- Email: [email protected]
Suspected bad data substitution using a search emulation procedure
Electric Power Systems Research, 13 (1987) 219 - 224
219
Suspected Bad Data Substitution Using a Search Emulation Procedure R. D. SHULTZ
University of Wisconsin-Platteville, Platteville, WI 53818 (U.S.A.) R. A. SMITH and L. L. WITTHUHN
Florida Power Corporation, St. Petersburg, FL (U.S.A.) (Received August 13, 1987)
SUMMARY
The ability to study and operate large p o w e r systems has been significantly increased by telemetering more and more remote data to a central control center. The effectiveness o f computational and operational activities depends directly on the ability to detect, identify and substitute for bad data being sent from the remote stations. This substitution process is often done by a person tracking flows back through the system a n d estimating values for suspected bad data from other data inputs. This paper presents a computer procedure which emulates the tracking process done by the h u m a n estimator and automatically verifies suspect data.
1. INTRODUCTION
In the past two decades, the increase in the quantity of telemetered power system data has resulted in improved system monitoring and operation. However, owing to the envir o n m e n t in which the sensing, transducing, and transmitting equipment must operate, the occurrence of failure or incorrect data transmission is not u n c o m m o n . Complete failure of a telemetered location is usually easily identified. The transmission of inaccurate data poses a more difficult identification problem. One m e t h o d frequently used to verify a suspected data point is h u m a n estimation of what t h a t data should be from other data inputs. For instance, if the power flow at terminal one of a line is suspect, the power flow at terminal two of t h a t line could be compared with the terminal one flow to see 0378-7796/87/$3,50
if t h e y are within a reasonable value of each other. If the terminal two data are not available or are also suspect, then the power flows of all other fines connected to terminal one could be summed with any loads at terminal one subtracted. This value could then be compared with the suspected terminal one data of the original line. While this procedure is straightforward, the complexity enters if any of the data from all other lines connected to terminal one are suspect or not available. If that is the case, then the person making the estimation must take the process one step further. The power flow at the other terminal of the new line with unavailable data could be used; or, if that is also unavailable, the summation of power flows from all other lines connected to the other terminal minus loads could be used to estimate the unavailable data. This process can be cascaded back through the system until good data are f o u n d to estimate and compare with the original suspect data. This process is effective and used often to confirm the accuracy of suspect data. It would, however, be faster and easier if a computer would emulate the procedure followed by the person to arrive at the estimated value. This paper presents work which resulted in a computer program written in Fortran which emulates this process.
2. THE EMULATION PROCEDURE
In order to make a good estimation from available data, a person must do a search of the system and make appropriate deductive decisions at each node. The search pattern which most closely emulates this procedure is a depth-first search with reliable data as the © Elsevier Sequoia/Printed in The Netherlands
220
suspect data. Therefore, before starting the dept h search at each suspect data point, the emulator first checks t o determine if data are avilable from the ot her terminal.
3. POWER SYSTEM APPLICATIONS
Fig. 1. Tree for depth-first search.
termination goal [ 1 , 2 ] . Figure 1 illustrates how such a search is performed. Node A is where the search begins to verify suspect data. Assume t hat the data at nodes B, D, and F are also suspect. For this particular application, a relationship exists between t h e data at different levels of the tree. As an example, for the power flow applications with no loads present, t he flows at E and F can be summed to estimate the flow at B. T he r ef or e to estimate A, an a t t e m p t would be made to sum the data at B, C, and D. The data at B, however, are unreliable. Node B data must be estimated by attempting to sum E and F. Data at F are also suspect. Since only one node is co n n ected t o F, the node F data are estimated to be the values of t he data at J. This estimation at F is t h e n summed with the n o d e E data to estimate the B data. The estimated value at B is now summed with the value o f the data at C. T he data at D, however, are suspect. The node D data can be estimated b y summing t he data at H and I. This value is t h e n added to the summation o f B and C to estimate the value of the data atA. The relationship between levels on the tree, of course, varies for different applications. If loads are present t hen t he y must be subtracted f r o m summations. If VAR flows are o f concern t h e n the VAR losses in the line and line charging m ay have t o be considered. This paper will present the application of this emulation to the power flow problem. An additional point must be made in applying t h e depth-first search to power systems. When data measurement on a transmission line is suspect, it may be possible to use data f r o m th e o th er end of the line to estimate the
The emulator was first tried on a small test system so t hat its correct operation could be confirmed. It was then applied to a fullscale power system to verify power flow telemetered data. In b o t h cases, the results were very encouraging. The test system consisted of 16 buses with 21 lines (Fig. 2). The asterisk in Fig. 2 indicates t hat the data at the bus 11 terminal o f line 11-5 are being verified. The squares indicate that the data are either unreliable or unavailable at that terminal o f t hat particular line. This scheme of unavailable data was chosen so as t o test the emulator in its search pattern. The emulator first attempts to get the data from the bus 5 terminal o f line 11-5. Since this poi nt is unavailable, the flows f r o m all ot her lines connected to bus 11 are summed. However, the data at the bus 11 terminal o f line 11-13 are unavailable. The emulator
~
16
12
15
13
t
I4
6
I
7
8
Fig. 2. Test system with unavailable data.
