High-performance computing for static security assessment of large power systems

References

• Ahmadi, A., Smith, M. C., Collins, E. R., Dargahi, V., & Jin, S. (2021). Fast Newton-Raphson power flow analysis based on sparse techniques and parallel processing. IEEE Transactions on Power Systems, 37(3), 1695–1705. https://doi.org/10.1109/TPWRS.2021.3116182
   Web of Science ®Google Scholar
• Balduino, L., & Alves, A. (2004). Parallel processing in a cluster of microcomputers with application in contingency analysis. In IEEE/PES transmission and distribution conference and exposition: Latin America (IEEE Cat. No. 04EX956) (pp. 285–290). IEEE.
   Google Scholar
• Brandwajn, V., & Lauby, M. (1989). Complete bounding method for ac contingency screening. IEEE Transactions on Power Systems, 4(2), 724–729. https://doi.org/10.1109/59.193806
   Web of Science ®Google Scholar
• Burada, S., Joshi, D., & Mistry, K. D. (2016). Contingency analysis of power system by using voltage and active power performance index. In IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES) (pp. 1–5). IEEE.
   Google Scholar
• Chen, Q., & McCalley, J. D. (2005). Identifying high risk nk contingencies for online security assessment. IEEE Transactions on Power Systems, 20(2), 823–834. https://doi.org/10.1109/TPWRS.2005.846065
   Web of Science ®Google Scholar
• Davis, C. M., & Overbye, T. J. (2010). Multiple element contingency screening. IEEE Transactions on Power Systems, 26(3), 1294–1301. https://doi.org/10.1109/TPWRS.2010.2087366
   Web of Science ®Google Scholar
• Ejebe, G., & Wollenberg, B. (1979). Automatic contingency selection. IEEE Transactions on Power Apparatus and Systems, 1, 97–109.
   Google Scholar
• Ezhilarasi, G. A., & Swarup, K. (2009). Parallel contingency analysis in a high performance computing environment. In International conference on power systems (pp. 1–6). IEEE.
   Google Scholar
• Gholami, M., Sanjari, M. J., Safari, M., Akbari, M., & Kamali, M. R. (2020). Static security assessment of power systems: A review. International Transactions on Electrical Energy Systems, 30(9), e12432. https://doi.org/10.1002/etep.v30.9
   Web of Science ®Google Scholar
• Gopal, A., Niebur, D., & Venkatasubramanian, S. (2007). Dc power flow based contingency analysis using graphics processing units. In IEEE Lausanne Power Tech (pp. 731–736). IEEE.
   Google Scholar
• Grainger, J., Stevenson, W., & Stevenson, W. (2015). Power systems analysis, 2nd education.
   Google Scholar
• Green, R. C., Wang, L., & Alam, M. (2011). High performance computing for electric power systems: Applications and trends. In IEEE power and energy society general meeting (pp. 1–8). IEEE.
   Google Scholar
• Hailu, E. A., Nyakoe, G. N., & Muriithi, C. M. (2023). Techniques of power system static security assessment and improvement: A literature survey. Heliyon, 9(3), 1–18.
   Web of Science ®Google Scholar
• Hassan, R., Sun, R., & Liu, Y. (2022). Online static security assessment for cascading failure using stacked de-noising auto-encoder. International Journal of Electrical Power & Energy Systems, 137, 107852. https://doi.org/10.1016/j.ijepes.2021.107852
   Web of Science ®Google Scholar
• Huang, S., & Dinavahi, V. (2018). Real-time contingency analysis on massively parallel architectures with compensation method. IEEE Access, 6, 44519–44530. https://doi.org/10.1109/Access.6287639
   Web of Science ®Google Scholar
• Huang, Z., Chen, Y., & Nieplocha, J. (2009). Massive contingency analysis with high performance computing. In IEEE power & energy society general meeting (pp. 1–8). IEEE.
   Google Scholar
• Irisarri, G., & Sasson, A. (1981). An automatic contingency selection method for on-line security analysis. IEEE Transactions on Power Apparatus and Systems, PAS-100(4), 1838–1844. https://doi.org/10.1109/TPAS.1981.316524
   Web of Science ®Google Scholar
• Kanpur, I. (2023). Northern regional power grid (NRPG) data. https://www.iitk.ac.in/eeold/facilities/Research_labs/Power_System/NRPG-DATA.pdf. Online; accessed 28 February 2023.
   Google Scholar
• Kothari, D. P., & Nagrath, I. (2003). Modern power system analysis. Tata McGraw-Hill Publishing Company.
   Google Scholar
• Kumar, U., & Reddy, H. (2015). Contingency ranking in modern power system by exact and precise method. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 3(5), 229–237.
   Google Scholar
• Li, K.-C., Hsu, C.-H., Wen, C.-H., Wang, H.-H., & Yang, C.-T. (2009). A dynamic and scalable performance monitoring toolkit for cluster and grid environments. International Journal of High Performance Computing and Networking, 6(2), 91–99. https://doi.org/10.1504/IJHPCN.2009.027459
   Google Scholar
• Li, K.-C., & Weng, T.-H. (2009). Performance-based parallel application toolkit for high-performance clusters. The Journal of Supercomputing, 48, 43–65. https://doi.org/10.1007/s11227-008-0204-2
   Web of Science ®Google Scholar
• Mikolinnas, T., & Wollenberg, B. (1981). An advanced contingency selection algorithm. IEEE Transactions on Power Apparatus and Systems, PAS-100(2), 608–617. https://doi.org/10.1109/TPAS.1981.316917
   Web of Science ®Google Scholar
• Mishra, V. J., & Khardenvis, M. D. (2012). Contingency analysis of power system. In IEEE Students' conference on electrical, electronics and computer science (pp. 1–4). IEEE.
