332 IEEE Transactions on Power Systems, Vol. 11, No. 1, February 1996

Meter Placement for Red-Time Monitoring of Distribution Feeders

Mesut E. Baran Jinxiang Zhu Arthur W. Kelley

Department of Electrical and Computer Engineering North Carolina State University

Raleigh, NC 27695

Abstract This paper identifies the data requirements for real-time monitoring and control of distribution systems. It points out that in addition to having supervisory control and data acquisition on switches and control equipment, methods are needed to obtain an accurate estimation of data needed for feeder automation functions. A meter placement method is proposed for this purpose. It is shown that the measurements from such a metering scheme can be used with a state estimator to provide the real-time data needed for real-time monitoring of distribution system.

Key Words: Distribution Systems, State Estimation, Real-time monitoring.

I. INTRODUCTION

Utility interest in Distribution Automation (DA) for monitoring and control of various devices at the substation and at the feeder level is continuously growing since the early demonstration projects indicated its feasibility and potential benefits. As utilities enter the implementation phase, the selection, prioritization, and integration of automation functions, such as feeder switching for restoration and reconfigu- ration, volt/var control, and load management, become more important [1,2]. However, methods for obtaining the real-time data needed for on-line implementation of these automation functions has not yet received proper attention.

Early demonstration projects focused on technological feasibility of the individual DA functions and the data required for these functions are either directly measured in real-time or obtained from historical load data [3,4]. New methods proposed for implementation of these DA functions assume the availability of the required real-time data [5-8]. Recently new methods, such as short-term load forecasting [9], power flow [lo], and state estimation [ll], have been proposed to obtain the necessary real-time data, mainly the load data.

The next section of this paper looks at some of the most commonly implemented DA functions and identifies their real-time data requirements. It points

This paper was presented at the 1995 IEEE Power Industry Computer Applications Conference held in Salt Lake City, Utah, May 7-12, 1995.

out that real-time measurements such as voltage, currents, and power flows taken from various points in the system are needed for distribution system monitoring and control. In section three, real-time data processing methods are reviewed and the advantages of State Estimation (SE) method is outlined. In section four, a meter placement method is proposed for placement of the meters needed to obtain the real-time measurements for SE. Test results and conclusions are presented in sections five and six, respectively.

11. REAL - TIME DATA REQUIREMENTS FOR DA

Real-time data required for real-time monitoring and control of a distribution system is mainly determined by the functions to be automated in the system. Economic considerations usually put limits on the number of the functions that can be automated. Table 1 shows the commonly selected functions [2,3].

Table 1: Commonly Selected DA functions

Service restoration Feeder Restoration

Remote meter reading Real-time Pricin

Data requirements for the functions listed in Table 1 can be grouped as follows:

(i) Status of Switches: Status of circuit breakers at the substation needs to be monitored and controlled for fault detection and isolation at substation level. Similarly, status of switches on feeders, such as line reclosers, and sectionalizers need to be monitored and controlled for fault detection and isolation. Switch status information is also used in determining the topology of the system in real-time. This topology information is used by other control functions.

(ii) Status and/or Setting of Control Devices: Settings of regulating transformers and capacitor banks at the substation, and settings of voltage regulators and capacitor banks on the feeder are also needed to determine the current operating point of the system and the possible control actions.

0885-8950/96/$05.00 0 1995 IEEE

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demonstration projects indicated [4], detailed load data - preferably power supplied to each distribution transformer and its sensitivity with respect to the voltage - need to be closely estimated for effective volt/var control.

Short term load forecasting methods can be used to improve the estimate of the load data [6,10]. Unfortunately, forecasting methods can give accurate results when the loads are aggregated, such as the total load. It is statistically much more difficult to forecast individual loads accurately. For on-line applications, voltage and power measurements need to be used to check the validity of the forecasted load and adjust the loads accordingly.

