Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability

Phasor measurement units (PMUs) have gained significant interest in recent decades. These instruments are used to measure synchronized phasor data. PMUs are gradually but definitely taking over power grids because of the significant phasor information that they generate for both regular and irregula...

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Autores:
Babu, Rohit
Gupta, Vikash Kumar
Tipo de recurso:
Article of journal
Fecha de publicación:
2023
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
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oai:repositorio.utb.edu.co:20.500.12585/13515
Acceso en línea:
https://hdl.handle.net/20.500.12585/13515
https://doi.org/10.32397/tesea.vol4.n2.523
Palabra clave:
Binary integer linear programming
Maximum observability
Phasor measurement unit
State Estimation
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openAccess
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Rohit Babu, Vikash Kumar Gupta - 2023
id UTB2_8ac02752fbfa788334b9e37fc5226e2d
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/13515
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
dc.title.translated.spa.fl_str_mv Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
title Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
spellingShingle Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
Binary integer linear programming
Maximum observability
Phasor measurement unit
State Estimation
title_short Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
title_full Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
title_fullStr Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
title_full_unstemmed Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
title_sort Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability
dc.creator.fl_str_mv Babu, Rohit
Gupta, Vikash Kumar
dc.contributor.author.eng.fl_str_mv Babu, Rohit
Gupta, Vikash Kumar
dc.subject.eng.fl_str_mv Binary integer linear programming
Maximum observability
Phasor measurement unit
State Estimation
topic Binary integer linear programming
Maximum observability
Phasor measurement unit
State Estimation
description Phasor measurement units (PMUs) have gained significant interest in recent decades. These instruments are used to measure synchronized phasor data. PMUs are gradually but definitely taking over power grids because of the significant phasor information that they generate for both regular and irregular conditions for the purpose of maintaining safety and control. PMUs may be used for a variety of purposes, including state estimation, which is a common task. In order to make state estimation more reliable, a variety of approaches have been looked into, and one of them is the positioning of PMUs. This paper provides a plan for the implementation of the PMUs, taking into account the potential for failure and vulnerability posed by PMU-equipped buses. Two separate studies were carried out and evaluated with the goal of solving the optimum PMU placement problem (OPPP), which pertains to the grids. The findings of the first study show that the maximum bus observability may be accomplished with the fewest possible number of PMUs, even while taking into consideration the fact that there is a risk that one or more PMUs would malfunction. This investigation was carried out with common measures such as zero injection bus (ZIB) and branch flow measurements, both with and without them, in order to assess the outcomes. The second research focused on selecting the PMU-equipped bus’s vulnerability analysis as its primary area of investigation. All of the tests were completed by using binary integer linear programming. Specifically, the described method is meant to be used with an existing PMU framework and in the case that new locations for new PMUs are necessary to be furnished with existing PMUs. This results confirm that the recommended strategy can be implemented successfully on the IEEE benchmark test systems.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-12-29 13:09:03
