Short-term Load Forecasting Based Capacity Check for Automated Power Restoration of Electric Distribution Networks

Transcription

1 1 Short-term Load Forecasting Based Capacity Check for Automated Power Restoration of Electric Distribution Networks Vaibhav Donde, Member, IEEE, Zhenyuan Wang, Member, IEEE, Fang Yang, Member, IEEE, and James Stoupis, Member, IEEE Abstract This paper concerns power restoration control in an electric distribution network after a permanent fault. It describes a process for online estimation of loads in a network, which further enables determination of the pre-fault load. The pre-fault load is used to adjust short-term forecasting based load profiles, which are then used in capacity check algorithm of the power restoration controller to determine a backfeed that is valid up to several hours subsequent to the restoration. The paper also introduces a concept of source capacity constraint estimator that resides on Distribution Management System (DMS), with a virtual IED interface that provides up-to-date and accurate information of source capacity limits to the restoration controller. The methodology as proposed in the paper is validated using a laboratory demonstration network setup. Index Terms feeder automation, distribution automation, restoration switching analysis, load profile, short-term load forecast, power restoration, network model. S I. INTRODUCTION MART Grid refers to electric power systems that enhance grid reliability and efficiency by automatically anticipating and responding to system disturbances [1]. To achieve smart grid at the power distribution system level, various automatic technologies have been attempted in the areas of system metering, protection, and control. Within these technologies, automated power restoration []-[4] is an important part of the smart grid puzzle. This paper focuses on the use of online generated and dynamically adjusted load profiles in the automated power restoration process in order to ensure reliable and safe restoration of the underlying distribution grid. A typical modern distribution network contains various types of switching devices such as circuit breakers, reclosers and sectionalizers. These devices are used for the purpose of feeder automation to automatically isolate a feeder fault and restore power to the disconnected loads due to the fault isolation. Identification of the appropriate switches to undergo switching poses a challenge. While the choice of switches for This work is supported by ABB Corporate Research funding. All authors are with the ABB US Corporate Research Center, 940 Main Campus Dr., Suite 300, Raleigh, NC 7606, USA ( s: Vaibhav.D.Donde@us.abb.com, Zhenyuan.Wang@us.abb.com, Fang.Yang@us.abb.com, James.Stoupis@us.abb.com ) /10/$ IEEE fault isolation is relatively easy, that of switches as well as the switching sequences for power restoration can be quite complex, due to the possibility of multiple alternative backfeed paths. The correct choice of such paths is contingent on the available capacities of the sources that participate in the back-feed, the power handling capacities of the devices (reclosers, switches, transformers etc.) lying on those alternative paths and the correct loading of the network feeders in the concerned paths. Various algorithms have been proposed in the literature to obtain the best back-feed path [5]- [8]. In principle, these algorithms have to scan all available back-feed paths directly (enumeration) or indirectly (via appropriate optimization) to identify the feasible ones that are capable of providing the extra loads without exceeding their sources capability limits, and the corresponding thermal and other limits of the lines and other devices. If multiple feasible paths are available for back-feeding of one group of disconnected loads, the best option may be selected, for instance, the one that results in the lowest loading level of the alternate source. The algorithms must ensure that the backfeed solution they approve for implementation is reliable and based on realistic network data, and it will indeed restore the unserved loads without causing interruption to other loads that are being served and post-backfeed network will operate safely. This paper addresses short-term forecasting based load variation and its consideration in an online power restoration algorithm for a distribution system network. Section II. discusses the basic concept using an illustrative example to emphasize a need for forecasting-based power restoration strategy. Section III. proposes a methodology using adjusted load profiles in online capacity check algorithms. Validation of the proposed algorithm using a laboratory demonstration setup is discussed in Section IV. Conclusions are presented in Section V. II. CONCEPTS, ILLUSTRATIONS AND REQUIREMENTS Figure 1 provides an illustration where the example electric distribution network contains three sources (S1 to S3), seven loads (L1 to L7) and various switching devices (Brk1 to Brk4 and SW1 to SW6). Devices SW, SW4 and SW6 serve as normally open (NO) tie switches. The tie switches make the

