Assessment of Electric Power Distribution Feeders Reliability: A Case Study of Feeders that Supply Ede Town

Assessment of Electric Power Distribution Feeders Reliability: A Case Study of Feeders that Supply Ede Town Johnson, Daniel Ogheneovo Department of Electrical and Electronic Engineering Federal Polytechnic, Ede, Osun State pleasantdaniel@yahoo.com Abstract Feeders are circuits that carry electric power to substations. Its performance and 24-hour a day consistent delivery of energy is central in Reliability of electric power distribution systems. This paper assesses and quantifies the Reliability of electric power distribution feeders using feeders that supply Ede town (in Osun State, Nigeria) as a case study. Data on daily power interruption for a period of twelve months on the feeders (a 33kV and two 11kV) were collected and mathematically analysed to obtain Reliability indices. The results were compared with average of some towns/cities in advanced countries of the world. The result confirms that no single Reliability index gives the true picture of reliability 1.0 Introduction Electric Power Distribution System is very important infrastructure because it delivers electricity to users either domestic or commercial consumers. Its continuous and reliable performance is essential to nation building and citizens way of life [1]. Failure of Power Distribution Systems has direct and indirect negative effect on daily activities and the social-economy well-being of users. The ever increasing use of generating set, inverter, solar battery and other alternatives at the consumer terminal are effort to improve the availability of electric power supply. It is a well known fact that all these alternatives are by far more costly than power utility services even with the so called crazy bill. Hence, the need to ensure power supply services reliability. As a consequence of ever increasing power demand (due to more residential buildings on new site and population growth), some distribution lines are more loaded than was planned when they were built [2]. With the increased loading of long distribution lines, many problems such as fault and system overloading can become a major power performance of power system. The total outage duration (SAIDI) for the period is 1884.99 hours, outage frequency (SAIFI) is 755 times, the percentage Availability (ASAI) is 78.48. This is too far from the internationally accepted standard values (IASV) of 2.5 hours for SAIDI, 0.01 SAIFI and 99.8 ASAI. Based on these values, power supply services in the town and by extension in the country, is still very unreliable and unpredictable despite the unbundling two years ago. Keywords: Reliability, Reliability Indices, Distribution Feeder distribution limiting factor. Hence, the reliability of such power networks becomes a serious issue. Recently the institutional changes in the electric sector, such as the progressive deregulation or the electric utilities privatization, with the purpose of creating better electric services, have given another degree of importance to the continuity of service [3]. Therefore the reliability of the systems has become an important issue during design process because of increasing dependence of our daily lives and schedule on satisfactory functioning of the (power) systems [4]. The most common methods of assessing electric power distribution network reliability deal with percentage availability, number of power outage, the outage frequency, the duration of those outages over a specified time. This research assesses and quantifies the Reliability of electric power distribution services to consumers using Ede town in Osun State, Nigeria as a case study. 2.0 Methodology With due official permission, outage data for 12 months duration were collected from Ibadan Electricity Distribution Company, which is one of the eleven 167

