Eastern Interconnection Model Reduction Based on Phasor Measurements Yin Lei, Gefei Kou, Jiahui Guo Yilu Liu, Fellow, IEEE Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville, TN, USA
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Abstract—This paper presents a procedure for constructing the dynamic equivalent of large power system based on the Frequency Monitoring Network (FNET) measurements. It consists of determination of coherent regions, generator and load aggregation, and network reduction. The coherent regions are determined from FNET voltage angle responses using a divisive hierarchical k-means clustering algorithm. Generator and load aggregation based on the detailed model is performed. This procedure is applied to the US Eastern Interconnection (EI) system by building its dynamic equivalent model in PSS/E. Preliminary simulation results are provided. The dynamic equivalent of the EI system can be further developed for possible use as a decision aid for system operators. Index Terms—large–scale power system, dynamic equivalent, FNET, WAMS, coherency, aggregation, system reduction
I.
INTRODUCTION
Considering the size and complexity of the US power system, it is neither practical nor necessary to perform transient analysis and system security assessment with a detailed model. Therefore, to analyze the performance of this large-scale power system, dynamic equivalencing is an efficient way to reduce the size of the system significantly while maintaining an acceptable accuracy of dynamic behavior to certain disturbances. The evolving technology of wide-area measurement system (WAMS) and the use of synchronized phasor measurement units (PMUs) have made monitoring the power system dynamics in real-time a promising aspect to enhance and maintain power system stability [1]. In one implementation, WAMS can be used to improve dynamic models of power systems as aids to analysis and control design. One key aspect of electric power system analysis is the determination of a strong model for simulating power system dynamics. The accuracy of such model depends on the level of detail of equipment. However, in a typical utility system the sheer number of components can get exponentially large which put a burden on software simulators. This problem is This work was supported in part 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. This work was also supported in part by ABB.
978-1-4799-3656-4/14/$31.00 ©2014 IEEE
Reynaldo Nuqui, Senior Member, IEEE U.S. Corporate Research Center ABB, Inc. Raleigh, NC, USA
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particularly important in situations when quick decisions need to be made based on the simulation. The research in dynamic reduction of power system can date back to the late 1960s when several methods such as modal equivalent, coherencybased equivalent and evaluation equivalent have been developed. The modal equivalent method suggests eliminating those buses that are not easily affected by the disturbances in some areas. Since it is difficult to find these undisturbed buses and additional modification has to be made in the original dynamic simulation programs in order to make use of the state matrix of the equivalent equations, this method is not widely applied. In the 1970s, a novel coherency based method was presented, with the corresponding dynamic reduction programs, DYNRED [2], developed by the Electric Power Research Institute (EPRI). It identifies the coherent generators on the basis of the swing curves obtained by time domain simulations. The typical way of dynamic equivalence done by DYNRED is to model in sufficient detail of the system within the boundaries of a control center and approximate the external system. The external model needs to be commensurate with the dynamic problems at hand. For example, for transient stability problems it is necessary that the external model is accurate during the first swing as stability is determined during this period. Although this approach suffices for the operator of this control area, it does not provide sufficient modeling needs if one is interested in observing the propagation of a disturbance in a larger geographical region or control area. The NETOMAC program is based on the evaluation equivalent method [3], in which the complete structure and parameters of the external system are not required. Slow coherency technique [4] based on singular perturbation theory was developed in the early 1980s, which defines the coherent generators by means of modal analysis and coherency analysis. As an alternative to WAMS, FNET takes high accuracy, GPS-synchronized measurements at 120V in the distribution system level. FNET provides a low-cost, quickly deployable way for wide-area monitoring of interconnected power grids.
With continuous monitoring of large interconnected power systems, the opportunity exists to develop a higher-level dynamic model that can approximate very large power systems such as the US Eastern Interconnect. The electromechanical response of this large system to a large disturbance can be measured by massively deployed sensors, which are commonly known as the Frequency Disturbance Recorder (FDR). Currently, more than 190 FDR units have been deployed in the North American power grid, of which more than 100 FDR units are in the Eastern Interconnection. Fig.1 shows the current FDR locations in North America.