2 network electrically radial, where each and every load is supplied by only one source. It is important to ensure that the total load supplied by each source and the current flowing through each switching device is within their respective maximum capacities. As in Figure 1, source S1 serves L1, L, L6 and L7 (900A in total), which is less than its maximum capacity of 1000A. Similarly sources S and S3 supply 400A and 50A of load respectively, which are less than their respective capacities. Furthermore, the current flowing through each switching device is less than its maximum load current carrying capacity (1000A for Brk1, 900A for SW1, for example). Electric Distribution Network IED1 IED IED3 IEDn Power Restoration Controller Sensors and Actuators ` Distribution Management System Figure 1: Electric Distribution Network Example Each switching device is associated with an Intelligent Electronic Device (IED) which acts both as a sensor and an actuator driver for controlling the switch. IEDs sense the current through the switch and its terminal voltage, which enable them to locally detect a fault and send appropriate open/close commands to the switch to clear the fault. Referring to Figure 1, IEDs communicate in real-time with a power restoration controller (or a group of coordinated controllers). The controller periodically polls the voltage, current and status (open/close) data from the IEDs. When a switch locks out after its reclosing sequence to clear a permanent fault, the fault occurrence is communicated to the controller by report-by-exception. The fault reporting triggers the fault isolation and power restoration algorithms that reside at the controller. According to the resulting power restoration plan, the controller orders various IEDs to open or close their respective switches as appropriate. Consider for instance, a permanent fault occurring at load node L1. Switching device Brk1 goes through a reclosing sequence and finally opens (locks out) to isolate the fault from the upstream side. Device SW1 opens as ordered by the power restoration controller to isolate the fault from the downstream. Following the fault isolation, loads L and L6 remain unserved. The power restoration controller now has the option of closing any of the three tie switches SW, SW4 or SW6 so that the unserved loads are restored. It performs a capacity ` check to determine a feasible option. If SW is closed, source S would have to supply the additional load of L and L6, which is 700A. It is already supplying L3 and L4, which are 400A in total. Given its maximum capacity is 1000A, it has only 600A net capacity margin and the capacity check would not be successful in this case. However the capacity check will be successful when either SW6 is closed to provide a backfeed from source S1, or SW4 is closed to provide a backfeed from source S3. In the former case, source S1 would have to supply a total of 800A (80% of its maximum capacity) and in the latter case, source S3 would have to supply 750A (75% of its maximum capacity). The best option would be the one that results in the lowest loading of the source that supplies the back-feed, which is source S3 in our case. As it is apparent from the example above, the process of evaluating whether an alternate source is capable of supplying the unserved load requires knowledge of the load, in terms of its current or active-reactive powers. A straightforward way is to use the rated value of the load in the evaluation process as done in Figure 1. The advantage of this procedure is that it can be programmed offline as the load rating is known from the network configuration data. In many instances however this procedure may not be sufficient, as the actual load usually varies with time and may deviate considerably from its rated value. The load rating itself may also vary for different seasons or even for different times of the day. For instance, assuming that the loads L5 and L6 are industrial loads and the restoration as outlined above is carried out during the night hours when the rated load demands are typically low. It is very likely that the demands of these industrial loads will be significantly higher during the following day, and the new larger loads may possibly overload the source that supplies it. Suppose the rated demand of L5 increases to 00A (from 50A) and that of L6 to 350A (from 00A) during the day, it will result in the overloading of source S3. The overloading may trigger further undesirable consequences, such as tripping of overloaded components of the network or loss of other sensitive load. Thus it is important that the variation in the load should be given a consideration while performing the backfeed in order to avoid any future unwanted consequences. In other words, the load profile and forecasting must be accounted for while designing the restoration strategy. This paper addresses short-term forecasting based load variation and its consideration in an online power restoration algorithm for a distribution system network. A general topic of service restoration and reconfiguration of electric distribution networks is plentifully discussed in the literature [5]-[8]. Solutions have been proposed using various techniques including mathematical programming, heuristic methods, genetic algorithms and expert systems. Leading realtime solutions have been presented in [9]-[13]. Power restoration schemes based on peer-to-peer communications between the IEDs are discussed in [9]-[1]. A field-based restoration application using master-slave architecture is

