Design and Implementation of Real-Time Off-Grid Detection Tool Based on FNET/GridEye Jiahui Guo1, Ye Zhang1, Yilu Liu1,2 Fellow, IEEE 1
The University of Tennessee Knoxville, TN {jguo7, yzhang86, liu}@utk.edu
Marcus Young1,2, Philip Irminger1,2, Aleksandar Dimitrovski2 2
Oak Ridge National Laboratory Oak Ridge, TN {youngmaii, irmingerp, ad1}@ornl.gov
Abstract—Real-time situational awareness tools are of critical importance to power system operators, especially during emergencies. The availability of electric power has become a linchpin of most post disaster response efforts as it is the primary dependency for public and private sector services, as well as individuals. Knowledge of the scope and extent of facilities impacted, as well as the duration of their dependence on backup power, enables emergency response officials to plan for contingencies and provide better overall response. Based on real-time data acquired by Frequency Disturbance Recorders (FDRs) deployed in the North American power grid, a real-time detection method is proposed. This method monitors critical electrical loads and detects the transition of these loads from an on-grid state, where the loads are fed by the power grid to an off-grid state, where the loads are fed by an Uninterrupted Power Supply (UPS) or a local backup generation system. The details of the proposed detection algorithm are presented, and some case studies and off-grid detection scenarios are also provided to verify the effectiveness and robustness. Meanwhile, how to implement the detection tool in real-time environment and its achievements in detecting off-grid situations are discussed in this paper.
Patrick Willging3 3
US Department of Energy Washington, D.C.
[email protected]
North American interconnections: Eastern Interconnection (EI), Western Electricity Coordinating Council (WECC), and Electric Reliability Council of Texas (ERCOT). Fig. 1 shows the current FDR deployment locations in North America. One fundamental application of a situational awareness system is event detection. Power system events such as generator trip, line trip, load shedding, and oscillation create perturbations in the voltage, frequency, and angle signals. These disturbances propagate throughout the electrical network in time and space. Using time-synchronized phasor and frequency measurements, it is possible to detect and locate these events in near real-time. Among these power system events, an off-grid operation is a situation during which some loads become electrically isolated from the remainder of the power system. Generally, the isolated power system is powered by UPS or local backup generators. The duration of the off-grid condition and the nature of the backup power source are key indicators for situational awareness, and usually very valuable for the grid operators.
Index Terms--Frequency disturbance recorder, Frequency deviation, FNET/GridEye, Off-grid detection.
I.
INTRODUCTION
Wide area measurement systems (WAMS) provide unprecedentedly deep insights into power system dynamics and facilitate monitoring, protection and control of the power system. The Frequency monitoring network (FNET/GridEye), operated by University of Tennessee and Oak Ridge National Lab (ORNL) provides a low cost and quickly deployable WAMS with high dynamic accuracy and minimal installation cost. The key component of this system, the Frequency Disturbance Recorder (FDR), could measure voltage magnitude, angle and frequency at a high precision from the 220V or 115V outlets. These measurements are calculated at 100ms intervals and then transmitted across the Internet to a central location, where they are synchronized, analyzed, and archived [1]-[4]. Since the system was initially deployed in 2004, more than 150 FDRs have been deployed in the three This work made use of Engineering Research Center Shared Facilities supported by the Engineering Research Center Program of the National Science Foundation and DOE under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program.
978-1-4799-6415-4/14/$31.00 ©2014 IEEE
Figure 1. Map of FDR locations in North America
This paper is organized as follows. In Section II, the detection algorithm is described in detail. In Section III, a UPS detection experiment is conducted to help tune the key parameters. Additional events are used to verify the robustness of the algorithm. Section IV briefly introduces the
implementation of this detection tool based on the Grid Solution Frameworks (GSF) [5] and presents an online monitoring interface. Several confirmed events are presented in Section V to help validate the performance of the detection tool. Section IV concludes this paper. II.
DETECTION METHOD
Power system frequency is an important indicator of the status of a power grid. Under normal operating condition, the power sources for equipment and devices are synchronized with the rest of the interconnection. Thus, the frequency deviation within the same interconnection should be identical. However, after a local blackout, UPS or backup generators are not electrically synchronized with the interconnection, resulting in a sustained frequency deviation between the isolated system and the bulk power system. When an FDR operates on UPS, the deviation between its reporting frequency and the frequency of the interconnection can be relatively large. This situation lasts until the isolated system returns to synchronism with the interconnection.
The integration of the frequency deviation F3i(t) continues until it falls below the third threshold Fth3 (6), indicating that the system has rejoined the power grid. F3i (T3 ) Fth 3
(6)
The flow chart of the detection algorithm is shown in Fig.2.