2025-05-21T19:15:47Z
dc.date.available.none.fl_str_mv 2023-12-29 13:09:03
dc.date.issued.none.fl_str_mv 2023-12-29
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.local.eng.fl_str_mv Journal article
dc.type.content.eng.fl_str_mv Text
dc.type.version.eng.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.eng.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/13515
dc.identifier.url.none.fl_str_mv https://doi.org/10.32397/tesea.vol4.n2.523
dc.identifier.doi.none.fl_str_mv 10.32397/tesea.vol4.n2.523
dc.identifier.eissn.none.fl_str_mv 2745-0120
url https://hdl.handle.net/20.500.12585/13515
https://doi.org/10.32397/tesea.vol4.n2.523
identifier_str_mv 10.32397/tesea.vol4.n2.523
2745-0120
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.references.eng.fl_str_mv Saikat Chakrabarti, Elias Kyriakides, and Demetrios G. Eliades. Placement of synchronized measurements for power system observability. IEEE Transactions on Power Delivery, 24(1):12–19, 2009. [2] R.F. Nuqui and A.G. Phadke. Phasor measurement unit placement techniques for complete and incomplete observability. IEEE Transactions on Power Delivery, 20(4):2381–2388, 2005. [3] Arun G. Phadke and James S. Thorp. Synchronized phasor measurements and their applications. Springer, Blacksburg, Virginia, 2008. [4] Arun G. Phadke and James S. Thorp. Computer relaying for power systems. John Wiley & Sons Ltd, West Sussex, 2nd edition, 2012. [5] Jyoti Paudel. Phasor measurement unit deployment approach for maximum observability considering vulnerability analysis. PhD thesis, Clemson University, All Theses. 2275, 2015. [Available at https://tigerprints.clemson.edu/all_theses/2275/]. [6] Nabil H. Abbasy and Hanafy Mahmoud Ismail. A unified approach for the optimal pmu location for power system state estimation. IEEE Transactions on Power Systems, 24(2):806–813, 2009. [7] Edmund O. Schweitzer and David E. Whitehead. Real-world synchrophasor solutions. In 2009 62nd Annual Conference for Protective Relay Engineers, pages 536–547, 2009. [8] B. Xu and A. Abur. Observability analysis and measurement placement for systems with pmus. In IEEE PES Power Systems Conference and Exposition, 2004., pages 943–946 vol.2, 2004. [9] Bei Xu and Ali Abur. Optimal placement of phasor measurement units for state estimation. PSERC publication, 2005. [10] Rohit Babu and Biplab Bhattacharyya. Optimal placement of pmu for complete observability of the interconnected power network considering zero-injection bus: A numerical approach. International Journal of Applied Power Engineering, 9(2):135–146, 2020. [11] Bei Gou. Generalized integer linear programming formulation for optimal pmu placement. IEEE Transactions on Power Systems, 23(3):1099–1104, 2008. [12] Bei Gou. Optimal placement of pmus by integer linear programming. IEEE Transactions on Power Systems, 23(3):1525–1526, 2008. [13] Anamitra Pal, Gerardo A. Sanchez-Ayala, James S. Thorp, and Virgilio A. Centeno. A community-based partitioning approach for phasor measurement unit placement in large systems. Electric Power Components and Systems, 44(12):1317–1329, 2016. [14] T.L. Baldwin, L. Mili, M.B. Boisen, and R. Adapa. Power system observability with minimal phasor measurement placement. IEEE Transactions on Power Systems, 8(2):707–715, 1993. [15] P. Xu and B. F. Wollenberg. Power system observability and optimal phasor measurement unit placement. University of Minnesota, Twin Cities, 2015. [16] Rohit Babu and Biplab Bhattacharyya. Phasor measurement unit allocation with different soft computing technique inconnected power network. In Michael Faraday IET International Summit 2015, pages 631–637, Kolkata, India, 2015. IET. [17] Rohit Babu and Biplab Bhattacharyya. Strategic placements of pmus for power network observability considering redundancy measurement. Measurement, 134:606–623, 2019. [18] Masoud Esmaili. Inclusive multi-objective pmu placement in power systems considering conventional measurements and contingencies. International Transactions on Electrical Energy Systems, 