One of the adjustment methods is based on power flow analysis. Actual measurements are used to scale the forecasted loads so that the power flow analysis results, based on this load distribution, match the measurements [lo]. Note that this procedure assumes that the measurements are perfect. As more real-time measurements become available, more general methods are needed to further correct the forecasted load distribution.

State Estimation (SE) is an analysis tool that gives "the best estimate" of the operating point (hence the load distribution) for a given set of measurements. Currently, there are state estimators used at transmis- sion level and these methods have been extended for state estimation in distribution systems [ll]. For a "good estimate," state estimation needs real-time voltage, current, and/or power measurements from various points along the feeder. SE can then check the validity of the forecasted load and make the necessary corrections to improve the accuracy of the load data.

Besides providing a more reliable real-time load data for feeder automation functions, SE can also be used to check the consistency of the measurements and the network model and hence provide data for the following real-time monitoring functions.

i) Switch Status Monitoring: Since most of the switching in a distribution system is done manually and not telemetered, SE can help the dispatchers to keep the network topology information up-to-date by detecting the status changes in switches.

To illustrate the process, consider the small radial feeder in Fig.1. As Fig.1 illustrates, we can define a zone for each switch in a radial feeder: it is the part of the feeder between the switch and its downstream neighbors. Similarly, meters placed in a feeder divide the feeder into mefer zones, as illustrated in Fig.1 also. By using SE, the total load in each switch zone can be estimated with a certain accuracy, for example by 20%. When the status of a switch changes, for example SW2 in Fig.1 opens, the measurement from mo will change and this change will be approximately equal to the total load in switch zone SW2. Since we have a good estimation of this load from SE performed before switching, by comparing this value with the change in the measurement we can detect that indeed it was the switching of SW2 that caused the change in measurement mo. This type of consistency check can

(iii) Real-Time Load Data: Real-time load data needed for implementation of the control functions listed in Table 1 is identified as follows: a) Transformer load balancing: total load supplied to

each feeder at the substation is needed so that the loading of transformers at the substation can adjusted as desired.

b) Feeder contingency analysis: power or current flows at the switch points are-needed to determine feeder restoration and reconfiguration schemes. This data determines the total load in each section of the feeder and is used to compare various load transfer options.

Volt/var control and load control: actual load profile (i.e., distribution of total load to distribution transformers) is needed. Effective volt/var and load control schemes depend very much on the accurate estimation of load distribution along the feeder [3,4].

This brief summary of real-time data requirements for individual DA functions can be used to determine the overall real-time data requirements of a DA implementation once the DA functions are selected. Among the DA functions, the substation automation usually takes the highest priority. The feeder switching functions take the second, and volt/var the third priority. Load management is usually considered separately, although its effective implementation depends on the amount of real-time load data available, and thus should be integrated with the other DA functions.

111. RFALTIME DATA PROCESSING METHODS

Meters and monitoring devices need to be placed at various points in the system and integrated in a SCADA system so that the real-time data obtained from these devices can be communicated to the dispatch center. Unfortunately, economics put very strong limits on the scale of such SCADA systems in distribution system monitoring and control applications. Table 2 shows devices that would be under SCADA in a large scale DA implementation.

Table 2 Devices under SCADA

Transformers Few Sectionalizers

Some Customer Meters

Note that only a small percentage of switches on a feeder are put under SCADA. Voltage regulators, and capacitor banks are not usually monitored either. Not included in the table is also the data acquisition needed to determine the real-time load distribution in the system. Conventionally, load data is obtained solely based on historical data collected by utilities about their customer loads. Although this data is sufficient for planning purposes, it may not be accurate enough for real-time monitoring and control functions. As

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easily be generalized for detecting changes in switch status in a given feeder.

zone mO

W " zone ml = zone swl

Figure 1: Zones defined by switches and meters in a feeder

ii) Locating Faults: The pre-fault and post-fault load distribution data from SE can also be used to identify the protection device (fuse or recloser) that operated to isolate a fault. To illustrate the procedure, consider Fig.1 again and assume that a fault occurred in zone SW2 and SW2 opened to isolate the fault. Since initially it is not known which switch opened, locating the fault becomes detecting the wrong switch status of SW2. Hence, the procedure outlined above for correcting the switch status can be applied to detect the switch that has opened to isolate the fault.