3 3 discussed in [9]-[11]. The approach presented in [1] involves various team agents that work with each other to generate a step by step power restoration plan, where as a master to slave architecture for distribution automation is presented in [13]. The topic of load estimation for power restoration has received much less attention though. The approach in [1] does not take into account future load variations when the team agents coordinate to come up with a restoration solution. The solution in [13] pushes the consideration of future load variations to the users and a user needs to build the logic from the ground up. Nevertheless, Peponis [14] has briefly addressed load modeling and forecasting in their analysis, Hsu [15] has considered load estimation through a simplified approach that uses available feeder currents and Toune [16] has assumed constant contracted current modeling for the loads. The literature however lacks documentation of a detailed framework for incorporating load forecasting in the capacity check algorithm, and its implementation, and that precisely is a focus of this paper. The novelty of the present work lies in the process of online estimation and dynamic adjusting of loads for use in the subsequent capacity check algorithm for determination of appropriate back-feed strategy for load restoration. Rather than depending on the less reliable offline rated values of the loads in the capacity check process, the process estimates the pre-fault values of the network loads and uses them to appropriately adjust forecasted load profiles. The paper also describes a process to generate load profiles online when such profiles are not already available for the loads in the network. It also discusses the concept of source capacity constraint estimator that resides at DMS, with a virtual IED interface to provide up-to-date and accurate information of source capacity limits to the restoration controller. The articles [9]-[10] have described a process that helps users build the power restoration logics quickly and implement them in real-time for power restoration using static rated values of network loads. The present work builds upon [9]-[10] by incorporating online generated and dynamically adjusted load profiles to ensure reliable and safe restoration. III. THE ALGORITHM This section discusses a methodology to determine appropriate load estimates that should be considered by the capacity-check performed in the power restoration process. Section III. A. introduces the concept of short-term load profile, while Sections III. B. and C. describe estimation of the pre-fault network load using feeder currents obtained from IEDs. Section III. D. discusses how the load profiles, adjusted according to the pre-fault load, are used in the capacity-check algorithm. Section III. E. establishes a link between the load forecasting based restoration algorithm in the controller and DMS. A. Load Profile The load as termed here is the aggregation of all loads insides a load zone, defined as the area between two adjacent switches. Since any of the actual loads in the load zone can be turned on or off individually, the aggregated load can vary from time to time, sometimes significantly. Each load is typically associated with its variational pattern over a period of time, commonly known as a load profile. The loads tend to show different profiles in different seasons, as well as on different days (workdays, weekends and holidays). Table 1 shows a template to store a daily load profile for two seasons. Such load profiles will be used to forecast the future value of the load and use it in the application of feeder restoration, as explained in the later sections. s can be predefined when the restoration controller is configured; using information derived from DMS. If the information is not available at the restoration controller configuration time, the load profiles can be populated with rated maximum load values, and then populated online over time by the restoration controller. Season Table 1: Load Profile Template 0:00 1:00 Day hour hour (MW) (MW) 3:00 hour (MW) Summer Work day S W0 S W1 S W3 Summer Weekend/holiday S H0 S H1 S H3 Winter Workday W W0 W W1 W W3 Winter Weekend/holiday W H0 W H1 W H3 B. Online Estimation of Load Using IED Data Real-time currents flowing through any two adjacent switches are measured by the corresponding IEDs. Knowledge of switch current magnitudes and switch open/closed statuses (as known from corresponding IEDs) makes the load estimation possible. The load, being defined as an aggregation of individual loads between two adjacent switches, is estimated using Kirchoff s current law. Figure (a) illustrates the basic idea. With the network being radial and the upstream/downstream directions of all feeder sections being known, the load currents are estimated as an algebraic sum of the neighboring switch currents. The load complex power can be computed using the rated voltage of the load and its rated power factor. I1 I I1 I I1 θ 1 I 1 θ1 I θ Figure : (a) Left: Estimation of Load Current Magnitude, (b) Right: Estimation of Complex Load Current I θ