companies providing electricity distribution services in Nigeria. The company is the electricity utility serving the whole of Ogun, Osun, Oyo, Kwara and small part Ekiti, Kogi, and Niger States in Nigeria. The data collected include: 1. Numbers of feeders (33, 11/0.415KV) 2. Numbers of outages on each feeders 3. Duration of each outages 4. Causes of outages 5. Number of consumers on each feeder 6. Equipment in the reserve (transformer, feeder pillar, fuse, pole, overhead cables etc) 7. Age of distribution equipment/item These data are taken to be accurate because there is 24- hour a day monitoring of the feeders in the control room by staff. Once there is outage, it is recorded immediately in the daily log book with date and the exact time it occurs. So also when the supply is restores, the exact time is recorded. The date with outage time and restoration time is then later entered into computer system for record purpose. I was given copy of computer print out as well as the photocopy of the log book. My comparison showed accuracy of the data. The data which covered from March 2014 to February 2015, are on the three feeders (33kV and two 11kV lines) supplying the town. Reliability indices like SAIDI, CAIDI, CAIFI and ASAI are use to assess reliability performance of the distribution feeders. 3.0 Background of the Study The research is carried out on Power Distribution Infrastructure and Services in Ede town, Osun State. The town is selected because it is one of the true representative models of electric power supply system in average Nigeria town. The town is supplied with 33kV (called Ede waterworks) from National Grid Control Centre, Oshogbo. It is further step down by 15MVA transformer to 11kV at an Injection Substation from which two 11kV feeders (named Ede township feeder and cottage feeder) are radiated out to feed consumers. At various points (more than 100) in the town, the voltage is further step-down to 415V which is the utility voltage. Majority of the city population are civil servants, students, traders and farmers. The town hosts a prestigious Federal Polytechnic, a very popular private Redeemer University, another private Adeleke University, big Nigeria Army Barrack (Engineering Regiment), a pharmaceutical company, Cocoa Processing industry, State-owned Hospital, a big productive modern poultry farm, the State Water Cooperation, NYSC camp, Nigerian Immigration Office, Nigeria Custom Office and many fuel stations. These are major consumers in Ede that need electricity for their daily activities apart from thousands of individual residential customers. 4.0 Reliability and Reliability Indices Reliability is commonly defined as the probability that an item will perform a required function without failure under stated condition for a stated period of time. However, for power system, Reliability is defined in many ways. This is true particularly for electric power distribution systems. According to Bhavaraju et al. 2005, Reliability of an electric power system is defined as the probability that the power system will perform the function of delivering electric energy to customers on a continuous basis and with acceptable service quality [5]. Reliability with regard to Power Distribution Systems is the probability that an item or collection of items (feeders, transformer, insulation, feeder pillar, distribution line, poles, fuse, isolator, connections etc) will perform satisfactorily, under all conditions during a given period of time. Quantitatively, Reliability is the probability of success or availability of supply [6]. Another approach taken is to define reliability through indices. Reliability indices are parametric quantity use to assess the performance levels of electrical power distribution systems so as to make it suitable for scientific analysis [7] [8]. Reliability indices typically consider such aspects as the number of customers interrupted, the duration of the interruption measured in minutes or hours and the frequency of interruptions. For example, the reliability of a distribution system is said to be described by a complete set of indices such as SAIFI (frequency of outage), SAIDI (duration of outage), CAIDI (required time to restore supply), ASAI (percentage availability). Other are MAIFI, ASIFI, ASIDI etc. One of these indices is not sufficient to completely measure 168