iii) Monitoring the Operation of Control Devices: Any change in the status of a capacitor bank, a voltage regulating transformer or a third party generation unit will affect the operation of a feeder appreciably. By measuring appropriate quantities in a feeder, SE can detect the changes in the status of these devices. For example, in the feeder of Fig.1, we can detect switching of the capacitor bank by using m2. Because, when the capacitor bank switches, the reactive power flow measurement of m;! will change by an amount equal to the switched kVar capacity of the bank.

IV. METER PLACEMENT FOR SE

The main goal of meter placement is determining the number, place, and type of meters that needs to be placed on a given feeder such that the SE with these measurements will have the desired performance as outlined in the previous section. However, as pointed out before, the cost considerations usually limit. the number of meters that can be placed on distribution feeders; usually below the minimum needed for state estimation (i.e./ the system won't be dbservable by using the actual meters alone). To overcome this observability problem, forecasted load data needs to be added as pseudo-measurements. Therefore, the main goal of meter placement in djstribution systems becomes supplementing the forecasted load data with real-time measurements such that the SE with these measurements will satisfy the performance requirements outlined in previous section.

Meter placement is a complex problem. This is not only due to size of the problem (number of choices available), but also often due to the conflicting requirements between the SE performance and the cost of the measurement system necessary to achieve the

desired performance. Here, rather than adopting a comprehensive formulation of this problem, such as the one developed for transmission systems [15], we developed a heuristic approach based on the following observations:

01: Since the automation functions to be implemented are usually prioritized as described in section 2, we don't have to estimate all the quantities with the same accuracy. For example, based on the performance requirements outlined in previous section, power flows at switching locations need to be estimated more accurately than the individual loads, since while the former data is needed for feeder switching the later data is mainIy for load monitoring and volt/var control which are of lower ranking functions than that of feeder switching in distribution automation. Therefore, placing meters at switch locations is a good choice since this will assure the most accurate estimation of power flows at khese points.

02: In 1111, it is observed that SE uses the measurements from the meters to "correct" loads in zones defined by these meters in a radial feeder. Hence, placing meters in such a way that they divide the feeder into meter zones with similar total load distribution will assure that the load estimation will be uniform, i.e., all the loads will be estimated with similar accuracy. Also, the smaller the load in zones the more accurate the load estimation would be.

03: Due to cost concerns, most of the measurements on the feeder are of current type rather than power, except the measuxement at the substation end of the feeder.

Based on these observations, a simple set of rules are developed for meter placement which can be outlined as follows.

Rule Based Meter Placement Scheme

Rule 1: Put meters at all the main switch and fuse locations that need to be monitored. These measurements will provide data especially for feeder switching and switch monitoriilg functions. In Fig. 1, meters ma and m i correspond to the meters placed according to this rule. One of these measurements must be at the substation end and be of power (kW and kVar) type, the other measurements can be of current type- $

Rule 2: Put additional meters alonzg the feeder line sections such that the total loads in the zones defined by the meters are similar in magnitude. These measurements can be of current type (if the cost of metering is to be kept to a minimum). If there are spot loads that are much bigger than the average load, putting meters on these loads usually helps to achieve this goal. The meter m2 in Fig.1 represents a meter placed to satisfy this rule. These additional meters improve the accuracy of load estimation.

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Rule 3 Put meters on normally open tie switches that are used for feeder switching. These meters can also be of current type. Voltage measurements at both ends of these tie switches are also desirable for monitoring and control of volt/var control devices from the substation and/or dispatch center [8]. Note that although these meters won't contribute to the accuracy of SE while the tie-switches are open, they will do so when these switches are closed for feeder reconfiguration or restoration.

