Abstract
Time synchronized phasors obtained using Phasor Measurement Units (PMU) spread across wide areas have revolutionized power system monitoring and control. These synchronized measurements must be accurate and fast in order to comply with the latest IEEE standards for synchrophasor measurements. The speed at which a PMU provides an output depends on the group delay associated with that PMU and the permissible group delay in-turn decides the utility of a PMU for either control or measurement application. Based on the group delay compensation techniques, in the literature, two individual types of PMUs, such as causal and non-causal PMUs have been introduced. This paper presents an approach where both causal and non-causal PMUs are combined in an integrated PMU architecture. This method not only illustrates the group delay performance of two PMUs in a single module, but also can be used for multiple functions. In this environment several PMU algorithms have been compared with respect to their group delays and their effect on the response time. Application of the integrated PMU architecture to a four-machine 10-bus power system has been demonstrated using a six-input PMU with three-phase voltage and current signals as inputs. Different causal compensation schemes are introduced due to the availability of voltage and current-based frequency and ROCOF signals. Impact of these compensation schemes on PMU accuracy is evaluated through the Total Vector Error (TVE) index. The influence of these compensation schemes on measurements like power and impedance is also investigated. Finally, outputs from the integrated PMU architecture are fed into a Power System Stabilizer (PSS) to control the small-signal stability performance of a power system during dynamic conditions.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. NERC. Real time application of synchrophasors for improving reliability. Village Blvd-Princeton, NJ: North American Electric Reliability Corporation; 2010:116–390 pp.Search in Google Scholar
2. Ivanković, I, Brnobić, D. Distance protection based on the synchrophasor data in control room. Istanbul, Turkey: 9th European Conference on Renewable Energy Systems; 2021.Search in Google Scholar
3. Martinez, EV, Serna, JA. Smart grids part 1: instrumentation challenges. IEEE Instrum Meas Mag 2015;18:6–9. https://doi.org/10.1109/mim.2015.7016673.Search in Google Scholar
4. Serna, JA, Vzquez Martnez, E. Smart grids part 2: synchrophasor measurement challenges. IEEE Instrum Meas Mag 2015;18:13–6. https://doi.org/10.1109/mim.2015.7016675.Search in Google Scholar
5. Dai, Z, Tate, JE. Emulating synchrophasor frequency measurements with transient stability simulation. IEEE Trans Power Syst 2021;36:1. https://doi.org/10.1109/tpwrs.2021.3062906.Search in Google Scholar
6. PGCL. Unified real time dynamic state measurement. India: Power Grid Corporation of India Limited Gurgaon; 2012.Search in Google Scholar
7. Zhang, F, Cheng, L, Gao, W, Huang, R. Synchrophasors-based identification for subsynchronous oscillations in power systems. IEEE Trans Smart Grid 2019;10:2224–33. https://doi.org/10.1109/tsg.2018.2792005.Search in Google Scholar
8. Jiang, T, Li, X, Bai, L, Li, F. Synchrophasor measurement-based modal analysis in power grids. In: 2019 North American power symposium (NAPS). Wichita, KS, USA: IEEE; 2019:1–5 pp.10.1109/NAPS46351.2019.9000237Search in Google Scholar
9. Standard. IEEE standard for synchrophasor measurements for power systems. IEEE Std C37.118.1-2011 (Revision of IEEE Std C37.118-2005); 2011:1–61 pp.Search in Google Scholar
