Optimised Placement of Micro-PMUs and voltage Estimation in Radial Distribution Networks Considering Zero Injection Constraints

Vamsidhar Reddy G; Palepu Suresh Babu; Venkata Ramana K; Sravan kumar T; Vinay kumar Reddy M

doi:10.5935/jetia.v12i59.3461

Autores

Vamsidhar Reddy G Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India. https://orcid.org/0009-0005-8078-8938 (não autenticado)
Palepu Suresh Babu Departmentof Electrical and Electronics Engineering, Annamacharya University, 516 126, Rajampet, India. https://orcid.org/0000-0001-5786-1395 (não autenticado)
Venkata Ramana K Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India. https://orcid.org/0009-0004-3966-8478 (não autenticado)
Sravan kumar T Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India. https://orcid.org/0009-0000-9836-6040 (não autenticado)
Vinay kumar Reddy M Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India. https://orcid.org/0009-0003-6893-4547 (não autenticado)

DOI:

https://doi.org/10.5935/jetia.v12i59.3461

Resumo

This project presents a robust and efficient methodology for the optimal placement of Micro-Phasor Measurement Units (µPMUs) in radial distribution networks, with particular attention to Zero Injection Bus (ZIB)constraints and high levels of Distributed Generation (DG).The proposed approach combines mathematical optimization and the Adaptive Mutated Practical Swarm Optimization (AMPSO) algorithm to perform accurate voltage estimation and enhance network observability. By strategically placing µPMUs, the method reduces the number of measurement units needed while still meeting full observability and system monitoring goals. The frame work is validated on the IEEE 33- bus test systems, showing improved performance, reduced computational complexity, and compatibility with renewable energy integration. Results highlight the project’s effectiveness in supporting smart grid development through cost-efficient, scalable, and practical solutions.