The proposed method is a good compromise between the accuracy and the computational simplicity; it does not guarantee the optimality of the solution, but it is computationally simple. The method selects both the type and location of meters and tries to improve the accuracy of the data needed for both the feeder switching and the volt/var control. We will refer to the metering scheme this method produces as the basic metering scheme. Note that this method is specially tailored for radial feeders, a more comprehensive approach is needed for meshed networks.

Meter Ranking

Since in feeder operation, data needed for feeder switching is considered more important than the load distribution data, it may be hard to justify economically placing all the meters the above method will suggest. To help user eliminate some of the meters from this basic metering scheme, we adapted Koglin's method [13] to rank these meters. The ranking will indicate the order with which the meters are to be eliminated.

Ranking is based on the contribution of a measurement to the accuracy of the quantities that we want to estimate which are called interesting quantities, y. For example, for feeder switching functions, current flows at switching locations are the interesting quantities. For a given metering scheme z, we can use SE to estimate these interesting quantities since they can be expressed in terms of the system state variables, x (usually chosen as the node voltage magnitudes and angles) as

Accuracy with which the SE will estimate these quantities can be expressed in terms of the variances of these quantities,

To calculate these variances, we assume that the measurement errors are normally distributed, i.e.,

zi = k(x) + ei i=l- . .rn (2)

where hi(x) is the measurement functions that defines measurement zi in terms of the state x, and ei represents the measurement error. Then, the covariance matrix of interesting quantities can be calculated from [14]

R, = F(H~R-'H)-'F~ (3)

where H is the Jacobian of the measurement functions F is the Jacobian of the functions of interesting

quantities, f(x), R is a diagonal covariance matrix containing the

variances &i of the measurements zit i = 1, ..., m.

Variances 6 occupy the diagonal entries of the covariance matrix Ry. The variances 0 2 i of the measurements needed in above calculations can be obtained by using measurement accuracies azi, since

azi(%) = (&i/zi)*lOO. (4) Note that the smaller the variance of a quantity,

the more accurate the estimation of corresponding interesting quantity will be. Hence, we define a sysfem accuracy index as:

k

i=l a(z) = xO; i ( z ) (5)

and use it to rank the measurements in the basic measurement set ZO. To determine the ranking, measurements in Zo are eliminated one by one from this set as follows. First measurements are taken odt of the available measurement list one at a time and the resulting change in a(z) is calculated. The measurement which causes the least change in a(z) is then actually eliminated from the measurement set and the elimination process is repeated until all the measurements are eliminated and ranked.

User can use this ranking to reduce the meter selection and to reach a compromise between the performance and the cost; the basic metering scheme will yield the best performance but will cost the most. As one eliminates meters, cost will decrease, but the performance will be sacrificed as the variances $ i t s will get bigger. After selecting a reduced metering scheme, a SE based simulation can be used to assess the performance of the reduced measurement set. This process is illustrated in the next section. A three-phase SE should be used in simulations especially if the load unbalance between the phases in the system is severe (greater than 30%), otherwise balanced feeder approximations can be used for three phase feeders, to simplify especially the variance calculations.

V. TEST RESULTS

The proposed rule based meter placement method is implemented on a DEC Workstation environment and tested using four different size feeders. The results will be given here for an IEEE test feeder.

The test feeder is a 34 node, 23 kV, 3-phase radial IEEE test feeder [12]. A one-line diagram of the feeder is given in Fig.2 with the nodes renumbered to make the illustration of the results easier. The feeder is pre- dominantly three-phase with some single-phase laterals and has both spot and distributed loads. For test purposes, distributed line section loads are lumped equally at terminal nodes of the line sections. This

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meter 0 0 switch v1 fuse

Figure 2: One-line diagram of the test feeder

nominal load data is used in simulations. The voltage at the substation is held at the assumed measured value of 1.0 p.u. Forecasted loads and actual measurements are assumed to have 30% and 3% accuracy, respectively. A three-phase SE'with full Jacobian [ l l ] is used for calculating the estimate of the interesting quantities and their variances. Test results can be summarized in the following two parts.

