disturbance detection of wide area power systems by use of

Disturbance Detection of Wide Area Power Systems by use of Short-term Synchrophasor Data

Y. Ota Department of Electric and Electronic Engineering

Tokyo City University Tokyo, JAPAN [email protected]

Abstract—Phasor measurement units have been installed on the power systems for grasping system static and dynamic behavior over wide area. Synchrophasor brings effective information of the power system disturbances such as the sudden supply and demand imbalance of the system. However, huge amount of synchrophasor data over the wide area power systems requires some techniques to extract the disturbances. In this paper, a screening scheme of the power system disturbances and their occurring area is proposed based on the experiences on actual data handling of the synchrophasor data. The proposed scheme would be suitable for online application because of using short-term data set of the synchrophasors.

Index Terms--Synchrophasor, Grid Frequency, Loss of Generation, System Islanding, Online Application

I. INTRODUCTION WASA (Wide Area Situational Awareness) brings the

adaptive operation, protection, and control over the wide area power systems. PMU (Phasor Measurement Unit) is expected to be a key device to obtain system-wide information [1]. Hundreds of PMUs are already installed into the worldwide power systems [2]. The author has experiences on the Campus WAMS project [3], in which the PMUs are installed on the university campuses covering all the operating area in Japanese power systems.

Synchrophasor measurements at multiple points of the power system are effective for the model validation, oscillation assessment, controller tuning of the stabilizers, and so on. Moreover, recognition of abnormal conditions of the synchrophasor data caused by the power system disturbances such as the configuration change by the line trip, the generator and/or load trip, and sudden power change of the renewable energy sources would be useful for adaptive remedial actions of the generators and other controllable devices.

In this paper, the power system disturbance detection method based on the synchrophasor data is proposed. For realizing online calculation, the proposed method does not need the complicated processing, and only use short-term data set of the synchrophasors. The reality and effectiveness of the

proposed method is validated on the measured synchrophasor data set during three years by the Campus WAMS project.

II. CAMPUS WAMS The operation areas of the power companies in Japanese

wide area power systems are interconnected as shown in Fig.1. Eight PMUs are installed on the power outlets of the university campus. The instantaneous values of power outlet voltage are measured with precise time-synchronization. The phasor angle is calculated from the instantaneous values in every period of 1/30[s] for the 60Hz systems. The phasor angles, effective values, and time stamps during twenty minutes are stored to one data file. Three files are generated in one hour. Then data files of the PMUs are collected to a server. In this research, 630720 files during three years, from 2006 to 2008, are processed for the power system disturbance detection.

III. DISTURBANCE DETECTION The supply and demand imbalance caused by disturbances

is appeared as change of the power system frequency. Figure 2 (a) shows the frequency measurements in case of a scheduled generator trip test, which is known by the power company’s press release. The frequency was decreased just after the generator trip at 600[s], and was recovered by the load frequency control of another generators.

Figure 1. Installation of PMUs in Japanese 60Hz system.

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Tokushima$University�

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(a) Frequency measurements

(b) Standard deviations of frequencies

(c) Phasor angle differences from NIT Figure 2. Synchrophasors of generator trip case.

This change of frequency can be sharply extracted by the standard deviation of the frequency measurements as shown in Fig.2 (b). The windows width of the moving average for calculating the standard deviation is 10[s], and the threshold for the disturbance detection is 0.05Hz. Disturbance area can be also detected by the phasor angle differences after the detected disturbance time. Fig.2 (c) shows the phasor angle differences from the phasor angle of NIT. The phasor angle is smallest at NIT after the frequency decrease, so it is estimated that interchanged power flows into NIT area from another areas. Therefore, the disturbance area is detected as around NIT, which is same location as reported by the power companies’ press release information.

Figure 3 shows a system islanding case. As the press release of the Japanese transmission system operator, the interconnected lines between Osaka and Fukui area were accidentally tripped, and Fukui area was separated from the main power system. The frequency of Fukui area is increased, on the other hands, the frequencies of other areas are decreased (Fig. 3 (a)). So the disturbance can be effectively detected by the standard deviation of the frequency measurements (Fig. 3 (b)). And the separation area can be identified because the phasor angle of Fukui area is obviously away from the phasor angles of other areas just after the disturbance detection time (Fig. 3 (c)).



(c) Phasor angle differences from NIT

Figure 3. Synchrophasors of system islanding case.