   Google Scholar
• Mitra, P., Vittal, V., Keel, B., & Mistry, J. (2016). A systematic approach to n-1-1 analysis for power system security assessment. IEEE Power and Energy Technology Systems Journal, 3(2), 71–80.
   Google Scholar
• Morante, Q., Ranaldo, N., Vaccaro, A., & Zimeo, E. (2006). Pervasive grid for large-scale power systems contingency analysis. IEEE Transactions on Industrial Informatics, 2(3), 165–175. https://doi.org/10.1109/TII.2006.877266
   Web of Science ®Google Scholar
• Nayak, S. K., Panda, S. K., & Das, S. (2022a). Constrained-based power management algorithm for green cloud computing. International Journal of Computational Science and Engineering, 25(6), 657–667. https://doi.org/10.1504/IJCSE.2022.127187
   Web of Science ®Google Scholar
• Nayak, S. K., Panda, S. K., & Das, S. (2022b). Unconstrained power management algorithm for green cloud computing. In Advances in distributed computing and machine learning: Proceedings of ICADCML (pp. 3–14). Springer.
   Google Scholar
• Nayak, S. K., Panda, S. K., Das, S., & Pande, S. K. (2021). A renewable energy-based task consolidation algorithm for cloud computing. In Control applications in modern power system: Select proceedings of EPREC (pp. 453–463). Springer.
   Google Scholar
• Nayak, S. K., Panda, S. K., Das, S., & Pande, S. K. (2022c). A multi-objective renewable energy-based algorithm for geographically distributed datacentres. International Journal of Embedded Systems, 15(2), 119–131. https://doi.org/10.1504/IJES.2022.123304
   Web of Science ®Google Scholar
• Patra, K. K. (2015). Contingency analysis in power system using load flow solution. International Journal of Computer Applications, 975(2), 8887.
   Google Scholar
• Pattery, J., & Hassainar, S. (2013). High performance computing for contingency analysis of power systems. International Journal of Engineering Research and Technology, 2(9), 1993–1998.
   Google Scholar
• Prabhakar, P. S. V., Krishan, R., & Pullaguram, D. R. (2022). Static security assessment of large power systems under n-1-1 contingency. In 22nd National power systems conference (NPSC) (pp. 35–40).
   Google Scholar
• Qian, T., Shi, F., Wang, K., Yang, S., Geng, J., Li, Y., & Wu, Q. (2022). N-1 static security assessment method for power grids with high penetration rate of renewable energy generation. Electric Power Systems Research, 211, 108200. https://doi.org/10.1016/j.epsr.2022.108200
   Web of Science ®Google Scholar
• Ramesh, V. (1996). On distributed computing for on-line power system applications. International Journal of Electrical Power & Energy Systems, 18(8), 527–533. https://doi.org/10.1016/0142-0615(96)00016-6
   Web of Science ®Google Scholar
• Roberge, V., Tarbouchi, M., & Okou, F. (2017). Parallel power flow on graphics processing units for concurrent evaluation of many networks. IEEE Transactions on Smart Grid, 8(4), 1639–1648. https://doi.org/10.1109/TSG.2015.2496298
   Web of Science ®Google Scholar
• Salem, F. K. A., Jaber, M., Abdallah, C., Mehio, O., & Najem, S. (2019). A distributed spatiotemporal contingency analysis for the lebanese power grid. IEEE Transactions on Computational Social Systems, 6(1), 162–175. https://doi.org/10.1109/TCSS.2018.2888689
   Web of Science ®Google Scholar
• Yang, C.-T., Shih, P.-C., & Li, K.-C. (2005). A high-performance computational resource broker for grid computing environments. In 19th International conference on advanced information networking and applications (AINA'05) Volume 1 (AINA papers) (Vol. 2, pp. 333–336). IEEE.
   Google Scholar
• Zaborszky, J., Whang, K.-W., & Prasad, K. (1980). Fast contingency evaluation using concentric relaxation. IEEE Transactions on Power Apparatus and Systems, PAS-99(1), 28–36. https://doi.org/10.1109/TPAS.1980.319605
   Web of Science ®Google Scholar
• Zhang, Y., Guo, Q., Zhou, Y., & Sun, H. (2021). Online frequency security assessment based on analytical model considering limiting modules. CSEE Journal of Power and Energy Systems, 8(5), 1363–1372.
   Web of Science ®Google Scholar
• Zhou, G., Zhang, X., Lang, Y., Bo, R., Jia, Y., Lin, J., & Feng, Y. (2016). A novel GPU-accelerated strategy for contingency screening of static security analysis. International Journal of Electrical Power & Energy Systems, 83, 33–39. https://doi.org/10.1016/j.ijepes.2016.03.048
   Web of Science ®Google Scholar
• Zhou, R., Ai, Z., Yang, J., Chen, Y., Li, J., Zhou, Q., & Li, K.-C. (2013). A hypervisor for MIPS-based architecture processors-a case study in Loongson processors. In IEEE 10th International conference on high performance computing and communications & 2013 IEEE international conference on embedded and ubiquitous computing (pp. 865–872). IEEE.
   Google Scholar
• Zou, Q., Luo, F., & Zhang, T. (2022). An incidence matrix based analytical method of n-1 contingency parallel analysis of main transformers in distribution networks. CSEE Journal of Power and Energy Systems.
   Google Scholar