4 4 If the IEDs have a phase measurement capability, the complex load current can be derived as in Figure (b). The complex load current along with the knowledge of voltage phasor at the load node, the complex load power estimate can be derived. At a given time instant, the estimated load values along with the open/closed statuses of network switches fully describe the network state at that instant due to the network topological radiality. In order to enable estimation of the prefault load as discussed in the next section, past N samples of the network state are stored in the memory of the controller at a given time. An illustrative template for this memory buffer is shown in Table where each sample set contains the time stamp, estimates of each load (ampere, per unit or MW/MVar) and open/closed statuses of the switches at that time instant. Note that as only the past N sample sets are stored, the older sample sets get continuously overwritten by a newer sample sets. Table : Template for Storing Past N Sample Sets of the Network State Time instant T 1 (newest sample) T T N (oldest sample) Load 1 LD 11 LD 1 LD 1 N Load LD 1 LD LD N M M M M Load L LD L1 LD L LD LN Switch SwSt statuses 1 SwSt SwSt N C. Determination of the Pre-fault Load The pre-fault load determination algorithm resides in the restoration controller. When a fault clearing switch locks out to clear the fault (that is, isolate the fault from the upstream), the fault occurrence is communicated to the controller by its IED (report by exception). At this time however the loads estimated by the controller are after-fault, that is, all the downstream loads have been lost due to upstream fault isolation and their estimated values would be zero. It is thus necessary to estimate in the controller the load values just before the fault occurrence so that the latest prefault network state is identified for use in the capacity-check process of the restoration algorithm. This is accomplished by searching the buffer of N sample sets of the network states (as discussed in the previous section) backward in time from the fault clearing time instant. Note that the pre-fault switch open/closed statuses are known (same as post-fault, except that the fault clearing switch status reversed). Thus the buffer needs to be searched until a network state with pre-fault switch statuses is found. In order to achieve better stability of this algorithm and avoid errors stemming during the transitional time of the switch reclosing sequence, the earliest network state in the buffer having the pre-fault switch statuses is taken as the pre-fault state. Note that the pre-fault load values are obtained as part of the pre-fault network state. The size of the buffer and the time interval, over which the set of N network states is recorded, is determined according to the recloser settings inside the IEDs. The overall recording time period covered should at least be longer than the longest fault clearing sequence of the network. D. Using Dynamic Load Profiles in the Capacity Check Short-term load profiles and the pre-fault load estimates together enable the determination of load values to be used in the capacity check algorithm for back-feed. Depending upon the season and the day of the restoration, appropriate row from Table 1 is selected for this determination process. Load profile spanning 4 hours of data from this row is schematically depicted in Figure 3 (a). Given that such load profile only represents a forecasted load and the actual load will likely be different from it, a predefined (by user) p% higher margin is assumed in anticipation that the actual load will stay within this margin. If the estimated pre-fault load value LD ' (as obtained in the last section) is within this margin, the load profile is adjusted to reflect a p% increase and the maximum load within the next 4 hours (shown ' as LD max ) is predicted. This maximum estimate is then used in identifying a suitable back-feed through capacity check functionality. LD' p% higher 0:00 4:00 LD p% higher LD' max LD max adjusted 0:00 4:00 Figure 3: (a) Top: Pre-fault Load is within Adjusted Profile (p% higher), (b) Bottom: Pre-fault Load is outside Adjusted Profile (p% higher) Suppose however that the estimated pre-fault load value LD happens to be higher than that given by the margin, the profile is adjusted such that the estimated pre-fault load lies on the new adjusted profile. The maximum load level within the