Basic Meter Placement

The test feeder is assumed to have three line switches, three fuses and two tie switches as shown in Fig. 2. Basic meter placement considers feeder switching, switch monitoring, and load estimation. Thus, currents through the six switches, yo - y5 in the figure, and the loads are chosen as the interesting quantities. By applying the rules of the proposed meter placement method, meters are placed on the test feeder as follows. First, six meters are placed at the switch/fuse locations - meters mo - m5 in Fig.2 - using the first rule. Using the second rule, two more meters (mg & m7 in the figure) are placed on the feeder to distribute the total load evenly into meter zones. Finally, to satisfy the third rule, two more meters are placed at the open- tie switch locations TSWl and TSW2 shown in Fig. 2. All of these meters are of current type except mo which measures the power supplied to the feeder at the substation end. Meters are assumed to be measuring d l the phases individually.

For this basic metering scheme, variances of interesting quantities of current flows at the switch locations are given in Tb1.3 (except that of yo which is always measured). Tb1.3 shows that for all yi's c$- < 02 where o:i is the variance of measurement yi. This result indicates that, with this metering scheme, currents through the six switches will be estimated more accurately by using SE than by just directly measuring them. Furthermore, variance calculations for loads indicate that the ten biggest loads will be estimated with accuracies ranging between 11% and 25%.

Table 3: Variances with basic metering

2 8 4 1 =v5 1 42 I o v 3 I I ( 5 1 I 2 variances 2

Phasec I I 0.0013 I 0.0445 I 0.0209 I 1

Reduced Metering Scheme

The goal here is to reduce the basic metering scheme obtained above for the case in which feeder switching and switch status monitoring are the only objectives. Therefore, the interesting quantities are only the current flows at switch locations, yo - y6.

For this case, the meters of the basic metering scheme Zo are first ranked by using the proposed ranking procedure. The results of this ranking is summarized in Fig.4 which shows the order with which the measurements are eliminated to determine the ranking and the corresponding changes in the system accuracy index. The elimination order is from right to left, starting with m7.

+mo +m3 +m4 +mi tm2 +m6 tm5 +m7

Figure 4: meter ranking with respect to accuracy index

The results in Fig.4 indicate that the first five measurements have relatively low impact on the system accuracy index, and hence they can be eliminated. The variances of interesting quantities corresponding to this new reduced metering scheme is given in Tb1.4. Note that this reduced metering scheme uses only three measurements (mol m3, mq) at switch locations, and there are no measurements at fuse locations of y1, y2 and y5.

We tested the performance of this reduced metering scheme as follows. First, since yo, y3, and y4 are measured but not y1, y2, and y5, the main concern is about the estimation of these non metered interesting

Table 4: Variances with reduced metering

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Civanlar S., J. J. Grainger, Forecasfing Distribution Feeder Loads: Modeling b Application to Volt/Var Control, IEEE Trans. on Power Delivery, Jun. 1988, pp. 255-264. Shirmohammadi D., Seraice Restoration in Distribution Networks via Network Recon- figuration, IEEE Trans. on Power Delivery, April

Liu C-C, et. al, IOPADS (Intelligent Operational Planning Aid for Distribution Systems), IEEE PES Summer Meeting 1992, paper no: 500-9 PWRD. Broadwater R., and et. al, Time Varying Load Analysis to Reduce Distribution Losses Through Reconfiguration , IEEE Trans. on Power Delivery,

Roytelman I., and S.M. Shahidehpour, Sta te Estimation for Electric Power Distribution Systems in Quasi Real-Time Conditions, IEEE PES Winter Meeting 1993, paper no: 090-1 PWRD. Baran M.E., and A.W. Kelley, State Estimation for Real-Time Monitoring of Distribution Systems, IEEE Trans. on Power Systems, Aug.