reliability of power supply since each focus on specific aspect of reliability. In this research, four are used to assess and quantify Reliability of electric power supply in Ede town. 5.0 Analysis and Discussion of Result With reference to table 1a on page 9, the following indices are mathematically and analytically obtained. 5.1 SAIDI is System Average Interruption Duration Index. It is a measure of duration. It measures the number of minutes/hours over a period of time (day, month or year) that the average customer is without power. It is given by = Summation of Customer-hour Total number of registered customer (2) Where N i is the number customer affected by the outage N t is the total number of registered customer, and d is the duration of the outage From table 1a on Appendix, Σ( N xd i i ) = 2,900, 121 N = 25, 857 t (1) customer per sustained interruption. It provides the average amount of time a customer is without power per interruption. It is given by the sum of customer interruption durations divided by the total number of customer interrupted. = Summation of Customer-hour Total number of customer interrupted (4) 2,900,121 = = 4.15 hours 698139 For every interruption, it takes an average of 4.15 hours to restore supply in this month. In other words, an average outage last for 4.15 hours for the month of March. 5.3 SAIFI is System Average Interruption Frequency Index. SAIFI is a measure of numbers of times (frequency). SAIFI is the average number of time that a consumer experiences an interruption of supply in a given period of time (day, month or year). SAIFI is dimensionless (3) 2,900,121 = 25,857 SAIDI = 112.16 hours This means that for the month of March, the 33KV feeder was interrupted for a total period of 112.16 hours. This is an average 3.62 hours in a day in this month. If there is no interruption at 11KV which is very unlikely and no fault at secondary distribution level as well as at the components involved (cables, transformer, fuse, poles etc), then all the customers on this feeder cannot have supply more than 24-3.62= 20.38 hours on a daily average basis for this month. Possible outages at 11KV feeder that get supply from 33KV and interruption from likely fault that may occur at 415V level will further reduced the duration of power supply in the month or increase the outage duration in the month for an average customer. 5.2 CAIDI means Customer Average Interruption Duration Index. It is also a measure of time. CAIDI is the average time needed to restore service to the average = Total Number of customers interrupted Total number of registered customer (6) 698139 = = 27 times 25,857 So for the month of March, interruptions on 33KV line occur 27times; an average of 0.9 times per day SAIDI CAIDI (5) It is interesting to note that SAIFI = (7) = 112.16 4.15 = 27.0265 times 5.4 ASAI is Average System Availability Index. It is the same as System Reliability Index SRI. It is a measure of the overall reliability of the system. It represents the percentage of time during the year (8760hours) or month 169

(720hours) or day (24hours) that the average customer has power supply. ( ) ASAI = 100 Ni xd 1 (8) N t xt 2,900,121 = 100 1 25,857x744 =84.92% or = Customer- hour service Availability Customer Hour service demand (10) 24x31 112.16 24x31 = = 84.92% The indices for other months on these feeders are similarly obtained and the result tabulated in the table 1 below This means electric power supply is available in 84.92% of the time. Alternatively ASAI is given by: = Customer Hour on Total numbers of hours in the duration (9) Table 1: Reliability Indices of the three feeders Months SAIDI(hours) Water Work Township Cottage 33kV 11kV 11kV CAIDI ( hours) WaterWork Township Cottage 33kV 11kV 11kV SAIFI WaterWork Township Cottage 33kV 11kV 11kV ASAI(%) WaterWork Township Cottage 33kV 11kV 11kV March 112.16 58.73 108.32 4.15 2.67 5.42 27 22 20 84.92 92.11 85.44 April 104.76 29.92 89.04 2.99 2.30 4.05 35 13 22 85.45 95.84 89.63 May 95.64 57.06 33.06 2.39 2.17 1.84 40 21 18 87.15 92.33 95.56 June 104.93 70.25 56.33 2.56 3.19 4.33 41 22 13 85.43 90.24 92.18 July 118.25 36.73 63.57 2.63 2.62 3.18 45 14 20 84.11 95.06 93. 85 Aug. 49.15 37.20 31.80 1.97 3.10 2.27 25 12 14 93.39 95.00 95.75 Sept. 93.48 65.54 111.02 2.40 3.28 3.70 39 20 30 87.02 90.90 84.51 Oct. 95.05 111.03 106. 89 1.90 3.08 2.97 50 36 36 87.22 85.08 85.63 Nov. 85.57 67.89 137.42 1.94 2.95 3.44 44 23 40 88.12 90.57 80. 