26(3):609–626, 2016. [19] Mohammad Ghamsari-Yazdel and Masoud Esmaili. Reliability-based probabilistic optimal joint placement of pmus and flow measurements. International Journal of Electrical Power & Energy Systems, 78:857–863, 2016. [20] Jamshid Aghaei, Amir Baharvandi, Mohammad-Amin Akbari, Kashem M. Muttaqi, Mohammad-Reza Asban, and Alireza Heidari. Multi-objective phasor measurement unit placement in electric power networks: Integer linear programming formulation. Electric Power Components and Systems, 43(17):1902–1911, 2015. [21] Arash Mahari and Heresh Seyedi. Optimal pmu placement for power system observability using bica, considering measurement redundancy. Electric Power Systems Research, 103:78–85, 2013. [22] Soumesh Chatterjee, Pronob K. Ghosh, and Biman K. Saha Roy. Pmu-based power system component monitoring scheme satisfying complete observability with multicriteria decision support. International Transactions on Electrical Energy Systems, 30(2):e12223, 2020. [23] Chawasak Rakpenthai, Suttichai Premrudeepreechacharn, Sermsak Uatrongjit, and Neville R. Watson. An optimal pmu placement method against measurement loss and branch outage. IEEE Transactions on Power Delivery, 22(1):101–107, 2007. [24] Rohit Babu, Saurav Raj, Joddumahanthi Vijaychandra, and Bugatha Ram Vara Prasad. Allocation of phasor measurement unit using an admissible searching-based algorithm a-star and binary search tree for full interconnected power network observability. Optimal Control Applications and Methods, 43(3):687–710, 2021. [25] Rohit Babu and Biplab Bhattacharyya. Allocation of phasor measurement unit using a-star method in connected power network. In 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), pages 1–6, Kanpur, India, 2015. [26] Nikolaos P. Theodorakatos, Miltiadis Lytras, and Rohit Babu. Towards smart energy grids: A box-constrained nonlinear underdetermined model for power system observability using recursive quadratic programming. Energies, 13(7), 2020. [27] Rohit Babu, Saurav Raj, Bishwajit Dey, and Biplab Bhattacharyya. Modified branch-and-bound algorithm for unravelling optimal pmu placement problem for power grid observability: A comparative analysis. CAAI Transactions on Intelligence Technology, 6(4):450–470, 2021. [28] Joel E. Anderson and Aranya Chakrabortty. Pmu placement for dynamic equivalencing of power systems under flow observability constraints. Electric Power Systems Research, 106:51–61, 2014. [29] Yoshiaki Matsukawa, Masayuki Watanabe, Yasunori Mitani, and Mohammad Lutfi Othman. Multi-objective pmu placement optimization considering the placement cost including the current channel allocation and state estimation accuracy. Electrical Engineering in Japan, 207(2):20–27, 2019. [30] Ranjana Sodhi, SC Srivastava, and SN Singh. Multi-criteria decision-making approach for multi-stage optimal placement of phasor measurement units. IET Generation, Transmission & Distribution, 5(2):181–190, 2011. [31] Nikolaos P. Theodorakatos. Optimal phasor measurement unit placement for numerical observability using a two-phase branch-and-bound algorithm. International Journal of Emerging Electric Power Systems, 19(3):20170231, 2018. [32] Heloisa H. Müller and Carlos A. Castro. Genetic algorithm-based phasor measurement unit placement method considering observability and security criteria. IET Generation, Transmission & Distribution, 10(1):270–280, 2016. [33] Xin Zhou, Haishun Sun, Cong Zhang, and Qiangsheng Dai. Optimal placement of pmus using adaptive genetic algorithm considering measurement redundancy. International Journal of Reliability, Quality and Safety Engineering, 23(03):1640001, 2016. [34] Z. Miljani´c, I. Djurovi´c, and I. Vujoševi´c. Optimal placement of pmus with limited number of channels. Electric Power Systems Research, 90:93–98, 2012. [35] Zhong-Jie Wang, Shu-Ying Yuan, Xuan Zhao, and Cheng-Chao Lu. Differential evolution-based optimal placement of phase measurement unit considering measurement redundancy. International Journal of Modeling, Simulation, and Scientific