221 attempts to get data from the bus 13 terminal of that line. Since those data are also unavailable, the flows of all other lines connected to bus 13 are summed. This process continues until reliable data are obtained on each search path. The result of this procedure for this particular test case equals the summation of bus 10 terminal flows of lines 10-1, 10-1, 10-9, and 10-3 minus the loads at buses 10 and 14 plus the flow at bus 13 terminal of line 10-13 plus the flow at bus 16 terminal of line 13-16 plus the summation of bus 12 terminal flows of lines 12-8 and 12-5 minus the load at bus 12 plus the flow at bus 13 terminal of line 13-15 minus the loads at buses 13 and 11 Care must be taken in each of these operations so that appropriate signs are placed on each flow to reflect assumed positive flow for each measurement. A problem did arise during the testing of the emulator. If in Fig. 2 the data at both terminals of line 13-10 are also unavailable, then a loop of unavailable data consisting of lines 13-10, 13-14, and 14-10 would exist. This condition makes it impossible to estimate values of data within that loop. To overcome this problem, the emulator stops its depth-first search from the bus 11 terminal and starts a search on the bus 5 terminal. If another loop with unavailable data is encountered in that search, the loop is treated as a super-node. The power flows into the loop equal the flows out minus the loads at the buses in the loop. This procedure allows estimation of unavailable data external to the loop, without requiring estimation of the unavailable data in the loop. The small test system confirmed that the emulator performed its designed procedure correctly. It was then applied to the Florida Power Corporation (FPC) energy control center real time telemetry system. For this test, the FPC state estimator (STESP) measurement residual calculations were used to drive the input to the emulator. The STESP was written and implemented by FPC engineers at the control center. It is based upon the algorithm developed in ref. 3 and is
dimensioned for a 1000 bus, 2000 line network model. Its measurement residual calculations are performed using weighted residuals [4] and the ten worst calculated residuals are presented for review on a CRT display. The program that drives the emulator reads and analyzes the ten worst residuals list to determine if suspected bad measurements have been detected by STESP. For FPC, the decision that bad measurements exist is based upon a calculated residual threshold of 0.1. Measurements whose weighted residuals are greater than or equal to 0.1 are suspected to be bad. The emulator driver program uses this same decision threshold and when it finds a measurement residual that meets this criterion, it triggers the emulator routine. The function of the emulator routine at this point is to scan nearby measurements to calculate or otherwise find a substitute value for the suspected bad measurement. If the emulator is able to find a substitute, it compares the substitute value with the suspected bad measurement. If a substantial difference between the two values exists, it flags this condition and recommends that the substitute value be used in subsequent STESP calculations. The comparison between the substitute and suspected bad measurement is necessary for weighted residual calculations because the largest weighted residual does not necessarily correspond to a bad measurement [5]. Thus, if the difference between the substitute and suspected bad measurement is large, the confidence in the weighted residual calculation is more secure. The example calculation presented here involves the presence of bad data in the FPC Hudson substations, HUDS. A study case of a real time telemetry snapshot was established, and three bad measurements of MW line flows were deliberately overridden in HUDS. The original real time snapshot one-line diagram is shown in Fig. 3 and the one-line diagram with the contaminated measurements (circled) is shown in Fig. 4. A STESP solution was executed for the measurement configuration of Fig. 4, and the resulting ten worst weighted residuals are shown in Table 1. The emulator driver program was executed next to process this list. The program determined that the MW flow on the Hudson to Brooksville West (BKVW) line
222 115 KV 2SNI
J
-3.41'
230 KV #I
]
I -I12'6
i
BKVW I
2HUD -
........
BKRG
jI
_HUDSONSUB_ST_AT_I ON. . . . . . .