91 Dec. 33.52 68.90 86.67 1.08 2.46 2.02 31 28 43 95.49 81.57 88.35 Jan. 23.70 64.65 72.10 1.03 2.69 2.33 23 24 31 96.81 91.31 90.31 Feb. 59.61 134.97 128.32 1.22 3.37 2.62 49 40 49 91.12 79.92 80.90 Year Value 975.82 802.87 1,024.54 2.19 2.82 3.18 449 275 336 88.86 90.83 88.30 The table above gives the values of reliability performance (reliability indices) of each of the feeders. The two 11kV are products of the 33kV stepped down for further distributions. Whatever happen on the 33kV (outage) affect the two 11kV feeders. The indices are first calculated on each feeder so as to study and assess the performance of each feeder line. The next table factored in the interruption on the 33kV feeder on each of the two 11kV feeder. In other words, table 2 gives the true reliability indices as experienced by consumers on the two 11kV feeders. In table 1, the township feeder in the month of March appears to be available 92.11% of the time. This is not the true picture. As long as there is no supply on 33KV, there will not be supply on any of the two 11kV lines since they are being supplied from the 33KV. Therefore, the first 11kV (named township feeder) is only available 92.11% of 84.92% i.e. (0.9211x 0.8492) = 78.22% of the time. The same procedure applies to the second feeder. It is available 85.44% of 84.92% = 72.56%. That is percentage availability of township feeder multiply 170

by percentage availability of Waterworks feeder (the 33kV being the source of the 11kV). The same principle applies to SAIDI, CAIDI and SAIFI. For SAIDI, the total outage duration in the month of March for township feeders is 112.10 + 58.73 = 170.89 hours. For cottage feeder, the true SAIDI is 112.16 + 108.32 = 220.48 hours in the month under consideration. That is outage duration of 33kV plus the outage duration on any of the 11kV gives the total true outage duration on the particular 11kV since, it takes its source from the 33kV. With the outage effect of 33kV waterworks feeder on the two 11kV, the average period of each outage in March (CAIDI) for the township feeder is 3.41 hours and 4.79 hours for the cottage feeder. The frequency of outage i.e. the number of outages in a month for the township feeder is 49 and 47 for the cottage feeder. Note that a reduction of numbers of outage (SAIFI) does not necessarily means reliability improvement. Looking at the two 11KV feeders as an example in table 1, the first feeder SAIFI is 22 with total Table 2: Reliability Indices of Township and Cottage Feeder Months SAIDI (hours) SAIFI (hours) ASAI (%) Township Feeder Cottage Feeder Township Feeder Cottage Feeder Township Feeder Cottage Feeder March 170.89 220.48 49 47 78.22 72.56 April 134.68 193.80 48 57 81.90 76.50 May 152.70 128.70 61 58 80.47 83.28 June 175.18 161.26 63 54 77.09 78.75 July 154.98 181.82 59 65 79.95 78.94 August 86.35 80.95 37 39 88.72 89.40 September 159.02 204.50 59 69 79.10 73.60 October 206.08 201.94 86 86 74.21 74.69 November 154.46 222.99 67 84 78.81 71.30 December 102.42 120.19 59 74 77.89 84.37 January 88.35 95.80 47 54 88.40 87.43 February 184.58 187.93 89 98 72.82 73.77 Year Value 1,769.61 2,000.36 724 785 79.79 77.16 Year Average 1884.99 754.5 78.48 duration of (SAIDI) of 58.73hours while the second 11KV feeder is 20 with total duration (SAIDI) of 108.32. The number of interruption is less in cottage feeder but with much longer period of unavailability while for the township feeder the number of outage is more but with much less duration. This is why a single index is not sufficient to quantify reliability of distribution system. The bar charts of figure 1, 2 and 3 below show the graphical representation of SAIDI, SAIFI and ASAI of the township and cottage feeder. Negative changes in values of the indices indicate that reliability is improving while positive changes of indices shows the reliability is getting worse except for ASAI where reverse is the case. 171

each month from November to the following January but then went worse in February. So there is no steady power supply improvement. No clear pattern or trend emerges from the analysis of the reliability indices over the year. In other words, It cannot be said whether the reliability is improving or not. However it is certain that the values of the reliability indices are too far from the internationally accepted standard values (IASV) of 2.5 hours for SAIDI, 0.01 SAIFI and 99.8 ASAI [9]. Based on these, power supply services in the town and by extension in the country, is still very unreliable and unpredictable despite the unbundling two years ago. From the three figures, for customers on the township feeders, reliability was fairly better in April than March, May, June and July; while for customer on cottage feeder, reliability was better in May than march, April, June and July. For the two feeders Power availability significantly improved in August over the previous five months