II.
DYNAMIC EQUIVALANCE OF EI SYSTEM
A. Coherency Determination To identify the coherent regions, clustering algorithm was applied on a data set consisting of the time-domain voltage angle responses of all FDRs in EI system during and following an oscillation. For a given oscillation event, the data set to be clustered can be presented by the following matrix. [
]
(1)
Where: : is the voltage angle. : is the time of the oscillation event. N: is the number of FDRs. An example of the time domain plot of voltage angle is shown in Fig.3. Coherency between FDR units can be determined from such a data set.
Figure 1. FDR location map
An information management system has been implemented on a server to detect and analyze power system disturbances in near-real time based on the FDR measurements, as shown in Fig.2. The information obtained from these units will give us a panoramic view of the North American power system, from which a higher-level dynamic model can be synthesized [5]-[7]. Figure 3. Voltage angle response from FNET
GPS
FDR1
FDR 2
End User FDR N
Webpage
Ethernet WAN
PC
Event Monitoring system Oscillation Monitoring system FNET Server
Database Server
Web Server
K-means is one of the most commonly used clustering algorithms. It is performed by minimizing the Euclidean distances between items and their corresponding centroids. However, K-means involves random generation of the initial centroids and the cluster number K must be given as a parameter, so the results often reach the local optima [8]. As in the coherency determination case, the total cluster number K in most cases are unknown, so this paper introduces the K-means based divisive hierarchical clustering algorithm. It starts from applying K-means clustering algorithm, while K=2, to the original data set, which is identified as level 0. After the clustering, it calculates the Euclidean distance between the two centroids found by K-means from (2).
Figure 2. Frequency Monitoring Network Architecture
This paper presents a detailed procedure of building the dynamic equivalent of EI system based on FNET measurements. The first step is coherency determination, followed by the aggregation of generators and loads, and network reduction. The reduced EI system is simulated in PSS/E, and preliminary simulation results are provided.
√∑ Where d is the Euclidean distance between centroid i and centroid j.
The Euclidean distance reflects the dissimilarity between each FDR unit in their response to the encountered oscillation. Based on the distance obtained by (2), a flag vector is then assigned to recognize the two clusters on the current level. When the distance is smaller than a threshold, the flag value is set to zero, which means it will stop splitting. Otherwise, the flag value is set to one and the K=2 K-means will continue to apply on this node. The flag vector for next level is updated by calculating the distance between new centroids. The algorithm will automatically stop when all the flag values in the vector of that level are set to zero. The K-means based hierarchical clustering algorithm utilized to group the FDR units into coherent clusters is described by the flow chart in Fig. 4. Start
Figure 5. Clustering results of FDRs B. Generator and Load aggregation After obtaining the coherent regions from FNET angle measurements, we are assuming that angle shifts from generation level to distribution level does not change the inner coherency of generators. Therefore, all EI generators can be associated to the determined clusters by their geographical locations. According to the 2011 Energy Visual Model, over 27,000 substations are spread around the whole EI system. The total generator and load capacities for each cluster are calculated based on the detailed model and the results are listed in Table I.
Input FNET angle data
From level 0
From the first flag=1 node
Nest level
Table I. Generator and Load capacities for each cluster
Apply K-means K=2 Nest node Get flag value for each children node
No End of this level No Yes All children flag=0 Yes End
Figure 4. The flowchart for the clustering algorithm The divisive hierarchical K-means clustering algorithm is applied on more than 50 oscillation cases selected from the FNET database. By statistically analysis, final groups of coherent FDRs are shown in Fig.5.