Computing, 06(01):1550016, 2015. [36] Chunhua Peng, Huijuan Sun, and Jianfeng Guo. Multi-objective optimal pmu placement using a non-dominated sorting differential evolution algorithm. International Journal of Electrical Power & Energy Systems, 32(8):886–892, 2010. [37] Rohit Babu and Biplab Bhattacharyya. Optimal allocation of phasor measurement unit for full observability of the connected power network. International Journal of Electrical Power & Energy Systems, 79:89–97, 2016. [38] Rohit Babu and Biplab Bhattacharyya. Optimal placement of phasor measurement unit using binary particle swarm optimization in connected power network. In 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON), pages 1–5, Allahabad, India, 2015. [39] Nadia Hanis Abd Rahman and Ahmed Faheem Zobaa. Integrated mutation strategy with modified binary pso algorithm for optimal pmus placement. IEEE Transactions on Industrial Informatics, 13(6):3124–3133, 2017. [40] Nadia Hanis Abd Rahman. Optimal allocation of phasor measurement units using practical constraints in power systems. PhD thesis, Brunel University London, 2017. [41] Tapas Kumar Maji and P. Acharjee. Multiple solutions of optimal pmu placement using exponential binary pso algorithm. In 2015 Annual IEEE India Conference (INDICON), pages 1–6, New Delhi, India, 2015. [42] Rohit Babu and Biplab Bhattacharyya. Weak bus-oriented installation of phasor measurement unit for power network observability. International Journal of Emerging Electric Power Systems, 18(5):20170073, 2017. [43] Rohit Babu, Saurav Raj, and Biplab Bhattacharyya. Weak bus-constrained pmu placement for complete observability of a connected power network considering voltage stability indices. Protection and Control of Modern Power Systems, 5(1):28, 2020. [44] John K. Karlof. Integer programming: theory and practice. CRC Press, Boca Raton, 1st edition, 2005. [45] Rohit Babu and Biplab Bhattacharyya. An approach for optimal placement of phasor measurement unit for power network observability considering various contingencies. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 42:161–183, 2018. [46] Farrokh Aminifar, Mahmud Fotuhi-Firuzabad, Amir Safdarian, and Mohammad Shahidehpour. Observability of hybrid ac/dc power systems with variable-cost pmus. IEEE Transactions on Power Delivery, 29(1):345–352, 2014. [47] Bhushan Madan Nikumbh. Optimal placement of pmus considering logical topology of communication medium power system observability. Master’s thesis, UiT Norges arktiske universitet, 2016. [48] Christie R. D. Power Systems Test Case Archive. Department of Electrical Engineering, University of Washington, 1999. https://www2.ee.washington.edu/research/pstca/ [Accessed: March 3, 2017]. [49] Ray Daniel Zimmerman, Carlos Edmundo Murillo-Sánchez, and Robert John Thomas. Matpower: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on Power Systems, 26(1):12–19, 2011. [50] Optimization toolbox™ user’s guide r2011b, 2011. MathWorks. http://cda.psych.uiuc.edu/matlab_programming_class_ 2012/optim_tb.pdf.
dc.relation.ispartofjournal.eng.fl_str_mv Transactions on Energy Systems and Engineering Applications
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dc.relation.citationstartpage.none.fl_str_mv 1
dc.relation.citationendpage.none.fl_str_mv 23
dc.relation.bitstream.none.fl_str_mv https://revistas.utb.edu.co/tesea/article/download/523/384
dc.relation.citationedition.eng.fl_str_mv Núm. 2 , Año 2023 : Transactions on Energy Systems and Engineering Applications
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dc.rights.eng.fl_str_mv Rohit Babu, Vikash Kumar Gupta - 2023
dc.rights.uri.eng.fl_str_mv https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.eng.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.eng.fl_str_mv This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.eng.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Rohit Babu, Vikash Kumar Gupta - 2023
https://creativecommons.org/licenses/by/4.0
This work is licensed under a Creative Commons Attribution 4.0 International License.