5 5 next 4 hours (shown as LD max ) is obtained accordingly and used in appropriate back-feed identification (ref Figure 3 (b)). The 4 hour period, as considered here, is presumably the time during which the faulty section of the network would be repaired and the network is restored to its original pre-fault state. If however it turns out that no feasible back-feed could be found with the maximum value of the load over the 4 hour period, the maximum value of the load over a shorter period is ' LD max or LD max used (instead of using ). For instance, the maximum of the load over the next 9 hours can be chosen instead of 4 hours if it is known that the average duration for fault repair is 9 hours, with an indication to the customer that the restoration is valid only for the following 9 hours. If no feasible back-feed option is found even with this option, the maximum can be calculated over 6 hours and the capacity check for candidate back-feeds are tried again. This process is repeated until a feasible back-feed option is found or all the possibilities are exhausted. Note that the yearly load forecasts may not be available for some loads (or any of the loads) to undertake the process above. In such cases, initially when no information is available, the load profile can be set to the maximum available load ratings and thereafter automatically populated and updates online. Consider the worst case, where even after trying all possibilities (down to one hour of fault repairing time, for instance), no feasible back-feed is found to power the disconnected loads due to insufficient available capacities of the alternate sources and devices in those paths. A load management scheme and/or a demand response scheme that limits the load to existing available capacity can be instated through DMS, SCADA, Advanced Metering Infrastructure (I), or any other advanced technologies. Load management can limit the load under certain amount by shedding loads, while demand response encourages customers to reduce their load willingly via certain incentives in response to system reliability concerns. In addition to the load power levels, the load composition at a given load location may also vary with time. Load composition affects the load power factor and thus the current magnitude (or KVA) that the load draws. This would further affect results obtained from the capacity check algorithm. Residential loads could have a dominant induction motor load component in summer due to high air-conditioning load demand and a dominant resistive component in winter due to a high heating load. Load composition can even change substantially during the span of a day at some geographical areas. For such cases, the load profile template for each load as in Table 1 can be modified to include load composition information and can then be taken into account while estimating the pre-fault load composition to be used in the capacity check. E. Obtaining Source Capacity Limits from DMS Network stability and performance constraints play an important role in determining the real-time source capacity limits that can safely be used for load restoration. For instance, a source may have 1000A maximum current handling capacity; however the network voltage and steady state stability constraints may reduce the actual available capacity further down to 700A. If a source attempts to supply more load than that specified by this capacity limit, the system may suffer from sagging voltages that may trigger voltage collapse process. The power restoration controller must use the real-time capacity limit in capacity check algorithms, rather than the rated maximum capacity. DMS has a detailed model for the entire distribution network (within the territory of the utility) and is equipped with various network analysis functionalities that enable the DMS to estimate actual capacity limits of the alternate sources. Referring to Figure 1, the power restoration controller communicates with the DMS, where the network is analyzed in real-time using tools such as power flow and contingency analysis. Using these analyses, the DMS predicts the short-term (hours ahead) actual capacity limit of each source and communicates it back to the restoration controller. A virtual IED that estimates real-time source capacity constraints can run at the DMS server to respond to calls from the restoration controller and communicate the actual source capacity information to it for use in the power restoration logic generation. IV. EXPERIMENTAL SETUP AND VALIDATION The short-term load forecasting based capacity check algorithm was tested in ABB s field-based feeder restoration application. This section illustrates the setup used for this validation test. The experimental setup includes a low-voltage laboratory circuit to simulate a power distribution network, IEDs that associate with switches in the network, and a restoration controller where the feeder restoration application and a real-time network simulator (explained in the next section) reside. DMS and its source capacity estimator having a virtual IED interface (to communicate source capacity data to the restoration controller) are omitted from this exercise. That is, the restoration algorithm works with source capacities as known to the restoration controller. A. Test Setup Figure 4 shows a screen-shot of the feeder restoration application graphical interface loaded with a three source feeder network for testing the load forecasting based capacity check algorithm. This application communicates with IEDs in the network in real-time. For cases where a few switches do not have physical IEDs associated with them, this application provides simulated IEDs along with a real-time network simulation. Thus the power restoration process can be carried out when all or none of the network switches have their IEDs

