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rms voltage threshold; possible values are 140, 120, 110, 90, 80, 70, 50, and 10 number of customers experiencing short-duration voltage deviations with magnitudes above X percent for X 100 or below X percent for X 100 due to measurement event i total number of customers served from section of system to be assessed
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Notice that SARFI is defined with respect to the voltage threshold x For example, if a utility has customers that are only susceptible to sags below 70 percent of nominal voltage, this disturbance group can be assessed using SARFI70 The eight defined threshold values for the index are not arbitrary They are chosen to coincide with the following: 140, 120, and 110 Overvoltage segments of the ITI curve 90, 80, and 70 Undervoltage segments of ITI curve 50 Typical break point for assessing motor contactors 10 IEEE Standard 1159 definition of an interruption An increasing popular use of SARFI is to define the threshold as a curve For example, SARFIITIC would represent the frequency of rms variation events outside the ITI curve voltage tolerance envelope Three such curve indices are commonly computed: SARFICBEMA SARFIITIC SARFISEMI
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Downloaded from Digital Engineering Library @ McGraw-Hill (wwwdigitalengineeringlibrarycom) Copyright 2004 The McGraw-Hill Companies All rights reserved Any use is subject to the Terms of Use as given at the website
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Power Quality Benchmarking 336 Eight
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This group of indices is similar to the System Average Interruption Frequency Index (SAIFI) value that many utilities have calculated for years SARFIx, however, assesses more than just interruptions The frequency of occurrence of rms variations of varying magnitudes can be assessed using SARFIx Note that SARFIx is defined for short-duration variations as defined by IEEE Standard 1159 There are three additional indices that are subsets of SARFIx These indices assess variations of a specific IEEE Standard 1159 duration category: 1 System Instantaneous Average RMS (Variation) Frequency Index (SIARFIx) 2 System Momentary Average RMS (Variation) Frequency Index (SMARFIx) 3 System Temporary Average RMS (Variation) Frequency Index (STARFIx)
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833 SARFI for the EPRI DPQ project
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Table 81 shows the statistics for various forms of SARFI computed for the measurements taken by the EPRI DPQ project These particular values are rms variation frequencies for substation sites in number of events per 365 days One-minute temporal aggregation was used, and the data were treated using sampling weights This can serve as a reference benchmark for distribution systems in the United States
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834 Example index computation procedure
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This example is based on actual data recorded on one of the feeders monitored during the EPRI DPQ project1 This illustrates some of the practical issues involved in computing the indices
TABLE 81
SARFI Statistics from the EPRI DPQ Project*
SARFI90 SARFI80 0000 5594 22813 28729 66260 103405 SARFI70 0000 0000 12126 18422 51000 70535 SARFI50 0000 0000 5165 8926 27037 56311 SARFI10 0000 0000 1525 3694 13519 35689 SARFICBEMA 0000 5316 25465 33293 71413 149488 SARFIITIC 0000 2791 18765 25390 51500 140768 SARFISEMI 0000 2362 13619 18535 38238 140768 0000 11887 43987 56308 135185 207644
Minimum CP05 CP50 Mean CP95 Maximum
*Submitted for IEEE Standard P15648
CP05, CP50, and CP95 are abbreviations that indicate that the value exceeds 5, 50, and 95 percent of the samples in the database For example, 50 percent of the sites in the project had more than 18765 events per year that were outside the ITI curve voltage tolerance envelope (SARFIITIC)
Downloaded from Digital Engineering Library @ McGraw-Hill (wwwdigitalengineeringlibrarycom) Copyright 2004 The McGraw-Hill Companies All rights reserved Any use is subject to the Terms of Use as given at the website
Power Quality Benchmarking Power Quality Benchmarking 337
First, one must know how many customers experience a voltage exceeding the index threshold for each rms variation that occurs Obviously, every customer will not be individually monitored Consequently, one must approximate the voltage experienced by each customer during a disturbance This is accomplished by segmenting the circuit into small areas across which all customers are assumed to experience the same voltage Obviously, the smaller the segments, the better the approximation One method of determining voltages for many circuit segments based on a limited number of monitoring points is power quality state estimation A special section (87) is included on this topic later State estimation provides pseudomeasurements for those segments not containing a measuring instrument Such state estimation requires a moderately detailed circuit model and known monitored data Without the pseudomeasurements provided by state estimation, the number of physical monitoring locations becomes the number of constant-voltage segments upon which the indices that are calculated This is referred to as monitor-limited segmentation (MLS) and results in only a few segments per circuit Although the calculated index values are less accurate, MLS still yields indices that are informative Figure 83 illustrates the three MLS segments for the example calculation feeder corresponding to the three power quality monitors, M1, M2, and M3 The exact number of customers served from each MLS
Figure 83 Circuit for example rms varia-
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