基于SCADA数据库参数估计进行传输线测量毕业论文外文翻译

1、外文翻译原文：measurement of transmission line parameters from scada data g. l. kusic and d. l. garrison abstract-transmission line equivalent circuit parameters are often 25% to 30% in error compared to values measured by the scada system. these errors cause the economic dispatch to be wrong, and lead to

2、increased costs or incorrect billing. the parameter errors also affect contingency analysis, short circuit analysis, distance relaying, machine stability calculations, transmission planning, and state estimator analysis. an economic example is used to demonstrate the affect of transmission line erro

3、rs. scada measurements from several utilities are used to compute the real world value of the transmission line parameters. state estimation with the estimated parameters is compared to the computations using the theoretical values. index termsscada measurements, state estimation, transmission line

4、parameter estimation i. introduction utilities in most instances use theoretical values for line parameters calculated from ideal line geometry such as height of conductor above flat, constant resistance earth. earth resistivity is variable with terrain. conductor sag effects are impossible to estim

5、ate over hilly terrain. usually shield wires are grounded at each tower instead of floating over the entire line length. line resistance varies with current, the ambient temperature and wind effects. an outage is required to measure the line charging equivalent capacitance, only if one-sided excitat

6、ion does not cause too much voltage rise on the open-circuit end. construction of new parallel lines with mutual coupling, affects old database values. the series reactance of a transmission line is rarely measured, so the value used is for an ideally transposed line. lines are not transposed becaus

7、e of the added construction cost to mechanically alter positions of the conductors with respect to the support poles every 1/3 the distance. with all of these variations from ideal conditions, and few real measurements, utilities often have as much as 25% to 30% error in their database parameters co

8、mpared to the real world values. utilities use mw and mvar metering for revenue. transmission line losses are usually less than 3% of the total generation. however, line parameters from a theoretical database are used to calculate loss coefficients, or determine the incremental loss factors, in orde

9、r to set the dispatch point for generators (output power). if accurate values of line parameters re-allocate the generator powers, and reduce transmission losses by 0.1%, this translates into immense energy savings over years of operating the power system. for example, at 0.1% savings, a large utili

10、ty transmitting 10000 gwh annually, 0.6 load factor, fuel at $20/mbtu and 10.5 mil rate, may save $11 million per year for bulk power in the eastern u.s. monitoring the state of the power system is a major security function of the computer system used at the central control center (dispatch center)

11、of utilities. voltages and power flow on lines and buses in the control area of the power system are monitored on the order of every 2 seconds. if the measured data are beyond safe operating tolerance limits, the alarms generated must be cleared by the dispatcher through switching line compensators

12、such as capacitor banks and shunt reactors, adjusting variable tap transformers, or transferring generator output, etc. so that dispatcher action can be based on reliable and complete information, measurements of voltages and power flow are processed by a state estimator which detects faulty measure

13、ment transducers (bad data) and fills in lost measurements when remote terminal units (rtus) have interrupted signals. state estimator calculations are based upon theoretical values for transmission line parameters. errors in the analytical values for transmission line parameters limit the state est

14、imators capability to detect bad data, and make it ineffective as a monitoring tool. with transmission line errors, the normalized residuals are larger than they should be. at least one utility adjusts its line parameter database to match the real world transmission lines in order to improve the sta

15、te calculated. ii. an economic example consider the 5 bus transmission network shown in figure 1, where the cost of generation is bid as shown for 4 busses. the total load is 669 mw and located at busses b,c, and d. the transmission line flows as calculated for loss-less lines 1 is shown in figure 1

16、. the flow on transmission line xed = .0297 is limited to 240 mw. the other transmission line reactances are xab = .0281, xbc = .0108, xcd = .0297 xad = .0303, xae = .0064 and are loss-less. the open arrows in figure 1 indicate injected power, and the black fill arrows indicate load at the bus in fi

17、gure 1, of all the locational marginal price (lmp) bids for generation, the 600 mw $10/mwh at bus e is completely utilized before the 69 mw at bus a is employed. the transmission line flows that result from this dispatch are shown on the figure. the total operating cost is $6966/h. fig. 1 economic d

18、ispatch for 669 mw load to utilize the lowest cost of available generation as the load on the system uniformly increases to 300 mw at busses b,c, and d, the lmp dispatch would attempt to use all the 210 mw of inexpensive power ( 110mw$14/mwh and 100 mw$15/mwh) at bus a before 90 mw of the $30/mwh po

19、wer at bus d is used. the total cost would be $11,740/h. however, when 210 mw is dispatched at bus a, this results in 243 mw of flow on line e-d to violate the constraint. as a result, only 166 mw of the inexpensive power at bus a is utilized and 124 mw at bus d is required. total operation cost inc

20、reases to $12,100/h because of the constraint. the dispatch is shown in figure 1a. if the transmission line e-d has a real world reactance of xed = 1.25*.0297, or in other words has a measured 25% more reactance than the database, the dispatch for 900 mw load utilizes all the low cost power because