10. Agarwal, P, Agarwal, V, Rathour, H. Application of pmu-based information in the Indian power system. Int J Emerg Elec Power Syst 2013;14:79–86. https://doi.org/10.1515/ijeeps-2013-0019.Search in Google Scholar
11. Shi, J, Foggo, B, Kong, X, Cheng, Y, Yu, N, Yamashita, K. Online event detection in synchrophasor data with graph signal processing. In: 2020 IEEE International conference on communications, control, and computing technologies for smart grids (SmartGridComm). Tempe, AZ, USA: IEEE; 2020:1–7 pp.10.1109/SmartGridComm47815.2020.9302947Search in Google Scholar
12. Liu, H, Qi, Y, Zhao, J, Bi, T. Data-driven subsynchronous oscillation identification using field synchrophasor measurements. IEEE Trans Power Delivery 2021;36:1. https://doi.org/10.1109/tpwrd.2021.3054889.Search in Google Scholar
13. Adhikari, P, Hooshyar, H, Vanfretti, L. Experimental quantification of hardware requirements for fpga-based reconfigurable pmus. IEEE Access 2019;7:57527–38. https://doi.org/10.1109/access.2019.2911916.Search in Google Scholar
14. Phadke, AG, Thorp, JS. Synchronized phasor measurements and their applications. US: Springer; 2008.10.1007/978-0-387-76537-2Search in Google Scholar
15. Yamada, T. High-accuracy estimations of frequency, amplitude, and phase with a modified dft for asynchronous sampling. IEEE Trans Instrum Meas 2013;62:1428–35. https://doi.org/10.1109/tim.2013.2239031.Search in Google Scholar
16. Dotta, D, Chow, JH. Second harmonic filtering in phasor measurement estimation. IEEE Trans Power Deliv 2013;28:1240–1. https://doi.org/10.1109/tpwrd.2013.2242701.Search in Google Scholar
17. Akke, M, Thorp, JS. Sample value adjustment improves phasor estimation at off-nominal frequencies. IEEE Trans Power Deliv 2010;25:2255–63. https://doi.org/10.1109/tpwrd.2010.2052114.Search in Google Scholar
18. Ghafari, C, Almasalma, H, Raison, B, Hadjsaid, N, Caire, R, Martin, E. Phasors estimation at offnominal frequencies through an enhanced-sva method with a fixed sampling clock. IEEE Trans Power Delivery 2017;32:1766–75. https://doi.org/10.1109/tpwrd.2016.2597778.Search in Google Scholar
19. Maharjan, S, Peng, J, Martinez, J, Xiao, W, Huang, P, Kirtley, J. Improved sample value adjustment for synchrophasor estimation at off-nominal power system conditions. IEEE Trans Power Delivery 2017;32:33–44. https://doi.org/10.1109/tpwrd.2016.2586946.Search in Google Scholar
20. Xia, T, Liu, Y. Single-phase phase angle measurements in electric power systems. IEEE Trans Power Syst 2010;25:844–52. https://doi.org/10.1109/tpwrs.2009.2031649.Search in Google Scholar
21. Zhan, L, Liu, Y, Liu, Y. A clarke transformation-based dft phasor and frequency algorithm for wide frequency range. IEEE Trans Smart Grid 2018;9:67–77. https://doi.org/10.1109/tsg.2016.2544947.Search in Google Scholar
22. Belega, D, Petri, D. Accuracy analysis of the multicycle synchrophasor estimator provided by the interpolated dft algorithm. IEEE Trans Instrum Meas 2013;62:942–53. https://doi.org/10.1109/tim.2012.2236777.Search in Google Scholar
23. Dervikadi, A, Romano, P, Paolone, M. Iterative-interpolated dft for synchrophasor estimation: a single algorithm for p- and m-class compliant pmus. IEEE Trans Instrum Meas 2018;67:547–58.10.1109/TIM.2017.2779378Search in Google Scholar
24. Frigo, G, Dervikadi, A, Paolone, M. Reduced leakage synchrophasor estimation: Hilbert transform plus interpolated dft. IEEE Trans Instrum Meas 2019;68:3468–83. https://doi.org/10.1109/tim.2018.2879070.Search in Google Scholar