6 6 physically present, and when a few switches communicate with physical IEDs and others need a simulated IED. In the present example, switch R1 is associated with a physical IED, while switches R-R5 use simulated IEDs. This is also reflected in the bottom left section of Figure 4. step, time scaling with respect to the real-time using the interactive controls in this window. Figure 5: Load Properties and Load Profile 14 1 Figure 4: Example Feeder Network for Load Forecasting Based Capacity Check and Power Restoration Switches R4 and R5 are Normally Open (NO). Each load is characterized by its 4 hour profile, with Figure 5 showing the screenshot of the corresponding Load Properties tab. The Mean Time To Repair (MTTR) parameter (for both upstream and downstream feeder, separately) provides the average time in hours to repair the fault on that feeder section. The Growth Rate parameter provides the rate at which the load would increase year by year, starting from the Base Year. The load profile provides the information of the daily load variation, on a weekday or a weekend day, for Fall, Winter, Spring and Summer seasons. Loads L1 and L3 are assumed to have identical profiles. In fact, Figure 5 shows properties of load L1 (or L3) with the profiles in the tables at the bottom of the figure. Loads are assumed to peak at 4:00 and reach the lowest point in their variation at 5:00, with a sinusoidal variation assumed throughout. The profiles are pictorially represented in Figure 6 for illustration. Bottom-right corner of Figure 4 shows a real-time network simulator window with details of the simulation. This simulator resides in the restoration controller and the real-time simulation is performed to accommodate simulated IEDs in the absence of complete physical network model. The power restoration controller communicates with the physical IED of R1 in real time (note the communication active icon next to R1). The network simulator periodically runs a network power flow based on the load profile, growth rate, current year (to compute load growth) and estimated random fluctuations in the load. The estimated load power values are displayed in the simulator window. The user can set the start date and time of the simulation as well as simulation properties such as time :00 :00 4:00 6:00 8:00 10:00 1:00 :00 4:00 L1, L3 (MW) L (MW) 6:00 8:00 Figure 6: Load Profiles of Loads: L1, L and L3 B. Test Cases and Validation 10:00 Test case 1 is carried out with MTTR for all the loads set to 5 hours, both upstream and downstream. The simulation start time is set to be 4:00am, so as to simulate the fault isolation and restoration process occurring in the early morning. A permanent fault is placed (physically, on the laboratory test network setup) on the downstream terminal of R1, where load L1 is located. The fault triggers the reclosing operation of R1, which locks out to isolate the fault from the upstream. This triggers automation algorithms in the power restoration control to create isolation and restoration logics. The fault isolation switch is identified as R. There are two possibilities for direct restoration of the lost loads (L and L3), that is, either by closing R5 or by closing R4. The load restoration based capacity check requires the knowledge of the maximum load at each of the load buses during the mean time to repair the fault, which is 5 hours for the present test case (until 9:00am). Referring to Figure 5 and Figure 6, the maximum values of the loads during this time are about 9MW, 4.5MW and 9MW respectively. These values are passed to the capacity check

7 7 algorithm next. The network simulator runs a power flow based on these MW values to produce branch current flow estimates. These are compared against the corresponding maximum capacity values to test whether the closing of R5 would violate network device/source capacities. In the present case, the capacity check is successful and R5 is issued a close command. Figure 7 shows a screenshot of the restored network. Thus R3, R4 and R5 are issued appropriate commands to restore the previously unserved load L and L3. Figure 8 shows a screenshot of the restored network. It should be noted that the MTTR has been arbitrarily chosen as 5 hours and 1 hours in the laboratory setup in order to test if the algorithm works. A particular utility may have more or less MTTR hours and the algorithm does not depend on the specific values of MTTR. V. CONCLUSIONS This paper presents a methodology to determine an appropriate backfeed strategy for distribution network power restoration while taking into account short-term load forecasts. The strategy uses online estimation of feeder loads so as to adjust load profiles appropriately, or generate load profiles online in case they are not available. The real-time source capacities are obtained from the DMS. These characteristics of the power restoration control process ensure that unserved loads due to fault isolation are restored in a reliable manner and no network elements are overloaded. VI. ACKNOWLEDGMENT Figure 7: Screenshot of the Restored Network for Test