Comparing to the detection methods reviewed in [6], this paper proposes an integration trigger algorithm, which could achieve short detection time, high reliability and cheap implementation at the same time. This proposed algorithm uses the frequency deviation between the measured off-grid frequency and the reference frequency, which is defined as the median value of all the monitored FDRs in the same interconnection. The reference frequency fref (t) is given by f ref (t ) median( f1 (t ), f 2 (t ),..., fi (t ),..., f N F (t )), i 1, 2,...N F
(1)
where NF is the number of FDRs that are monitored in the interconnection, and fi(t) is the frequency value measured at time t by the i-th FDR. The frequency deviation Δfi(t) is calculated with (2). f i (t ) f i (t ) f ref
(2)
Once the frequency deviation F1i(t) surpasses the first threshold Fth1 given by (3), the integration process of the deviation starts. This integration is shown in (4) where the integration of the frequency deviation F2i(t) is defined for the i-th FDR, in time interval ΔT2. F1i (t ) Fth1
F2i (T2 )
Tend
t Tstart
f i (t ) f ref
(3) (4)
If F2i(t) is above a certain threshold Fth2, given by (5), it is determined that the system monitored by the i-th FDR is in off-grid operation. F2 i ( T2 ) Fth 2
(5)
Figure 2. Flow chart of the detection algorithm.
III.
OFFLINE EXPERIMENTS
A. UPS Detection Experiment Case 1: UPS In this experiment, five FDRs and one commercially available UPS were used. In order to obtain the real-time system frequency data, four FDRs (Unit812, Unit851, Unit865, and Unit873) were directly connected to the power grid and the other FDR (Unit874) was connected to the power grid through the UPS. In this situation, once the UPS is turned to “ON”, the FDR will measure the frequency of the UPS, and once the UPS is turned to “OFF”, the FDR will measure the frequency of the power grid. In one of the experiments, the UPS was switched to “ON” for around 1.5 minutes and then switched back to “OFF.” By doing this, the FDR connected to the UPS acquired the data needed to test the UPS detection algorithm. The real-time data was sent to the FNET/GridEye server and stored in MySQL database and MS Access files for offline analysis. Fig. 3 shows the frequency plot in a 3 minutes time window. Through several similar experiments, the thresholds are determined as Fth1 = 15mHz, Fth2 = 800mHz·s and Fth3 = 300 mHz·s. The two integration time are set as 30s and 40s, respectively.
Figure 3. UPS case
Figure 5. Line trip case
B. Validation of Detection Algorithm Robustness The detection algorithm for off-grid operation should not be falsely triggered during other power system events. The following cases are selected from FNET/GridEye historical detected events and are used to verify the robustness of the detection algorithm to ensure that the algorithm does not give a false positive during other power system events.
The proposed algorithm did not trigger under this case. The frequency deviation detected in this case was 8.45mHz, since even the first threshold was not reached, this case was not triggered.
Case 2: Generation Trip Fig.4 shows a generation trip detected by FNET/GridEye on November 14, 2013 at 17:18 (UTC) with an estimated 1200MW of generation tripped offline. For this event, the frequency deviation detected was 20.20mHz, which surpassed the first threshold. While the integration of the deviated frequency was 597.35mHz·s, which was below the second threshold, the detection process ended without been triggered. Figure 6. Load shedding case
Case 5: Oscillation Fig. 7 shows an oscillation case caused by an incident on the 500kV network detected on March 14, 2013 at 11:15:33 (UTC). The proposed algorithm did not trigger in this case. The frequency deviation detected in this case was 21.1mHz, the integration of the frequency deviation was 571.58mHz·s, thus it was not triggered.
Figure 4. Generation trip case
Case 3: Line Trip Fig. 5 shows a line trip case detected on September 24, 2013, at 11:55:15 (UTC), which is a typical line trip case. The frequency deviation detected in this case was 74mHz, the integration of the frequency deviation was 314.02mHz·s, thus it was not triggered. Case 4: Load Shedding Fig. 6 shows a load shedding case detected on November 24, 2013 at 09:45:29 (UTC) near Fresno, CA.
The results of these offline case studies based on historical data indicate that the detection algorithm is robust enough to be practically applied for off-grid detection in the FNET/GridEye situational awareness system. IV.