500 KV
#I
230 KV
i,,o.4 l CURL jCURL
J-83.B + 89.3 I .4
SHE,_
+ 98.4
SHEL
+161.8
I ULrl
7sP
~.¢
. . . . . .LAKE . . TARPON . . . . SUBSTATION . . . . . . . . . . . .
Fig. 3. Original real t i m e MW flow s n a p s h o t . ( N o t e : positive n u m b e r s are flows o u t o f a bus.)
2SHI 2HUD
II5KV
I
23UKV BKVW
1 I
+63.0
I
i
iL- . . . . . . . . . . . . . . . . . .HUDSON . . . . . SUBSTATION BKRG
50OKV
#I
J
230KV
~CURL
+161.8 1-83.2
7SP
I CUR,.
+ B9.3J-H
SHEL
J ULr~
+
98.4
~.~
SHEL
SUBSTATION . . . . . . . . . . . . . . . . .LAKE . . . . . .TARPON ....
Fig. 4. Real t i m e MW flow s n a p s h o t w i t h c o n t a m i nated measurements.
was suspected to be bad at t he HUDS end. The driver program t hen eliminated from consideration in its calculations all line flow measurements with weighted residuals greater than 0.1 and all line flow measurements with MVA values greater t han the MVA limit of their com pani on lines. This procedure removes potentially bad measurements f r o m the emulator calculations and provides for better accuracy in its calculations of substitute values. For this case, the line flow measurements deleted included b o t h measurements on the Hudson to Lake T a r p o n (LTPN) line, the LTPN transformer 1, and the HUDS transformer 1. N ot e that the HUDS transformer has a bad measurement t hat does not appear in the residual list. This condition occurs because it is a critical measurement. However, its deletion occurs because its overridden value exceeds its MVA limit. Certain ot her measurements were also deleted, but t h e y are not featured in the emulator calculations and thus are not noted here. The emulator searched the lines adjacent to BKVWHUDS line and, with holes caused by measurement deletion, was forced t o track to LTPN for the first series of calculations. At this station, t he MW flow leaving the 500 kV bus was used to yield a substitute flow on the 230 kV bus of t he LTPN transformer 1. Then the flows leaving the LTPN 230 kV bus were summed to calculate a substitute value for the HUDSLTPN line at LTPN. This q u a n t i t y was inverted in sign t o substitute for the MW
TABLE 1 F i r s t m e a s u r e m e n t residual c a l c u l a t i o n results State e s t i m a t o r c a l c u l a t e d residuals Element
Type*
Calc.
Tel.
Residual
T F T F T T F T F F
--139.0 148.2 --147.9 246.4 56.4 --130.5 131.2 204.8 257.1 248.1
--999.6 999.7 --46.4 172.2 --9.2 --73.2 75.1 161.5 290.0 215.2
6.6655 6.5252 0.3705 0.1981 0.1548 0.1184 0.1133 0.0675 0.0390 0.0387
Bus
No.
Name
No.
925 924 924 836 837 854 854 946 489 67
BKVWHUDS HUDSLTPN HUDSLTPN LTPN XFMR 1 BKRG XFMR 1 BKRG7SP BKRG7SP BKRGLTPN CFLA XFMR 2 CRLAHOLD
816 816 690 691 624 710 624 602 389 832
*T= To, F = From.
Name 230 230 230 500 230 230 230 500 500 230
HUDSON HUDSON LK TARP1 LK TARPN BRKRIDGE S E V E N SP BRKRIDGE BRKRIDGE CENT FLA HOLDER 3
MW MW MW MW MW MW MW MW MW MW
"
223
flow at the HUDS end of the HUDSLTPN line. The emulator now focused attention on the Hudson substation. The MV flows on the lines leaving the 115 kV bus were summed to yield the flow on the HUDS transformer 1, 115 kV bus. This value was inverted in sign to yield the transformer MW flow on its 230 kV bus. At this point the emulator had enough information on the HUDS 230 kV bus to calculate the substitute value for the MW flow on the HUDSBKVW line. The steps noted above for calculating the substitute number are shown in the one-line diagram of Fig. 5. Each calculated substitute MW flow is shown in a numbered circle. The numbers indicate the order in which the emulator performed its calculations. The substitute value of the HUDSBKVW line was used in place of the suspected bad II5KV
230KV
2SHI I
BKVW
+63.0
2HUD
I
I
I I
HUDSON SUBSTATION
500KV
BKo I
I
+161.5
measurement of the next STESP solution. The measurement residuals for this solution are shown in Table 2. In this case, the BKVWHUDS residual is reduced and the worst residual is now flagged to be on the HUDSLTPN line at HUDS. The emulator program was executed again and the substitute value for this measurement was f o u n d to be 43.1 MW. This value was used in the third and next STESP solution. The ten worst measurement residuals for this solution are shown in Table 3. In this case, the largest residual did not trigger the emulator execution and no further calculations were performed. The emulator algorithm, as driven from the state estimator residual calculations, relieved the estimator of its suspected bad measurement. However, it did not officially find a substitute measurement for the HUDS transformer 1 MW flow for the reasons noted earlier. This condition points out the need for use of the emulator also as a raw telemetry check on all measurements or those suspected to be bad because of MVA limit or other types of reasonability checks.