In those ways, the disturbances and their occurring area can be effectively detected by the short-term data set of the synchrophasors. It is well known that global and/or local oscillations among the multiple synchronous generators are induced by the system disturbances. The author also proposes the online stability assessment method combining the proposed disturbance detection scheme and the nonlinear Koopman mode analysis of short-term synchrophasor data just after the disturbance [4] (Appendix. A).

IV. EVALUATION OF DISTURBANCE DETECTION The standard deviations of the frequency measurements

during three years are shown in Fig. 4. The standard deviation is set as zero when there are mission data or time stump error. The Campus WAMS uses general-purpose internet, and data gathering depend on network conditions. Indoor setup environment of the PMU often make the GPS antenna sensitivity difficult. It is found that total eighteen disturbances are detected by the proposed method.

The date, time, type, and location of the disturbance estimated by the proposed method are compared to released information about accidents and scheduled tests on the major power plant, as shown in Table. 1. Eleven cases would be caused by the generator trip, and five of the cases are confirmed by the released information.

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(a) Year : 2006

(b) Year : 2007

(b) Year : 2008 Figure 4. Standard deviations of frequency measurements.



(c) Phasor angle differences from NIT

Figure 5. Synchrophasors of load separation case.

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Table 1. Estimated disturbances and released information.

KIT$

Hiroshima$ Osaka$

Fukui$

NIT$

KIT$

Hiroshima$

Osaka$Fukui$

NIT$

Tokushima$

Koopman$Mode$Analysis$

Six frequency decrease cases caused by the sudden load separation are also detected by the proposed method. An example of the load separation in around NIT is shown in Fig. 5. The system islanding is only one case during three years experiences, as shown in Fig. 3.

V. CONCLUSION In this paper, the power system disturbance and area

detection method is proposed. The standard deviations of the frequencies show abnormal conditions on the supply and demand imbalance of the systems well. The phasor angles indicate information about the disturbance location in the systems.

The proposed method consists of simple signal processing and quantitative evaluation to the synchrophasor data. Online implementation of the proposed method is expected to be effective as the supervisory information for the adaptive remedial actions and the special protection schemes by the centralized and distributed control devices in each power system operating area.

ACKNOWLEDGMENT The authors gratefully acknowledge the data analyses of

Y. Nishio, and the contributions of M. Aoki and H. Ukai.

APPENDIX A The synchrophasor data bring information of power

oscillations among the multiple synchronous generators. In reference [4], the Koopman Mode Analysis (KMA) is applied to the short-term synchrophasor data measured by the Campus WAMS. Oscillatory characteristics of the western longitudinal power system are evaluated by the Koopman eigenvalues and modes.

Figure. 6 show a short-time data set of synchrophasors after the disturbance detected by the proposed method. The phasor angles of each area are offset at 600 second when the scheduled generator trip was initiated in NIT area. The Koopman eigenvalues and modes are approximately computed by use of the dataset during 8 seconds (240 samples) after the trip, encompassed by the block in the figure.

The Koopman eigenvalues in which the norms of the Koopman modes are dominant are shown in Table. A. A DC component and two oscillation modes are clarified by the KMA, and the residual are within 10-12. Mode 1 shows a typical feature of global oscillation that is reported in previous literature [3] and has significant amplitudes and opposite phases in both end of the system as shown in Fig. 7. Mode 2 seems to be well-damped harmonic oscillation.

The KMA can detect the inherent, global oscillatory mode of the system which is previously discussed in operation and control of the Japanese power system. And the proposed disturbance detection method is effective to extract the dataset, in which disturbance induced oscillation components are appeared.

REFERENCES [1] A. G. Phadke, R. M. de Moraes, “The Wide World of Wide-area

Measurement”, IEEE Power and Energy Magazine, Vol.6, Issue.5, pp.52-65, 2008.

[2] NASPI (North American SynchroPhasor Initiative) [Online]. Available: https://www.naspi.org.

[3] Y. Ota, T. Hashiguchi, H. Ukai, M. Sonoda, Y. Miwa, and A. Takeuchi, “Monitoring of Interconnected Power System Parameters using PMU based WAMS”, Proc. IEEE PowerTech Conference, pp.1718-1722, 2007.

[4] Y. Ota, Y. Susuki, F. Raak, and I. Mezić, “Koopman Mode Analysis of Synchrophasor Data in Japanese 60-Hz Power System”, Annual Meeting Record I.E.E Japan, Vol.6, pp.156, 2015.


(c) Phasor angle differences from Tokushima Figure 6. Short-term synchrophasors after disturbance.

Table 2. Parameters of Koopman eigenvalues and modes.

Figure 7. Koopman modes.

disturbance detection of wide area power systems by use of

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