure 24: respondent rating on the Soweto distribution Network
The Objective of the reliability team is to have a network that perfoms optimally with minimum
The Objective of the reliability team is to have a network that perfoms optimally with minimum