230KV
.i;
4. C O N C L U S I O N S
I CURL
+161.81-83.2
I CURL
• 69.3V.4
7SP SHEL SHEL I
LAKE TARPON SUBSTATION . . . . . . . . . . . . . . . . . . . . . .
_
Fig. 5. E m u l a t o r s u b s t i t u t i o n steps.
This paper has presented an approach to solving the problem of finding substitute values for suspected bad measurements. The algorithm derived was driven by state estimator weighted residual calculations. However, other kinds of reasonability checks
TABLE 2 S e c o n d m e a s u r e m e n t residual c a l c u l a t i o n results
State estimator calculated residuals Element
Type*
Calc.
Tel.
Residual
F MW T MW T MW VOLT T MW T MV F MW VOLT F MV F MW
223.1 --223.3 --8.2 241.3 182.4 --20.7 236.5 116.6 --9.7 280.4
999.7 --46.4 --143.9 238.5 211.6 0.6 215.2 117.5 --29.5 260.7
5.4284 1.1138 0.1658 0.0307 0.0307 0.0164 0.0163 0.0143 0.0141 0.0138
Bus
No.
Name
No.
924 924 925 391 821 493 67 536 414 947
HUDSLTPN HUDSLTPN BKVWHUDS 230 CRYST RV ANCLECLW BKRGCRP CFLAHOLD 115 BARTOW 2 PIEDSPLK CRP HOLD
816 690 816
230 230 230
HUDSON LK TARP1 HUDSON
694 624 832
230 230 230
ANCLOTE BRKRIDGE HOLDER 3
345 391
069 230
PIEDMON1 CRYST RV
* T = To, F = F r o m .
Name
224 TABLE 3 Third m e a s u r e m e n t residual calculation results State estimator calculated residuals Element
Type*
Calc.
Tel.
Residual
T MW VOLT T MW T MV F MW VOLT F MV F MW F MW F MV
181.4 241.3 --92.4 --20.7 235.2 116.6 --9.7 74.4 279.7 5.0
211.6 238.5 --143.9 0.6 215.2 117.5 --29.5 55.4 260.7 --13.7
0.0327 0.0304 0.0239 0.0164 0.0143 0.0142 0.0131 0.0130 0.0127 0.0387
Bus
No.
Name
No.
Name
821 391 925 493 67 536 414 487 947 975
ANCLECLW 230 C R Y S T R V BKVWHUDS BKRGCRP CFLAHOLD 115 B A R T O W 2 PIEDSPLK CFLALEBN CRP H O L D BARCFMD
694
230
ANCLOTE
816 624 832
230 230 230
HUDSON BRKRIDGE HOLDER 3
345 387 391 851
069 069 230 230
PIEDMON1 C E N T FL1 CRYST RV BARCOLA5
*T = To, F = F r o m .
can be used to trigger the emulator. A test of the technique on the Florida Power Corporation real time telemetry has shown promising results and FPC intends to continue exploring its use in other applications.
REFERENCES 1 P. H. Winston, Artificial Intelligence, AddisonWesley, Reading, MA, 1984.
2 P. H. Winston and B. K. P. Horn, Lisp, AddisonWesley, Reading, MA, 1984. 3 J. J. Allemong, L. R a d u and A. M. Sasson, A fast and reliable state estimation algorithm for AEP's new control center, IEEE Trans., PAS-101 (1982) 933 - 944. 4 E. Handschin, F. C. Schwepp, J. Kohlas and A. Fiechter, Bad data analysis for p o w e r system state estimation, IEEE Trans., PAS-94 (1975) 329 - 337. 5 L . Mili, Th. Van Cutsem and M. Ribbens-Pavella, Hypothesis testing identification: a new m e t h o d for bad data analysis in p o w e r system state estimation, IEEE Trans., PAS-103 (1984) 3239 3252.