Cluster Number
FDR Units Included
Generation Capacity (MW)
Load Capacity (MW)
1
682,684
64613.99
71346.41
2
712,715
12868.78
10350.22
3
664,665
1581.87
4758.34
4
715,719
5439.64
442.83
5
729,730,740,726
11964.96
7117.85
6
720,739,760,777,832
26013.61
28615.81
7
620,755,778
46947.41
46110.48
8
667,707,733
29907.66
18789.02
9
523,692,770
44738.59
37712.85
10
601,785
77598.79
82824.38
11 12 13
623,663 672,673,669,674,753 710,713,797,830
49752.82 54369.59 23986.32
52439.02 50860.93 24832.3
14
670,679
6195.26
18585.12
15
686,688
59370.23
59916.54
16
786
25581.73
26042.5
17
759
53727.9
51992.95
18
767
55694.3
42274.88
19
756,781
25884.57
30334.01
20
621
24618.18
19062.76
21
696
50905.66
48127.22
C. Network reduction Load flows transferred between generators in each cluster of the detailed model are then calculated to determine if there is a tie line flow between clusters. One branch is then used to identify the tie line between two clusters. The tie lines are then retained in the reduced system, and all the other branches are eliminated. Then the structure of the proposed reduced EI model can be represented by Fig. 6.
1100MW generation trip at 0.5s was followed by an obvious frequency drop in the reduced EI system. Fig. 8 shows the measurements of a real generation trip event in EI system detected by FNET. The estimated amount is also 1100MW. As we can see from both figures, the frequencies drop roughly the same amount of 0.06Hz during a period of 20s.
Figure 7. Simulation results of frequency deviations during a 1100 MW generation trip case
Figure 6. Reduced EI system Structure III.
PSS/E MODEL AND SIMULATION RESULTS
Since PSS/E can accomplish nonlinear time domain simulations for the large-scale power system efficiently, this paper attempts to construct the equivalent model for the EI system under PSS/E with the procedure directly implemented in it. The equivalents are developed in both load flow model and dynamic model.
Figure 8. Frequency measurements of a 1100MW generation trip case
A. Load Flow Model Based on the aggregation and network reduction results, the EI model is then built in PSS/E. Clusters are connected by phase shift transformers. The phase shifters are adjusted so that the power transfers between clusters preserve the network loadflow on the portions of the system retained in full detail [9]. B. Dynamic Model To study the dynamic characteristics of the reduced EI model, generator, exciter and governor models are adopted. All the generators are first modeled as round rotor generators. The inertia constant H for each generator is estimated based on the FNET measurements of oscillation frequency. Each generator is attached with a simplified exciter and steam turbine governor [10]. Future work is planned for more accurate generator representation and includes other types of generators such as hydro, combustion turbine, etc. as needed. C. Preliminary Simulation Results To assess the validity of the reduced EI model, dynamic simulations are performed in PSS/E. As shown in Fig.7, a
Figure 9. Simulated frequency deviations caused by a line fault In Fig.9, a line fault on the transmission line from bus 2 to 7 was applied at 1.0 sec, and the fault was cleared after 10 cycles. The plot illustrates that the line fault causes dramatic oscillations in the EI system, and it is easy to visualize from the frequency deviation plot that there are at least two oscillation modes with one damped faster than the other. One of these modes is approximately 0.7Hz, which is close to an
inter-area oscillation mode in the EI system [11]. IV.
CONLUSIONS AND FUTURE WORK
This paper presents a procedure to construct the dynamic equivalent models of large-scale power systems. The procedure mainly consists of identification of coherent regions, aggregation of coherent generators and network reduction. The procedure is illustrated on the Eastern Interconnection and simulations in PSS/E are performed to assess the accuracy of the equivalent model. Results of the simulation show that the developed equivalent system has the capability in representing some of the dynamic characteristics of the original system. It is recognized that the accuracy of this model could not probably duplicate the more detailed model used in simulation. But for the target dynamical response, such high level models can be good decision aids for initiatives such as developing a national grid of a very large country like the United States. Another potential application could be simulating the faster than real time response of a large dynamic system as decision aids for power system operators. ACKNOWLEDGMENT This work was supported in part 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. Special thanks to Dr. Xiaoming Feng and Dr. Zhenyuan Wang of ABB for their support and technical guidance. The authors would like to thank all of the FDR host universities, companies, high schools and individuals throughout the world. REFERENCES [1]
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