25. Jin, T, Zhang, W. A novel interpolated dft synchrophasor estimation algorithm with an optimized combined cosine self-convolution window. IEEE Trans Instrum Meas 2021;70:1–10. https://doi.org/10.1109/tim.2020.3033073.Search in Google Scholar
26. Standard. IEEE standard for synchrophasor measurements for power systems – amendment 1: modification of selected performance requirements. IEEE Std C37.118.1a-2014 (Amendment to IEEE Std C37.118.1-2011); 2014:1–25 pp.Search in Google Scholar
27. Standard. IEEE/IEC international standard-measuring relays and protection equipment-part 118-1: synchrophasor for power systems-measurements. IEC/IEEE 60255-118-1:2018; 2018:1–78 pp.Search in Google Scholar
28. Roscoe, AJ, Abdulhadi, IF, Burt, GM. P and m class phasor measurement unit algorithms using adaptive cascaded filters. IEEE Trans Power Deliv 2013;28:1447–59. https://doi.org/10.1109/tpwrd.2013.2238256.Search in Google Scholar
29. Gurusinghe, DR, Rajapakse, AD, Narendra, K. Testing and enhancement of the dynamic performance of a phasor measurement unit. IEEE Trans Power Deliv 2014;29:1551–60. https://doi.org/10.1109/tpwrd.2014.2322516.Search in Google Scholar
30. Thilakarathne, C, Meegahapola, L, Fernando, N. Improved synchrophasor models for power system dynamic stability evaluation based on ieee c37.118.1 reference architecture. IEEE Trans Instrum Meas 2017;66:2937–47. https://doi.org/10.1109/tim.2017.2714558.Search in Google Scholar
31. Premerlani, W, Kasztenny, B, Adamiak, M. Development and implementation of a synchrophasor estimator capable of measurements under dynamic conditions. IEEE Trans Power Deliv 2008;23:109–23. https://doi.org/10.1109/tpwrd.2007.910982.Search in Google Scholar
32. Serna, JA. Synchrophasor estimation using prony’s method. IEEE Trans Instrum Meas 2013;62:2119–28. https://doi.org/10.1109/tim.2013.2265436.Search in Google Scholar
33. Platas-Garza, MA, Serna, JA. Dynamic phasor and frequency estimates through maximally flat differentiators. IEEE Trans Instrum Meas 2010;59:1803–11. https://doi.org/10.1109/tim.2009.2030921.Search in Google Scholar
34. Zhan, L, Liu, Y. Improved wls-tf algorithm for dynamic synchronized angle and frequency estimation. In: 2014 IEEE PES general meeting — conference exposition. National Harbor, MD, USA: IEEE; 2014:1–5 pp.10.1109/PESGM.2014.6938906Search in Google Scholar
35. Vejdan, S, Sanaye-Pasand, M, Malik, OP. Accurate dynamic phasor estimation based on the signal model under off-nominal frequency and oscillations. IEEE Trans Smart Grid 2017;8:708–19.10.1109/TSG.2015.2503742Search in Google Scholar
36. Fu, L, Yu, L, Xiong, S, He, Z, Mai, R, Li, X. A dynamic synchrophasor estimation algorithm considering out-of-band interference. IEEE Trans Power Delivery 2021;36:1. https://doi.org/10.1109/tpwrd.2021.3079990.Search in Google Scholar
37. Castello, P, Liu, J, Muscas, C, Pegoraro, P, Ponci, F, Monti, A. A fast and accurate pmu algorithm for p+m class measurement of synchrophasor and frequency. IEEE Trans Instrumentation and Measurement 2014;63:2837–45. https://doi.org/10.1109/tim.2014.2323137.Search in Google Scholar
38. Bansal, Y, Sodhi, R. An adaptive iir notch filter based half-cycle p-class phasor measurement estimation scheme. In: 2019 8th international conference on power systems (ICPS). Jaipur, India: IEEE; 2019:1–6 pp.10.1109/ICPS48983.2019.9067648Search in Google Scholar
39. Kamwa, I, Samantaray, SR, Joos, G. Wide frequency range adaptive phasor and frequency pmu algorithms. IEEE Trans Smart Grid 2014;5:569–79. https://doi.org/10.1109/tsg.2013.2264536.Search in Google Scholar
40. Standard. IEEE standard for synchrophasors for power systems. IEEE Std 1344-1995(R2001); 1995.Search in Google Scholar