IEEE W.G., Radial Distribution Test Feeders. IEEE Trans. on Power Systems, Aug. 1991, pp. 975985. Koglin H.J., Optimal Measuring System for State Estimation, Proc. PSCC Conference, paper no: 2.3/12, Sept. 1975. Clements KA., Observability Methods and Optimal Meter Placement, Int. J. of Electrical Power and Energy Systems, April 1990, pp. 88-93. Baran E.M., J. Zhu, H. Zhu, and &E. Garren, A Meter PZacement Method for State Estimation, paper presented at the IEEE PES Winter Meeting, Feb. 1995, paper no: 218-8 PWRS.

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quantities. The accuracy with which these quantities will be estimated can be calculated from Tb1.4, by adapting (4) which yields E%, 28%, and 20% accuracy for y1, y2, and y5, respectively. Note that these interesting quantities are to be used for monitoring the status of corresponding fuses only. Hence we need to check if this accuracy level is adequate for this purpose. Monitoring SW5 would be easy, since it is the only one in the zone of m4. SW1 and SW2 on the other hand are both in the zone of mo, and hence they need to be monitored by using mo. The SE based switch monitoring procedure outlined in Sec.3 will monitor these two switches successfully, since there is a big enough difference between y1 and y2. For example, for the nominal load considered, they would be estimated as:

91 = 18.87(1~.15) p.u. on phase "a" and 92 = 3.42(1~.28) p.u. on phases "a,b,c".

Hence, if a change in mo falls within the range of one of these estimated values then it will indicate a change in the status of the corresponding switch. A similar analysis will indicate that the reduced metering scheme can also monitor the capacitor banks by using m3.

VI. CONCLUSIONS

Test results indicate that even a few meters placed strategically on a radial distribution feeder may provide enough data needed for real-time monitoring of distribution feeders. The proposed meter placement and meter ranking methods are very effective in helping the user to identify these meters. The methods are also computationally simple and exploit the special features of radial feeders and feeder monitoring functions. We believe that the proposed methods would be a very valuable tool for distribution engineers in their planning of the next generation distribution SCADA and distribution automation systems.

Acknowledgments This research has been supported by EPRC,

NCSU, its members, and Pacific Gas and Electric Company.

REFERENCES

EPRI, GuideZines for Evaluating Distribution Automation, Nov. 1984, report no: EL3728. Brown D.L., et. al, Prospects of Distribution Auto- mation at Pacific Gas and Electric Company, IEEE Trans. on Power Delivery,, Oct. 1991, pp. 1946-1953. Rizy D.T., et. al, Dis t r ibu t ion Automut ion Applications Software f o r the Athens Utilities Board, presented at IEEE PES 1988 Winter Meeting, 1988, paper no: 097-8. Reed J.H., et. al, Monitoring Load Control at the Feeder Level Using High Speed Monitoring Equipment, presented at IEEE PES 1988 Winter Meeting, 1988, paper no: 095-2. Sun D.I.H., S. Abe, and et. al, Calculation of Energy Loses in a Distribution System, IEEE Trans. on PAS, July/Aug. 1980, pp. 1347-1356.

---- Mesut E. Baran received his B.S. and M.S. degrees in Electrical Engineering from Middle East Technical University, Turkey and his Ph.D. from the University of California, Berkeley. He worked for Empros Corp. for about one and a half years on Energy Management Systems. Later, he joined North Carolina State University where he is currently an Assistant Professor. His research interests include distribution and transmission power systems, optimization, and system theory.

Jinxiang Zhu received his B.S. degree in Electrical Engineering from Tsinghua University, Beijing, E'. R. China in 1990. Currently he is a graduate student in North Carolina State University.

Arthur W. Kelley received his B.S.E. from Duke University, Durham, North Carolina in 1979. He continued at Duke as a James B. Duke Fellow and received his M.S., and Ph.D. degrees in 1981, and 1984, respectively. After three years with Sundstrand Corporation he joined North Carolina State University where he currently holds the rank of Associate Professor.