21、the power flow does not violate the e-d line constraint. the transmission line power flows for this case are shown on figure 2. the total cost of the dispatch is $11,740/h. the example shown in figure 1 was employed by an eastern usa power pool to demonstrate constraints in line flow. figure 2 shows

22、 that power flows computed with correct line parameters are significantly different, to the point where large economic differences are present. corrections are rarely made to transmission line parameters in order to match measured line flows because sources of measurement error are unknown. in a uti

23、lity it also difficult to change parameters in the database because so many different groups within the utility, e.g., planning, relaying, security, etc., must change their values or settings of field equipment. iii. estimation of transmission line parameters for short to medium length, compared to

24、a 60 hz wavelength, 3-phase transmission lines are modeled by pi-equivalents calculated from ideal geometry and operated under balanced phase conditions. the voltage magnitudes at the line terminations are an average of the phase measurements, usually obtained with 1% to 2% accuracy step-down transf

25、ormer measurements. fig. 1a economic dispatch for 900 mw load limited by constraint on line e-d the real and reactive power flow for the pi-equivalent is a sum of flows on the 3 phases and obtained as an instantaneous product of 1% to 2% accurate current measurements with the voltage measurements. n

26、eglecting the small contributions of a/d converters at the transducers and computer numerical word length, the overall accuracy of power flow measurements also is on the order of 1% to 2%. figure 2 economic dispatch for 900 mw load with xed =1.25*.0297 transmission line reactance (no line constraint

27、s violated) the eastern power pool used for figure 1and 1a provided a scada snapshot of power flow and voltage measurements for the 3-bus network shown in figure 3. data of the snapshot is presented in figure 4. notice more real power comes out of line l1 than goes into it (s1 and s2), but this anom

28、aly is due to measurement tolerance as the real power difference is 2% of the absolute value. snapshot is state estimator terminology for an almost synchronized set of measurements because a short time interval (milliseconds) exists from the initial measurement to the final measurement for a slowly

29、changing load/generation. fig. 3 scada measurement points on a 3 bus network fig. 4 scada measurements for figure 3 a state estimation computation smoothes the data, detects bad transducers, and calculates the best estimate of the voltage and phase angle at busses of the network, i.e., the state of

30、the network. the calculated state is a weighted least squares estimate best fit to the measurements using database values for the pi-equivalent transmission line parameters, r+jx for the series p.u. impedance elements jy/2 for the shunt p.u. susceptance at both ends of the line. analytical methods e

31、xist to estimate transmission line parameters from snapshots in conjunction with the state estimator. the first such method appeared soon after the start of state estimation 2. many contributions were made around 1990 of which 3, 4 are typical, new techniques continue to evolve 5. a recent summary i

32、s reference 6. the data snapshot of figure 4 was used to estimate the transmission line parameters of figure 3 by a method of propagating residual errors of state estimation related to 5 and finding the worst fit line. the results of the parameter estimation with this method are presented in figure

33、5. only 2 transmission lines of the figure 3 network could be estimated before residual errors swamped out further detection. lines bc and ab show 25% errors in line charging susceptance compared to the database. there is a 50% error in the estimated line resistance of line bc compared to the databa

34、se value, which may be due to an operating temperature difference. fig. 5 results of transmission line parameter estimation for network of figure 3 with scada data of figure 4 the estimated values for line charging susceptance of transmission lines bc and ab affect reactive compensation in their vic

35、inity. the 50% difference in resistance of line bc may affect the transmission loss coefficients in the vicinity. power flow calculations with s1 to s6 data of figure 4 show that power flow on the network lines is much closer to measured values using the estimated parameters than with the database v

36、alues. iv. verification of the parameter estimation method it is difficult to prove that estimated transmission line parameters are true to the real world values. analytical cases using power flow computations from standard ieee 5 bus, 14 bus cases, etc., then corrupting the line parameters and flow

37、s by random noise, have been used in order to obtain test the algorithm. however, this analytical process does not match real world data. a real world case to verify the parameter estimation algorithm is as follows. parameters for transformers are perhaps the best known of large power handling eleme

38、nts because of measurements performed by the manufacturer. the 3 parallel transformers and a 4th series transformer shown in figure 6a, had the scada snapshot measurements shown in figure 6b. fig. 6a parallel and series transformers fig. 6b scada measured flows for the parallel and series transforme

39、rs the parameter estimation program applied to the data of figure 6b resulted in the values shown in figure 7. there is exact agreement of estimated and database reactance for transformer ta bk 63, and some difference in resistance. the estimated parameters for ta bk 62 match the database values for

40、 ta bk 60, and the estimated values for ta bk 60 match the database values for ta bk 62. these two analysis discrepancies were resolved when the utility discovered ta bk 62 and ta bk 60 had their field instrumentation wires switched at the remote-terminal unit (rtu). a more extensive verification of