41. Martin, K, Benmouyal, G, Adamiak, M, Begovic, M, Burnett, R, Carr, K, et al.. IEEE standard for synchrophasors for power systems. IEEE Trans Power Delivery 1998;13:73–7. https://doi.org/10.1109/61.660853.Search in Google Scholar
42. Standard. IEEE standard for synchrophasors for power systems. IEEE Std C37.118-2005 (Revision of IEEE Std 1344-1995); 2006:1–65 pp.Search in Google Scholar
43. Meng, W, Wang, X, Wang, Z, Kamwa, I. Impact of causality on performance of phasor measurement unit algorithms. IEEE Trans Power Syst 2018;33:1555–65. https://doi.org/10.1109/tpwrs.2017.2734662.Search in Google Scholar
44. Musleh, A, Muyeen, S, Durra, A, Kamwa, I, Masoum, M, Islam, S. Time-delay analysis of wide-area voltage control considering smart grid contingences in a real-time environment. IEEE Transactions on Industrial Informatics 2018;14:1242–52. https://doi.org/10.1109/tii.2018.2799594.Search in Google Scholar
45. Sharma, C, Tyagi, B. Fuzzy type-2 controller design for small-signal stability considering time latencies and uncertainties in pmu measurements. IEEE Syst J 2017;11:1149–60. https://doi.org/10.1109/jsyst.2014.2336864.Search in Google Scholar
46. Chaudhuri, N, Chaudhuri, B, Ray, S, Majumder, R. Wide-area phasor power oscillation damping controller: a new approach to handling time-varying signal latency. IET Generation, Transmission & Distribution 2010;4:620–30. https://doi.org/10.1049/iet-gtd.2009.0624.Search in Google Scholar
47. Stahlhut, J, Browne, T, Heydt, G, Vittal, V. Latency viewed as a stochastic process and its impact on wide area power system control signals. IEEE Transactions on Power Systems 2008;23:84–91. https://doi.org/10.1109/tpwrs.2007.913210.Search in Google Scholar
48. Zhu, K, Song, J, Chenine, M, Nordström, L. Analysis of phasor data latency in wide area monitoring and control systems. In: 2010 IEEE international conference on communications workshops. Cape Town, South Africa: IEEE; 2010:1–5 pp.10.1109/ICCW.2010.5503915Search in Google Scholar
49. Wang, S, Meng, X, Chen, T. Wide-area control of power systems through delayed network communication. IEEE Trans Contr Syst Technol 2012;20:495–503. https://doi.org/10.1109/tcst.2011.2116022.Search in Google Scholar
50. Guardado, RA, Guardado, JL. A pmu model for wide-area protection in atp/emtp. IEEE Trans Power Deliv 2016;31:1953–60. https://doi.org/10.1109/tpwrd.2015.2494870.Search in Google Scholar
51. Dotta, D, Chow, J, Vanfretti, L, Almas, M, Agostini, M. A matlab-based pmu simulator. In: 2013 IEEE power energy society general meeting. Vancouver, BC, Canada: IEEE; 2013:1–5 pp.10.1109/PESMG.2013.6672629Search in Google Scholar
52. Roy, B, Sinha, A, Pradhan, A. Synchrophasor-assisted prediction of stability/instability of a power system. Int J Emerg Elec Power Syst 2013;14:1–8. https://doi.org/10.1515/ijeeps-2013-0028.Search in Google Scholar
53. Kulkarni, AR, Ballal, MS. Ultra mega power plant disturbance related oscillation detection in indian grid using pmu data. Int J Emerg Elec Power Syst 2020;21:20190178. https://doi.org/10.1515/ijeeps-2019-0178.Search in Google Scholar
54. Proakis, JG, Manolakis, DG. Digital signal processing. New Jersey, U.S.A: Pearson Prentice Hall; 2007.Search in Google Scholar
55. Serna, JA. Dynamic phasor estimates for power system oscillations. IEEE Trans Instrum Meas 2007;56:1648–57. https://doi.org/10.1109/tim.2007.904546.Search in Google Scholar
56. Petri, D, Fontanelli, D, Macii, D. A frequency-domain algorithm for dynamic synchrophasor and frequency estimation. IEEE Trans Instrum Meas 2014;63:2330–40. https://doi.org/10.1109/tim.2014.2308996.Search in Google Scholar