but went worse in September and October; and then improved 6.0 Comparison of Reliability Indices While attempt is made in table 3 below to compare power supply reliability in Ede town (Nigeria) to other countries cities average, it should be said that interruption indices are not homogeneous and that comparisons between countries and companies should be made with caution. The input data sources vary tremendously and there are major differences in approach to basic calculation methods from one utility to another [10]. Therefore, the values available should be taken with caution and benchmarking these values could lead to inaccurate conclusions [11] However, there is no gainsaying that reliability indices in these countries are far much better than power system reliability in Nigeria. Hence, these values are providing a point of reference from which to compare reliability 172

performance. The table below gives the national mean values of interruption duration, frequency of outage and percentage availability in a year. Table 3: Reliability indices of some countries Countries SAIDI SAIFI ASAI (%) (hour) Austria 0.59 0.59 99.99 Barcelona City 1.79 2.28 99.98 Belgium 0.70 0.90 99.99 Finland 3.04 4.06 99.97 France 0.89 1.21 99.99 Germany 0.62 0.27 99.99 Great Britain 1.17 0.77 99.99 Ireland 3.93 1.34 99.97 Italy 3.38 3.83 99.96 Norway 3.63 2.73 99.96 Spain 2.55 2.98 99.97 Sweden 1.65 2.07 99.98 Netherlands 0.46 0.38 99.99 USA 1.36 0.97 99.98 Ede Town (Nigeria) 1884.56 754.5 78.48 Sources: 1. International Reliability Analysis in Distribution Networks (for European countries) [11] 2. Annual Report Utility Reliability Indices, New Mexico Public Regulation Commission (USA) It should be pointed out as well that Ede town alone is not sufficient to represent the power system performance in Nigeria. To get acceptable values, we need to get reliability indices of other town spread across the nation and then find the average. However, for now, there is no values of reliability indices of most town even though work on electric power distribution reliability of some Nigeria towns abound, I am not aware of anyone that specifically quantify, calculate or estimate reliability of power distribution system of any town in term of values. 7.0 Recommendation and Conclusion The result of this research is true reflection of power distribution in the study area because the data use is accurate. The value of reliability obtained from the analysis of the feeders show that outage duration, outage frequency and time taken to restore supply are very high. There is the need to drastically reduce these indices values (SAIDI, CAIDI and SAIFI) thereby increases percentage availability (ASAI). This can be achieved by: (i) Prompt response by technical staff to clear fault on the distribution line and systems (ii) Better feeder design and feeder automation. Better design will provide robust and more than one path of supply while automation will help in identifying and locating fault point on the feeder length. (iii) ly maintenance of distribution lines is necessary, particularly trimming and cutting of trees which causes earth fault by bridging line to earth or bridging two or three phases (iv) Effective means of preventing snakes and lizards from climbing up feeder lines should be effected. Acknowledgement The author is grateful to the Management and Staff of Ibadan Electricity Distribution Company IBEDC, Ede, for their cooperation in providing outage data and technical information required to carry out this research. References [1] Thomas Robert J., Managing Relationships Between Electric Power Industry Restructuring and Grid Reliability, 2005. Availableat:www.pserc.wisc.edu/documents/ / thomas_grid_reliability_2005.pdf [2] Varsha Vishwakarma and Nitin Saxena, Mitigation of Power Quality Problems by D-Statcom, International Journal of Electrical and Electronics Research Vol. 2, Issue 2, pp: (158-165), April - June 2014. Available at:www.researchpublish.com [3] Leonel Carvalho, Using Evolutionary Swarms (EPSO) in Power System Reliability Indices Calculation, MSc Thesis, Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, Portugal, 2008. [4] Komolafe O.A., Lecture Note on Reliability, Maintainability and Management of Engineering Systems, EEE 666, Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, 2014. 173