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.eng.fl_str_mv application/pdf
dc.publisher.eng.fl_str_mv Universidad Tecnológica de Bolívar
dc.source.eng.fl_str_mv https://revistas.utb.edu.co/tesea/article/view/523
institution Universidad Tecnológica de Bolívar
repository.name.fl_str_mv Repositorio Digital Universidad Tecnológica de Bolívar
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spelling Babu, RohitGupta, Vikash Kumar2023-12-29 13:09:032025-05-21T19:15:47Z2023-12-29 13:09:032023-12-29https://hdl.handle.net/20.500.12585/13515https://doi.org/10.32397/tesea.vol4.n2.52310.32397/tesea.vol4.n2.5232745-0120Phasor measurement units (PMUs) have gained significant interest in recent decades. These instruments are used to measure synchronized phasor data. PMUs are gradually but definitely taking over power grids because of the significant phasor information that they generate for both regular and irregular conditions for the purpose of maintaining safety and control. PMUs may be used for a variety of purposes, including state estimation, which is a common task. In order to make state estimation more reliable, a variety of approaches have been looked into, and one of them is the positioning of PMUs. This paper provides a plan for the implementation of the PMUs, taking into account the potential for failure and vulnerability posed by PMU-equipped buses. Two separate studies were carried out and evaluated with the goal of solving the optimum PMU placement problem (OPPP), which pertains to the grids. The findings of the first study show that the maximum bus observability may be accomplished with the fewest possible number of PMUs, even while taking into consideration the fact that there is a risk that one or more PMUs would malfunction. This investigation was carried out with common measures such as zero injection bus (ZIB) and branch flow measurements, both with and without them, in order to assess the outcomes. The second research focused on selecting the PMU-equipped bus’s vulnerability analysis as its primary area of investigation. All of the tests were completed by using binary integer linear programming. Specifically, the described method is meant to be used with an existing PMU framework and in the case that new locations for new PMUs are necessary to be furnished with existing PMUs. This results confirm that the recommended strategy can be implemented successfully on the IEEE benchmark test systems.application/pdfengUniversidad Tecnológica de BolívarRohit Babu, Vikash Kumar Gupta - 2023https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/523Binary integer linear programmingMaximum observabilityPhasor measurement unitState EstimationAnalysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum ObservabilityAnalysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum ObservabilityArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Saikat Chakrabarti, Elias Kyriakides, and Demetrios G. Eliades. Placement of synchronized measurements for power system observability. IEEE Transactions on Power Delivery, 24(1):12–19, 2009. [2] R.F. Nuqui and A.G. Phadke. Phasor measurement unit placement techniques for complete and incomplete observability. IEEE Transactions on Power Delivery, 20(4):2381–2388, 2005. [3] Arun G. Phadke and James S. Thorp. Synchronized phasor measurements and their applications. Springer, Blacksburg, Virginia, 2008. [4] Arun G. Phadke and James S. Thorp. Computer relaying for power systems. John Wiley & Sons Ltd, West Sussex, 2nd edition, 2012. [5] Jyoti Paudel. Phasor measurement unit deployment approach for maximum observability considering vulnerability analysis. PhD thesis, Clemson University, All Theses. 