219
Suspected Bad Data Substitution Using a Search Emulation Procedure R. D. SHULTZ
University of Wisconsin-Platteville, Platteville, WI 53818 (U.S.A.) R. A. SMITH and L. L. WITTHUHN
Florida Power Corporation, St. Petersburg, FL (U.S.A.) (Received August 13, 1987)
SUMMARY
The ability to study and operate large p o w e r systems has been significantly increased by telemetering more and more remote data to a central control center. The effectiveness o f computational and operational activities depends directly on the ability to detect, identify and substitute for bad data being sent from the remote stations. This substitution process is often done by a person tracking flows back through the system a n d estimating values for suspected bad data from other data inputs. This paper presents a computer procedure which emulates the tracking process done by the h u m a n estimator and automatically verifies suspect data.
1. INTRODUCTION
In the past two decades, the increase in the quantity of telemetered power system data has resulted in improved system monitoring and operation. However, owing to the envir o n m e n t in which the sensing, transducing, and transmitting equipment must operate, the occurrence of failure or incorrect data transmission is not u n c o m m o n . Complete failure of a telemetered location is usually easily identified. The transmission of inaccurate data poses a more difficult identification problem. One m e t h o d frequently used to verify a suspected data point is h u m a n estimation of what t h a t data should be from other data inputs. For instance, if the power flow at terminal one of a line is suspect, the power flow at terminal two of t h a t line could be compared with the terminal one flow to see 0378-7796/87/$3,50
if t h e y are within a reasonable value of each other. If the terminal two data are not available or are also suspect, then the power flows of all other fines connected to terminal one could be summed with any loads at terminal one subtracted. This value could then be compared with the suspected terminal one data of the original line. While this procedure is straightforward, the complexity enters if any of the data from all other lines connected to terminal one are suspect or not available. If that is the case, then the person making the estimation must take the process one step further. The power flow at the other terminal of the new line with unavailable data could be used; or, if that is also unavailable, the summation of power flows from all other lines connected to the other terminal minus loads could be used to estimate the unavailable data. This process can be cascaded back through the system until good data are f o u n d to estimate and compare with the original suspect data. This process is effective and used often to confirm the accuracy of suspect data. It would, however, be faster and easier if a computer would emulate the procedure followed by the person to arrive at the estimated value. This paper presents work which resulted in a computer program written in Fortran which emulates this process.
2. THE EMULATION PROCEDURE
In order to make a good estimation from available data, a person must do a search of the system and make appropriate deductive decisions at each node. The search pattern which most closely emulates this procedure is a depth-first search with reliable data as the © Elsevier Sequoia/Printed in The Netherlands
220
suspect data. Therefore, before starting the dept h search at each suspect data point, the emulator first checks t o determine if data are avilable from the ot her terminal.
3. POWER SYSTEM APPLICATIONS
Fig. 1. Tree for depth-first search.
termination goal [ 1 , 2 ] . Figure 1 illustrates how such a search is performed. Node A is where the search begins to verify suspect data. Assume t hat the data at nodes B, D, and F are also suspect. For this particular application, a relationship exists between t h e data at different levels of the tree. As an example, for the power flow applications with no loads present, t he flows at E and F can be summed to estimate the flow at B. T he r ef or e to estimate A, an a t t e m p t would be made to sum the data at B, C, and D. The data at B, however, are unreliable. Node B data must be estimated by attempting to sum E and F. Data at F are also suspect. Since only one node is co n n ected t o F, the node F data are estimated to be the values of t he data at J. This estimation at F is t h e n summed with the n o d e E data to estimate the B data. The estimated value at B is now summed with the value o f the data at C. T he data at D, however, are suspect. The node D data can be estimated b y summing t he data at H and I. This value is t h e n added to the summation o f B and C to estimate the value of the data atA. The relationship between levels on the tree, of course, varies for different applications. If loads are present t hen t he y must be subtracted f r o m summations. If VAR flows are o f concern t h e n the VAR losses in the line and line charging m ay have t o be considered. This paper will present the application of this emulation to the power flow problem. An additional point must be made in applying t h e depth-first search to power systems. When data measurement on a transmission line is suspect, it may be possible to use data f r o m th e o th er end of the line to estimate the
The emulator was first tried on a small test system so t hat its correct operation could be confirmed. It was then applied to a fullscale power system to verify power flow telemetered data. In b o t h cases, the results were very encouraging. The test system consisted of 16 buses with 21 lines (Fig. 2). The asterisk in Fig. 2 indicates t hat the data at the bus 11 terminal o f line 11-5 are being verified. The squares indicate that the data are either unreliable or unavailable at that terminal o f t hat particular line. This scheme of unavailable data was chosen so as t o test the emulator in its search pattern. The emulator first attempts to get the data from the bus 5 terminal o f line 11-5. Since this poi nt is unavailable, the flows f r o m all ot her lines connected to bus 11 are summed. However, the data at the bus 11 terminal o f line 11-13 are unavailable. The emulator
~
16
12
15
13
t
I4
6
I
7
8
Fig. 2. Test system with unavailable data.