[5] Bhavaraju M.P., Billinton R., Brown R.E., Endrenyi J., Li, W., Meliopoulos, A.P.; and Singh, C., IEEE Tutorial on Electric Delivery System Reliability Evaluation, IEEE Power Engineering Society (PES), Publication 05TP175: 39-51, 2005. [6] Babla A.C., Electric Power Distribution, (5th ed.), New Delhi: Tata McGraw-Hill, 2009. [7] G.A. Ajenikoko and O.O. Olaluwoye, A Generalized Model for Electrical Power Distribution Feeders Contributions to System Reliability Indices, International Journal of Engineering vol. 3, Issue No.11, pp : 640-644, 2014. [8] I.K. Okakwu, and E. S. Oluwasogo, The Reliability Study of 11-kV Distribution Feeders: A Case Study of Idi-Araba PHCN Injection Substation, IOSR Journal of Electrical and Electronic Engineering vol. 10, Issue 1 (Jan Feb. 2015), pp 58-65, 2015. [9] R. Akinwumi, Development of Method of Improving the Reliability of Voltage Distribution Supply Network, MSc Thesis, Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, 2012. [10] J. Kueck, B. Kirby, P. Overholt and Markel L. Measurement Practices for Reliability and Power Quality, Oak Ridge National Laboratory Oak Ridge, Tennessee 37831-6285, 2004. [11] A. Sumper, A. Sudrià and Ferrer F., International Reliability Analysis in Distribution Networks, Centre d Innovació Tecnològica en Convertidors Estàticsi Accionaments.CITCEA, Universitat Politècnica de CatalunyaAv. Diagonal, 647.Pavelló A; 08028 Barcelona, 2006. Table 1a: 33kV Outage Data for March (Source: IBEDC, Ede) Date Start Restored Duration (hour) Numbers of Customers Customer-Hour 04 Mar 8:45 9:30 0.75 25857 19392.75 05 Mar 18:30 0:00 5.5 25857 142213.5 06 Mar 0:01 10:12 10.18 25857 263224.3 07 Mar 17:47 21:25 3.63 25857 93860.91 07 Mar 22:26 0:00 1.57 25857 40595.49 08 Mar 0:01 9:43 9.7 25857 250812.9 09 Mar 10:28 11:19 0.85 25857 21978.45 09 Mar 16:12 19:12 3 25857 77571 10 Mar 15:31 19:50 4.32 25857 111702.2 12 Mar 14:55 17:11 2.27 25857 58695.39 13 Mar 16:48 17:44 0.93 25857 24047.01 15 Mar 10:42 12:13 1.52 25857 39302.64 16 Mar 13:49 15:58 2.15 25857 55592.55 16 Mar 17:57 0:00 6.05 25857 156434.9 17 Mar 0:01 13:46 13.75 25857 355533.8 17 Mar 19:49 22:03 2.23 25857 57661.11 17 Mar 22:59 0:00 1.02 25857 26374.14 18 Mar 0:01 7:22 7.35 25857 190049 19 Mar 6:19 6:59 0.67 25857 17324.19 19 Mar 18:49 20:06 1.18 25857 30511.26 19 Mar 20:20 22:40 2.33 25857 60246.81 20 Mar 10:41 20:33 9.87 25857 255208.6 23 Mar 9:14 12:39 3.42 25857 88430.94 174

23 Mar 17:49 0:00 6.18 25857 159796.3 24 Mar 0:01 8:59 8.97 25857 231937.3 24 Mar 16:27 17:00 0.55 25857 14221.35 27 Mar 16:00 18:13 2.22 25857 57402.54 SUM 112.16 698139 2900121 Table 1b: 11kV (Township) Outage Data for March (Source: IBEDC, Ede) Date Start Restored Duration (hour) Numbers of Customers 04 Mar 19:28 20:34 1.1 6597 7256.7 Customer-Hour 09 Mar 14:04 17:40 3.6 6597 23749.2 09 Mar 19:52 23:05 3.22 6597 21242.34 11 Mar 17:01 18:40 1.65 6597 10885.05 11 Mar 19:19 20:03 0.73 6597 4815.81 13 Mar 19:26 20:15 0.82 6597 5409.54 19 Mar 16:03 20:01 3.97 6597 26190.09 19 Mar 20:08 22:41 2.55 6597 16822.35 20 Mar 20:38 0:00 3.37 6597 22231.89 20 Mar 23:03 0:00 0.95 6597 6267.15 21 Mar 0:01 8:50 8.82 6597 58185.54 21 Mar 19:28 0:00 4.53 6597 29884.41 22 Mar 0:01 0:22 0.35 6597 2308.95 22 Mar 2:28 5:28 3 6597 19791 22 Mar 23:05 0:00 0.92 6597 6069.24 23 Mar 0:01 4:48 4.78 6597 31533.66 25 Mar 21:01 23:25 2.4 6597 15832.8 27 Mar 6:25 8:02 1.62 6597 10687.14 30 Mar 5:01 8:10 3.15 6597 20780.55 30 Mar 2:31 4:32 2.02 6597 13325.94 30 Mar 19:28 23:00 3.53 6597 23287.41 30 Mar 20:12 21:51 1.65 6597 10885.05 SUM 58.73 145134 387441.8 Table 1c: 11kV (Cottage) Outage Data for March (Source: IBEDC, Ede) Date Start Restored Duration (hour) Numbers of Customers Customer-Hour 04 Mar 22:38 0:00 1.37 5993 8210.41 05 Mar 0:00 7:11 7.18 5993 43029.74 09 Mar 19:52 21:05 1.22 5993 7311.46 10 Mar 12:26 19:52 7.43 5993 44527.99 175

11 Mar 5:02 12:58 7.93 5993 47524.49 12 Mar 6:07 17:12 11.08 5993 66402.44 18 Mar 21:00 0:00 3 5993 17979 19 Mar 0:00 7:01 7.02 5993 42070.86 19 Mar 14:44 20:02 5.3 5993 31762.9 19 Mar 0:00 7:01 7.02 5993 42070.86 21 Mar 0:01 5:48 5.78 5993 34639.54 23 Mar 4:39 8:55 4.27 5993 25590.11 24 Mar 21:05 0:00 2.92 5993 17499.56 25 Mar 0:01 7:55 7.9 5993 47344.7 25 Mar 21:01 23:25 2.4 5993 14383.2 26 Mar 6:24 9:24 3 5993 17979 27 Mar 5:17 6:26 1.15 5993 6891.95 30 Mar 2:01 5:02 3.02 5993 18098.86 31 Mar 14:54 14:54 17.16 5993 102839.9 31 Mar 21:50 0:00 2.17 5993 13004.81 SUM 108.32 119860 649161.8 176