57. Radulovi, M, Zeevi, ., Krstaji, B. Dynamic phasor estimation by symmetric Taylor weighted least square filter. IEEE Trans Power Deliv 2020;35:828–36. https://doi.org/10.1109/tpwrd.2019.2929246.Search in Google Scholar
58. Shubhanga, K. Power system analysis: a dynamic perspective. India: Pearson Education; 2018.Search in Google Scholar
59. Rao, K, Shubhanga, K. Mape - an alternative fitness metric for prony analysis of power system signals. Int J Emerg Elec Power Syst 2018;19. https://doi.org/10.1515/ijeeps-2018-0091.Search in Google Scholar
60. Sarkar, N, Rao, K, Shubhanga, KN. A comparative study between prony and eigensystem realization algorithm for identification of electromechanical modes. In: 2018 20th national power systems conference (NPSC). Tiruchirappalli, India: IEEE; 2018:1–6 pp.10.1109/NPSC.2018.8771722Search in Google Scholar
61. Rubea, R, Ivankovi, I, Reki, M, Brnobi, D, Grudeni, V, Holjevac, N. On line electromechanical oscillations detection in transmission network with synchrophasor. In: 2020 3rd international colloquium on intelligent grid metrology (SMAGRIMET). Cavtat, Croatia: IEEE; 2020:114–20 pp.10.23919/SMAGRIMET48809.2020.9264024Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Theorems to explore the nature of cyber attacks on power system voltage stability
- An integrated PMU architecture for power system applications
- Commercial building load characteristics modeling considering equipment innate laws and various staff behaviors under demand response mechanism
- Design and performance improvements of solar based efficient hybrid electric vehicle
- A fault detection technique based on line parameters in ring-configured DC microgrid
- Integration of deterministic and game-based energy consumption scheduling for demand side management in isolated microgrids
- Optimal placement of wide area monitoring system components in active distribution networks
- Modeling, cost optimization and management of grid connected solar powered charging station for electric vehicle
- Improvements in deviation settlement mechanism of Indian electricity grid system through demand response management
- Design and implementation of an adaptive relay based on curve-fitting technique for micro-grid protection
- The comparison and analysis of Type 3 wind turbine models used for researching the stability of electric power systems
Articles in the same Issue
- Frontmatter
- Research Articles
- Theorems to explore the nature of cyber attacks on power system voltage stability
- An integrated PMU architecture for power system applications
- Commercial building load characteristics modeling considering equipment innate laws and various staff behaviors under demand response mechanism
- Design and performance improvements of solar based efficient hybrid electric vehicle
- A fault detection technique based on line parameters in ring-configured DC microgrid
- Integration of deterministic and game-based energy consumption scheduling for demand side management in isolated microgrids
- Optimal placement of wide area monitoring system components in active distribution networks
- Modeling, cost optimization and management of grid connected solar powered charging station for electric vehicle
- Improvements in deviation settlement mechanism of Indian electricity grid system through demand response management
- Design and implementation of an adaptive relay based on curve-fitting technique for micro-grid protection
- The comparison and analysis of Type 3 wind turbine models used for researching the stability of electric power systems