2275, 2015. [Available at https://tigerprints.clemson.edu/all_theses/2275/]. [6] Nabil H. Abbasy and Hanafy Mahmoud Ismail. A unified approach for the optimal pmu location for power system state estimation. IEEE Transactions on Power Systems, 24(2):806–813, 2009. [7] Edmund O. Schweitzer and David E. Whitehead. Real-world synchrophasor solutions. In 2009 62nd Annual Conference for Protective Relay Engineers, pages 536–547, 2009. [8] B. Xu and A. Abur. Observability analysis and measurement placement for systems with pmus. In IEEE PES Power Systems Conference and Exposition, 2004., pages 943–946 vol.2, 2004. [9] Bei Xu and Ali Abur. Optimal placement of phasor measurement units for state estimation. PSERC publication, 2005. [10] Rohit Babu and Biplab Bhattacharyya. Optimal placement of pmu for complete observability of the interconnected power network considering zero-injection bus: A numerical approach. International Journal of Applied Power Engineering, 9(2):135–146, 2020. [11] Bei Gou. Generalized integer linear programming formulation for optimal pmu placement. IEEE Transactions on Power Systems, 23(3):1099–1104, 2008. [12] Bei Gou. Optimal placement of pmus by integer linear programming. IEEE Transactions on Power Systems, 23(3):1525–1526, 2008. [13] Anamitra Pal, Gerardo A. Sanchez-Ayala, James S. Thorp, and Virgilio A. Centeno. A community-based partitioning approach for phasor measurement unit placement in large systems. Electric Power Components and Systems, 44(12):1317–1329, 2016. [14] T.L. Baldwin, L. Mili, M.B. Boisen, and R. Adapa. Power system observability with minimal phasor measurement placement. IEEE Transactions on Power Systems, 8(2):707–715, 1993. [15] P. Xu and B. F. Wollenberg. Power system observability and optimal phasor measurement unit placement. University of Minnesota, Twin Cities, 2015. [16] Rohit Babu and Biplab Bhattacharyya. Phasor measurement unit allocation with different soft computing technique inconnected power network. In Michael Faraday IET International Summit 2015, pages 631–637, Kolkata, India, 2015. IET. [17] Rohit Babu and Biplab Bhattacharyya. Strategic placements of pmus for power network observability considering redundancy measurement. Measurement, 134:606–623, 2019. [18] Masoud Esmaili. Inclusive multi-objective pmu placement in power systems considering conventional measurements and contingencies. International Transactions on Electrical Energy Systems, 26(3):609–626, 2016. [19] Mohammad Ghamsari-Yazdel and Masoud Esmaili. Reliability-based probabilistic optimal joint placement of pmus and flow measurements. International Journal of Electrical Power & Energy Systems, 78:857–863, 2016. [20] Jamshid Aghaei, Amir Baharvandi, Mohammad-Amin Akbari, Kashem M. Muttaqi, Mohammad-Reza Asban, and Alireza Heidari. Multi-objective phasor measurement unit placement in electric power networks: Integer linear programming formulation. Electric Power Components and Systems, 43(17):1902–1911, 2015. [21] Arash Mahari and Heresh Seyedi. Optimal pmu placement for power system observability using bica, considering measurement redundancy. Electric Power Systems Research, 103:78–85, 2013. [22] Soumesh Chatterjee, Pronob K. Ghosh, and Biman K. Saha Roy. Pmu-based power system component monitoring scheme satisfying complete observability with multicriteria decision support. International Transactions on Electrical Energy Systems, 30(2):e12223, 2020. [23] Chawasak Rakpenthai, Suttichai Premrudeepreechacharn, Sermsak Uatrongjit, and Neville R. Watson. An optimal pmu placement method against measurement loss and branch outage. IEEE Transactions on Power Delivery, 22(1):101–107, 2007. [24] Rohit Babu, Saurav Raj, Joddumahanthi Vijaychandra, and Bugatha Ram Vara Prasad. Allocation of phasor measurement unit using an admissible searching-based algorithm a-star and binary search tree for full interconnected power network observability. Optimal Control Applications and Methods, 43(3):687–710, 2021. [25] Rohit Babu and Biplab Bhattacharyya. Allocation of phasor measurement unit using a-star method in connected power network. In 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), pages 1–6, Kanpur, India, 2015. [26] Nikolaos P. Theodorakatos, Miltiadis Lytras, and Rohit Babu. Towards smart energy grids: A box-constrained nonlinear underdetermined model for power system observability using recursive quadratic programming. Energies, 