221 attempts to get data from the bus 13 terminal of that line. Since those data are also unavailable, the flows of all other lines connected to bus 13 are summed. This process continues until reliable data are obtained on each search path. The result of this procedure for this particular test case equals the summation of bus 10 terminal flows of lines 10-1, 10-1, 10-9, and 10-3 minus the loads at buses 10 and 14 plus the flow at bus 13 terminal of line 10-13 plus the flow at bus 16 terminal of line 13-16 plus the summation of bus 12 terminal flows of lines 12-8 and 12-5 minus the load at bus 12 plus the flow at bus 13 terminal of line 13-15 minus the loads at buses 13 and 11 Care must be taken in each of these operations so that appropriate signs are placed on each flow to reflect assumed positive flow for each measurement. A problem did arise during the testing of the emulator. If in Fig. 2 the data at both terminals of line 13-10 are also unavailable, then a loop of unavailable data consisting of lines 13-10, 13-14, and 14-10 would exist. This condition makes it impossible to estimate values of data within that loop. To overcome this problem, the emulator stops its depth-first search from the bus 11 terminal and starts a search on the bus 5 terminal. If another loop with unavailable data is encountered in that search, the loop is treated as a super-node. The power flows into the loop equal the flows out minus the loads at the buses in the loop. This procedure allows estimation of unavailable data external to the loop, without requiring estimation of the unavailable data in the loop. The small test system confirmed that the emulator performed its designed procedure correctly. It was then applied to the Florida Power Corporation (FPC) energy control center real time telemetry system. For this test, the FPC state estimator (STESP) measurement residual calculations were used to drive the input to the emulator. The STESP was written and implemented by FPC engineers at the control center. It is based upon the algorithm developed in ref. 3 and is
dimensioned for a 1000 bus, 2000 line network model. Its measurement residual calculations are performed using weighted residuals [4] and the ten worst calculated residuals are presented for review on a CRT display. The program that drives the emulator reads and analyzes the ten worst residuals list to determine if suspected bad measurements have been detected by STESP. For FPC, the decision that bad measurements exist is based upon a calculated residual threshold of 0.1. Measurements whose weighted residuals are greater than or equal to 0.1 are suspected to be bad. The emulator driver program uses this same decision threshold and when it finds a measurement residual that meets this criterion, it triggers the emulator routine. The function of the emulator routine at this point is to scan nearby measurements to calculate or otherwise find a substitute value for the suspected bad measurement. If the emulator is able to find a substitute, it compares the substitute value with the suspected bad measurement. If a substantial difference between the two values exists, it flags this condition and recommends that the substitute value be used in subsequent STESP calculations. The comparison between the substitute and suspected bad measurement is necessary for weighted residual calculations because the largest weighted residual does not necessarily correspond to a bad measurement [5]. Thus, if the difference between the substitute and suspected bad measurement is large, the confidence in the weighted residual calculation is more secure. The example calculation presented here involves the presence of bad data in the FPC Hudson substations, HUDS. A study case of a real time telemetry snapshot was established, and three bad measurements of MW line flows were deliberately overridden in HUDS. The original real time snapshot one-line diagram is shown in Fig. 3 and the one-line diagram with the contaminated measurements (circled) is shown in Fig. 4. A STESP solution was executed for the measurement configuration of Fig. 4, and the resulting ten worst weighted residuals are shown in Table 1. The emulator driver program was executed next to process this list. The program determined that the MW flow on the Hudson to Brooksville West (BKVW) line
222 115 KV 2SNI
J
-3.41'
230 KV #I
]
I -I12'6
i
BKVW I
2HUD -
........
BKRG
jI
_HUDSONSUB_ST_AT_I ON. . . . . . .