IMPLEMENTATION OF REAL-TIME DETECTION TOOL
The proposed algorithm has been implemented in FNET Server based on the GSF, which is an extensive open source collection of .NET code used by electric power utilities, more information can be found on [9]. Fig. 8 shows the hierarchical structure of the implementation of this detection tool with customized adapters. The FNETPhasorProtocol Parser is used to parse the frequency data stream from the monitored FDRs, and align the data based on their timestamps. Then the OffGridTrigger Adapter
Figure 7. Oscillation case
Figure 9. Online monitoring dashboard
processes the time-aligned data stream to detect the status of each monitored units. The status here is classified as 3 types (NODATA, ON GRID, and ON BACKUP). The configuration information is stored in MySQL database, including the geographic information of the devices which helps to identify the event location and the thresholds for the OffGridTrigger Adapter.
deviation 262.65mHz and an integration of frequency deviation 18204mHz·s, thus the off-grid operation was detected. Fig.11 shows when the islanded system returns to synchronism with the power grid while the integration of frequency deviation fell to 160.71mHz·s.
Grid Solutions Framework (.NET)
RealTime FNET Raw DataStream
Input Adapter
Action Adapter
FNETPhasorProtocol Parser
OffGridTrigger Adapter
Device Connection Settings
Monitored Device Filtering Trigger Thresholds Settings
MySQL
Figure 8. Implementation structure of the real-time detection tool
Figure 10. Sandy case when off-grid situation started
The detection tool runs once every 0.1 second to identify the status of each FDR. It then stores the detection result in MySQL database to further provide an XML feed to the offgrid event reporting module of Department of Energy. Then the online monitoring interface queries the database to obtain the status of each monitored FDR and then display them on a map every 5 seconds. Once an off-grid operation is detected, the web interface will highlight the FDR, indicate the geographic information, and issue an alert. Fig. 9 shows the interface when an event is detected in Oak Ridge, TN, which is described in Case 8. V.
DETECTED EVENT CASES
After being implemented in the FNET/GridEye system, this detection tool has detected several off-grid operation cases, the followings are shown to verify the effectiveness in real-time environment. Case 6: Sandy During Hurricane Sandy in 2012, an FDR in Sussex, NJ, detected off-grid status, which started on October 30 at 00:09 and ended on November 1 at 14:18 (UTC). Fig. 10 shows the start of the off-grid operation, with a detected frequency
Figure 11. Sandy case when off-grid situation ended
Case 7: Backup Generation Fig. 12 shows a backup generation case which occurred in Fredericksburg, VA on November 12, 2011. The event occurred from 19:30 to 19:50 (UTC) and was first detected by FDR857 with frequency deviation 639.7mHz. The integration of frequency deviation was 46564mHz·s, and was detected as off-grid operation. This event lasted around 20
minutes as shown in Fig.12, and when the integration of frequency deviation dropped to 133.98mHz·s, it was detected that it returned to grid-tied operation.
several events to confirm the validity of this method. Meanwhile, the online monitoring dashboard could benefit system operators in real-time monitoring. However, the trigger thresholds, as essential parameters for the performance of the detection algorithm, are determined experimentally and empirically. In this paper, uniform thresholds are used, but there might be a chance that the parameters need to be tuned to achieve smaller detection time delay in a specific scenario to boost the performance of the algorithm. Future work will concentrate on the development of a heuristic self-adaptive threshold tuner module to determine the trigger thresholds.
Figure 12. Backup generation case
Case 8: Microgrid at ORNL Distributed Energy Communication & Controls (DECC) Laboratory is a unique testing facility for dynamic controls for both rotating and inverter-based distributed energy (DE) resources in ORNL. The layout of the DECC facility and its connection to the bulk power system is shown in Fig. 13. The FNET/GridEye system is able to monitor the status of the microgrid through the utilization of FDRs installed on the microgrid [6]. In this case, the microgrid is isolated from the power grid and powered by its own DE. The frequency plot is shown in Fig.14. The frequency deviation, integrations of frequency deviation were respectively evaluated as 29.75mHz, 1308.7mHz·s and 100.23mHz·s, which triggered the detection algorithm, then an alert was issued on the online monitoring dashboard, as shown in Fig. 9.
Figure 14. Microgrid case at ORNL
ACKNOWLEDGMENT We would like to thank colleagues at the University of Tennessee, Knoxville for their helps and advices. We also would like to thank our FDR hosts at universities, high schools, companies, and individual residences for their participation in providing data for the analysis. REFERENCES [1]
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[4] Figure 13. DECC laboratory interfaced with the ORNL distribution system. [5]
VI.
CONCLUSION
The off-grid operation detection algorithm is proposed in this paper, and several case studies have been analyzed to verify the significance and robustness of this trigger algorithm for identifying the operation status of off-grid operation. Moreover, a real-time detection tool is implemented in the FNET/GridEye system and has captured
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