13(7), 2020. [27] Rohit Babu, Saurav Raj, Bishwajit Dey, and Biplab Bhattacharyya. Modified branch-and-bound algorithm for unravelling optimal pmu placement problem for power grid observability: A comparative analysis. CAAI Transactions on Intelligence Technology, 6(4):450–470, 2021. [28] Joel E. Anderson and Aranya Chakrabortty. Pmu placement for dynamic equivalencing of power systems under flow observability constraints. Electric Power Systems Research, 106:51–61, 2014. [29] Yoshiaki Matsukawa, Masayuki Watanabe, Yasunori Mitani, and Mohammad Lutfi Othman. Multi-objective pmu placement optimization considering the placement cost including the current channel allocation and state estimation accuracy. Electrical Engineering in Japan, 207(2):20–27, 2019. [30] Ranjana Sodhi, SC Srivastava, and SN Singh. Multi-criteria decision-making approach for multi-stage optimal placement of phasor measurement units. IET Generation, Transmission & Distribution, 5(2):181–190, 2011. [31] Nikolaos P. Theodorakatos. Optimal phasor measurement unit placement for numerical observability using a two-phase branch-and-bound algorithm. International Journal of Emerging Electric Power Systems, 19(3):20170231, 2018. [32] Heloisa H. Müller and Carlos A. Castro. Genetic algorithm-based phasor measurement unit placement method considering observability and security criteria. IET Generation, Transmission & Distribution, 10(1):270–280, 2016. [33] Xin Zhou, Haishun Sun, Cong Zhang, and Qiangsheng Dai. Optimal placement of pmus using adaptive genetic algorithm considering measurement redundancy. International Journal of Reliability, Quality and Safety Engineering, 23(03):1640001, 2016. [34] Z. Miljani´c, I. Djurovi´c, and I. Vujoševi´c. Optimal placement of pmus with limited number of channels. Electric Power Systems Research, 90:93–98, 2012. [35] Zhong-Jie Wang, Shu-Ying Yuan, Xuan Zhao, and Cheng-Chao Lu. Differential evolution-based optimal placement of phase measurement unit considering measurement redundancy. International Journal of Modeling, Simulation, and Scientific Computing, 06(01):1550016, 2015. [36] Chunhua Peng, Huijuan Sun, and Jianfeng Guo. Multi-objective optimal pmu placement using a non-dominated sorting differential evolution algorithm. International Journal of Electrical Power & Energy Systems, 32(8):886–892, 2010. [37] Rohit Babu and Biplab Bhattacharyya. Optimal allocation of phasor measurement unit for full observability of the connected power network. International Journal of Electrical Power & Energy Systems, 79:89–97, 2016. [38] Rohit Babu and Biplab Bhattacharyya. Optimal placement of phasor measurement unit using binary particle swarm optimization in connected power network. In 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON), pages 1–5, Allahabad, India, 2015. [39] Nadia Hanis Abd Rahman and Ahmed Faheem Zobaa. Integrated mutation strategy with modified binary pso algorithm for optimal pmus placement. IEEE Transactions on Industrial Informatics, 13(6):3124–3133, 2017. [40] Nadia Hanis Abd Rahman. Optimal allocation of phasor measurement units using practical constraints in power systems. PhD thesis, Brunel University London, 2017. [41] Tapas Kumar Maji and P. Acharjee. Multiple solutions of optimal pmu placement using exponential binary pso algorithm. In 2015 Annual IEEE India Conference (INDICON), pages 1–6, New Delhi, India, 2015. [42] Rohit Babu and Biplab Bhattacharyya. 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MathWorks. http://cda.psych.uiuc.edu/matlab_programming_class_ 2012/optim_tb.pdf.Transactions on Energy Systems and Engineering Applications4123https://revistas.utb.edu.co/tesea/article/download/523/384Núm. 2 , Año 2023 : Transactions on Energy Systems and Engineering Applications220.500.12585/13515oai:repositorio.utb.edu.co:20.500.12585/135152025-05-21 14:15:47.245https://creativecommons.org/licenses/by/4.0Rohit Babu, Vikash Kumar Gupta - 2023metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com

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