500 KV
#I
230 KV
i,,o.4 l CURL jCURL
J-83.B + 89.3 I .4
SHE,_
+ 98.4
SHEL
+161.8
I ULrl
7sP
~.¢
. . . . . .LAKE . . TARPON . . . . SUBSTATION . . . . . . . . . . . .
Fig. 3. Original real t i m e MW flow s n a p s h o t . ( N o t e : positive n u m b e r s are flows o u t o f a bus.)
2SHI 2HUD
II5KV
I
23UKV BKVW
1 I
+63.0
I
i
iL- . . . . . . . . . . . . . . . . . .HUDSON . . . . . SUBSTATION BKRG
50OKV
#I
J
230KV
~CURL
+161.8 1-83.2
7SP
I CUR,.
+ B9.3J-H
SHEL
J ULr~
+
98.4
~.~
SHEL
SUBSTATION . . . . . . . . . . . . . . . . .LAKE . . . . . .TARPON ....
Fig. 4. Real t i m e MW flow s n a p s h o t w i t h c o n t a m i nated measurements.
was suspected to be bad at t he HUDS end. The driver program t hen eliminated from consideration in its calculations all line flow measurements with weighted residuals greater than 0.1 and all line flow measurements with MVA values greater t han the MVA limit of their com pani on lines. This procedure removes potentially bad measurements f r o m the emulator calculations and provides for better accuracy in its calculations of substitute values. For this case, the line flow measurements deleted included b o t h measurements on the Hudson to Lake T a r p o n (LTPN) line, the LTPN transformer 1, and the HUDS transformer 1. N ot e that the HUDS transformer has a bad measurement t hat does not appear in the residual list. This condition occurs because it is a critical measurement. However, its deletion occurs because its overridden value exceeds its MVA limit. Certain ot her measurements were also deleted, but t h e y are not featured in the emulator calculations and thus are not noted here. The emulator searched the lines adjacent to BKVWHUDS line and, with holes caused by measurement deletion, was forced t o track to LTPN for the first series of calculations. At this station, t he MW flow leaving the 500 kV bus was used to yield a substitute flow on the 230 kV bus of t he LTPN transformer 1. Then the flows leaving the LTPN 230 kV bus were summed to calculate a substitute value for the HUDSLTPN line at LTPN. This q u a n t i t y was inverted in sign t o substitute for the MW
TABLE 1 F i r s t m e a s u r e m e n t residual c a l c u l a t i o n results State e s t i m a t o r c a l c u l a t e d residuals Element
Type*
Calc.
Tel.
Residual
T F T F T T F T F F
--139.0 148.2 --147.9 246.4 56.4 --130.5 131.2 204.8 257.1 248.1
--999.6 999.7 --46.4 172.2 --9.2 --73.2 75.1 161.5 290.0 215.2
6.6655 6.5252 0.3705 0.1981 0.1548 0.1184 0.1133 0.0675 0.0390 0.0387
Bus
No.
Name
No.
925 924 924 836 837 854 854 946 489 67
BKVWHUDS HUDSLTPN HUDSLTPN LTPN XFMR 1 BKRG XFMR 1 BKRG7SP BKRG7SP BKRGLTPN CFLA XFMR 2 CRLAHOLD
816 816 690 691 624 710 624 602 389 832
*T= To, F = From.
Name 230 230 230 500 230 230 230 500 500 230
HUDSON HUDSON LK TARP1 LK TARPN BRKRIDGE S E V E N SP BRKRIDGE BRKRIDGE CENT FLA HOLDER 3
MW MW MW MW MW MW MW MW MW MW
"
223
flow at the HUDS end of the HUDSLTPN line. The emulator now focused attention on the Hudson substation. The MV flows on the lines leaving the 115 kV bus were summed to yield the flow on the HUDS transformer 1, 115 kV bus. This value was inverted in sign to yield the transformer MW flow on its 230 kV bus. At this point the emulator had enough information on the HUDS 230 kV bus to calculate the substitute value for the MW flow on the HUDSBKVW line. The steps noted above for calculating the substitute number are shown in the one-line diagram of Fig. 5. Each calculated substitute MW flow is shown in a numbered circle. The numbers indicate the order in which the emulator performed its calculations. The substitute value of the HUDSBKVW line was used in place of the suspected bad II5KV
230KV
2SHI I
BKVW
+63.0
2HUD
I
I
I I
HUDSON SUBSTATION
500KV
BKo I
I
+161.5
measurement of the next STESP solution. The measurement residuals for this solution are shown in Table 2. In this case, the BKVWHUDS residual is reduced and the worst residual is now flagged to be on the HUDSLTPN line at HUDS. The emulator program was executed again and the substitute value for this measurement was f o u n d to be 43.1 MW. This value was used in the third and next STESP solution. The ten worst measurement residuals for this solution are shown in Table 3. In this case, the largest residual did not trigger the emulator execution and no further calculations were performed. The emulator algorithm, as driven from the state estimator residual calculations, relieved the estimator of its suspected bad measurement. However, it did not officially find a substitute measurement for the HUDS transformer 1 MW flow for the reasons noted earlier. This condition points out the need for use of the emulator also as a raw telemetry check on all measurements or those suspected to be bad because of MVA limit or other types of reasonability checks.