41、 the parameter estimation method was obtained in very closely controlled laboratory test bed experiments conducted at nasa glenn research center 7. these tests, performed on both radial and loop networks, considered a transmission line fault to be any change in the transmission line test bed value c

42、ompared to the pre-test or calibrated database value. in the laboratory tests, discrete physical resistors were added as series or line-to-ground components in the test bed, and the parameter estimation program was required to detect the altered line from a data snapshot. the tests found the deliber

43、ate error introduced in a transmission line 100% of the time over wide ranges of network operating conditions. the tests demonstrated the limit of the transmission line parameter estimation program is the capability to estimate lines with residuals above the threshold of noise due to measurement and

44、 other transmission line errors in the network.for the power system case shown in figures 3, 4, and 5, only 2 of 3 transmission lines could be estimated. for the transformer case in figures 6a, 6b, and figure 7, only 3 of 4 transformers could be parameter estimated before the algorithm became limite

45、d by noise. fig. 7 database and parameter estimations for a transformer group ( ta bk 62 and ta bk 60 switched in the field) v. a large network test case the 19 bus, 42 line, 2 transformer network shown in figure 8 had a scada snapshot of only transmission line flows and voltages with which to perfo

46、rm the parameter estimation. in the figure, the bus numbers are in circles. often 2 or 4 transmission lines are in parallel from bus-to-bus. the scada data snapshot for figure 8 consisted of 170 measurements of line flows plus voltages. a portion of the scada data is presented in figure 9. the 170 s

47、cada measurements were used to compute the state estimate from the state estimator residuals, the worst fit of estimated parameters to database values was for the transmission line between bus #8 and #16, followed by the parallel lines between bus #6 and bus #8, etc., in the order presented in figur

48、e 9. values of estimated transmission line parameters are presented in figure 10 for lines that could be estimated before noise limited figure 8 test network of 19 busses fig. 9 scada data for part of figure 8(p.u., 100 mva base) fig. 10 estimated line parameters for 9 lines from the 19 bus network

49、(figure 8) a comparison of figure 10 estimated parameters against the figure 9 database values shows 50% to 400% differences in line charging. estimated resistance compared to database resistance varies from close agreement to 300% difference. the lines between bus #15 and bus #6 show 300% differenc

50、e in reactances estimated compared to database. these are significant differences. by matching line termination p,q,v in a one line, two bus power flow, the calculation shows estimated lines are a closer fit to scada flows. a state estimator computation for the 19 bus topology of figure 8 with datab

51、ase line parameters had 50 points of normalized residuals .00005 but =.0003, and no points higher. the same snapshot with estimated transmission line parameters had only 28 points in this same range. this is a much closer fit of calculations to measurements. vi. concluding remarks the parameter esti

52、mation method was verified by several field tests, simple computations, and in laboratory experiments. physical checks, such as scada measurements on open-end excitation of transmission lines, should be used by utilities to measure line capacitance when a line is restored to service. scada-based est

53、imates are more accurate than theoretical database parameters. the property of parameter estimation, as eventually limited by noise in measurements and other transmission line errors, forces the algorithm to be applied to only 15 to 30 bus portions of larger networks. analysis is performed until the

54、 algorithm becomes limited, then the test area is moved to a new portion of the larger network. vii. references 1 wood, a.j., and wollenberg, b.f. “power generation, operation, and control”, text, j. wiley, 1996, isbn 0-47158699-4 2 debs, a., “parameter estimation for power systems in the steady-sta

55、te”, ieee trans. power, vol.19, #6, dec. 1974 3 wu, f.f., “detection of topology errors by state estimation”, ieee pes winter meeting, 1988 4 liu, w-h.,e., wu, f.f., and lun, s-m, “ observability analysis and bad data processing for state estimation with equality constraints”, ieee trans. pow. sys.,

56、 vol.pwrs-3, may 1988 5 liu, w-h.,e., wu, f.f., and lun, s-m., “estimation of parameter errors from measurement residuals in state estimation”, trans. pow. sys., vol. 7, no. 1, feb 1992 6 zarco, p. and exposito, a.g., “power system parameter estimation : a survey”, ieee trans. pow. sys., vol 15, no.

57、1, feb,2000 7 kusic, g.l., “experimental tests on power system monitoring and fault detection”, report to nasa glenn research center from power systems consultants, inc, dec 24, 2002 dr. george kusic, m 1953, received his bsee (1957), msee (1964), and ph.dee (1968) from carnegie-mellon university. s

58、ince he received his doctorate, he has been a faculty member of the department of electrical engineering at the university of pittsburgh. his areas of interest are the power field and electronics. dr. kusic has numerous ieee publications in the power field and is the author of a textbook, “computer-aided power system analysis”, by prentiss-hall. in 1981 dr. kusic founded power systems consultants to provide electrical engineering consulting to various firms, utilities, and governmental agencies. among the firms clients are westinghouse electric, ibm, c