Case 1 Figure 8: Screenshot of the Restored Network for Test Case Test case is carried out using the same setup, the MTTR however is now set to 1 hours. This leads the capacity check algorithm to consider the maximum of the predicted load until 4:00pm for each load. Referring to Figure 6, these values are 1MW, 6MW and 1MW for the loads L1-L3 respectively (larger than the ones from the previous test case). Consequently, no single source based backfeed passes the capacity check, as no single source is capable of serving the peak load fully by itself. The restoration algorithm finds a feasible solution that uses a multi-source based backfeed that involves the opening of R3 and the closing of R4 and R5. The authors would like to thank ABB Corporate Research for the funding support of this research and gratefully acknowledge discussions with Dr. Mohamed H. Maharsi, ABB USCRC. VII. REFERENCES [1] [] G. Ockwell, Implementation of Network Reconfiguration for Taiwan Power Company, IEEE PES General Meeting, 003. [3] D.M. Staszesky, D. Craig, C.Befus, Advanced Feeder Automation Is Here, IEEE Power & Energy Magazine, Sept./Oct [4] J. Fan, X. Zhang, Feeder Automation within the Scope of Substation Automation, Power System Conference and Exposition, Nov [5] T. Taylor and D. Lubkeman, Implementation of Heuristic Search Strategies for Distribution Feeder Reconfiguraiton, IEEE Transactions on Power Delivery, January [6] Y. Moon, B. Cho, H. Park, H. Ryu, B. Ha, and S. Lim, Fault Restoration Algorithm Using Fast Tracing Technique based on the Tree- Structured Database for the Distribution Automation System, IEEE PES Summer meeting, 000. [7] C. Huang, Multi-objective Service Restoration of Distribution Systems Using Fuzzy Cause-Effect Networks, IEEE Transactions on Power Systems, Vol. 18, No., May 003. [8] K. N. Miu, H. D. Chiang, B. Yuan, G. Darling, Fast Service Restoration for Large-Scale Distribution Systems with Priority Customers and Constraints, IEEE Transactions on Power Systems, Vol. 13, No. 3, August [9] Z. Wang, V. Donde, F. Yang and J. Stoupis, A Deterministic Analysis Method for Back-feed Power Restoration of Distribution Networks, Proceedings of the IEEE PES General Meeting, Calgary, July 009. [10] F. Yang, Z. Li, V. Donde, Z. Wang, J. Stoupis, Graph Theory-Based Feeder Automation Logic for Low-End Controller Application, Proceedings of the IEEE PES General Meeting, Calgary, July 009. [11] ABB, Patent Application E , Advanced Feeder Architecture with Automated Power Restoration [1] S&C Electric, Patent Application US007/ A1, Method and Apparatus for Control of an Electric Power Distribution System in Response to Circuit Abnormalities [13] NovaTech, Distribution Automation Orion Application Note,

8 8 [14] G. Peponis, M. Papadopolous, Reconfiguration of radial distribution networks: application of heuristic methods on large-scale networks, IEE Proceedings Generation Transmission Distribution, Vol. 14, No. 6, November 1995 [15] Y.-Y. Hsu, H. Huang, H. Kuo, et al Distribution system service restoration using a heuristic search approach, IEEE Transactions on Power Delivery, Vol. 7, No., April 199 [16] S. Toune, H. Fudo, T. Genji, et al A reactive Tabu search for service restoration in electric power distribution systems, in proceedings of IEEE International Conference on Evolutionary Computation, 4-9 May 1998, pp VIII. BIOGRAPHIES Vaibhav Donde (M 001) joined ABB US Corporate Research Center in Raleigh, North Carolina in 006, where he is currently a Consulting R&D Engineer. Prior to joining ABB, he was a postdoctoral research fellow at Lawrence Berkeley National Laboratory ( ). He holds M.S. (000) and Ph.D. (004) degrees, both in electrical engineering from the University of Illinois at Urbana-Champaign and a B.E. degree (1998) in electrical engineering from V.J.T.I., Mumbai, India. His technical interests include power system analysis, modeling and simulation, EMS/DMS applications, power distribution system automation and nonlinear control. Zhenyuan Wang (M 000) joined ABB US Corporate Research Center in Raleigh, North Carolina in 000, where he is currently a Principal Consulting R&D Engineer. His research interests include electric power equipment condition monitoring/assessment/diagnosis, system monitoring, control and automation for a smart grid. His experiences include asset management IT applications in the electric power industry, power system transient analysis, substation/distribution automation, and data integration/warehousing/mining applications. Fang Yang (M 007) joined ABB US Corporate Research Center in Raleigh, North Carolina in 007. Her research interests include distribution automation and voltage/var optimization, power system reliability analysis, the application of artificial intelligent techniques in power system control. James Stoupis (M 1991) is a Principal Consulting R&D Engineer in the Power Technologies Department for ABB s US Corporate Research Center located in Raleigh, North Carolina. Jim has been employed at USCRC for 1 years, and his research has been focused in the areas of distribution and feeder automation, wireless communications, power system protection and control, and event detection and classification.