230KV
.i;
4. C O N C L U S I O N S
I CURL
+161.81-83.2
I CURL
• 69.3V.4
7SP SHEL SHEL I
LAKE TARPON SUBSTATION . . . . . . . . . . . . . . . . . . . . . .
_
Fig. 5. E m u l a t o r s u b s t i t u t i o n steps.
This paper has presented an approach to solving the problem of finding substitute values for suspected bad measurements. The algorithm derived was driven by state estimator weighted residual calculations. However, other kinds of reasonability checks
TABLE 2 S e c o n d m e a s u r e m e n t residual c a l c u l a t i o n results
State estimator calculated residuals Element
Type*
Calc.
Tel.
Residual
F MW T MW T MW VOLT T MW T MV F MW VOLT F MV F MW
223.1 --223.3 --8.2 241.3 182.4 --20.7 236.5 116.6 --9.7 280.4
999.7 --46.4 --143.9 238.5 211.6 0.6 215.2 117.5 --29.5 260.7
5.4284 1.1138 0.1658 0.0307 0.0307 0.0164 0.0163 0.0143 0.0141 0.0138
Bus
No.
Name
No.
924 924 925 391 821 493 67 536 414 947
HUDSLTPN HUDSLTPN BKVWHUDS 230 CRYST RV ANCLECLW BKRGCRP CFLAHOLD 115 BARTOW 2 PIEDSPLK CRP HOLD
816 690 816
230 230 230
HUDSON LK TARP1 HUDSON
694 624 832
230 230 230
ANCLOTE BRKRIDGE HOLDER 3
345 391
069 230
PIEDMON1 CRYST RV
* T = To, F = F r o m .
Name
224 TABLE 3 Third m e a s u r e m e n t residual calculation results State estimator calculated residuals Element
Type*
Calc.
Tel.
Residual
T MW VOLT T MW T MV F MW VOLT F MV F MW F MW F MV
181.4 241.3 --92.4 --20.7 235.2 116.6 --9.7 74.4 279.7 5.0
211.6 238.5 --143.9 0.6 215.2 117.5 --29.5 55.4 260.7 --13.7
0.0327 0.0304 0.0239 0.0164 0.0143 0.0142 0.0131 0.0130 0.0127 0.0387
Bus
No.
Name
No.
Name
821 391 925 493 67 536 414 487 947 975
ANCLECLW 230 C R Y S T R V BKVWHUDS BKRGCRP CFLAHOLD 115 B A R T O W 2 PIEDSPLK CFLALEBN CRP H O L D BARCFMD
694
230
ANCLOTE
816 624 832
230 230 230
HUDSON BRKRIDGE HOLDER 3
345 387 391 851
069 069 230 230
PIEDMON1 C E N T FL1 CRYST RV BARCOLA5
*T = To, F = F r o m .
can be used to trigger the emulator. A test of the technique on the Florida Power Corporation real time telemetry has shown promising results and FPC intends to continue exploring its use in other applications.
REFERENCES 1 P. H. Winston, Artificial Intelligence, AddisonWesley, Reading, MA, 1984.
2 P. H. Winston and B. K. P. Horn, Lisp, AddisonWesley, Reading, MA, 1984. 3 J. J. Allemong, L. R a d u and A. M. Sasson, A fast and reliable state estimation algorithm for AEP's new control center, IEEE Trans., PAS-101 (1982) 933 - 944. 4 E. Handschin, F. C. Schwepp, J. Kohlas and A. Fiechter, Bad data analysis for p o w e r system state estimation, IEEE Trans., PAS-94 (1975) 329 - 337. 5 L . Mili, Th. Van Cutsem and M. Ribbens-Pavella, Hypothesis testing identification: a new m e t h o d for bad data analysis in p o w e r system state estimation, IEEE Trans., PAS-103 (1984) 3239 3252.