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<p>This paper introduces a novel optimization problem that explores the trade-offs between the strategic placement of phasor measurement units (PMUs) and state estimation accuracy (SEA) in electrical grid networks. State estimation in power networks has extensively employed PMU measurements and has demonstrated its efficacy in improving the accuracy of state estimation. The binary brown-bear optimization (BBOA) method is utilized to identify the strategic locations of PMUs. The proposed objective function framework includes strategic PMU locations, measurement redundancy (MR), and the normalized state estimation error. The Newton–Raphson (NR) method is used for load flow analysis, and the weighted least squares (WLS) method is employed for state estimation of power networks. The difference between the root mean square of the WLS state estimation without PMU data and with PMU data yields the state estimation error. The proposed framework ensures that improvements in MR and SEA are balanced against the objective of obtaining strategic placement of PMUs (SPP), thereby finding a feasible solution. The results are verified by testing the proposed frameworks on the IEEE 14-bus, 30-bus, and 57-bus systems. Furthermore, outcomes are contrasted with other approaches, revealing improved results.</p>

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An Optimal Framework for Trade-off between Strategic Placement of PMUs and Enhanced State Estimation Accuracy

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Anand K.,
  • Tapan Prakash,
  • Himadri Lala

摘要

This paper introduces a novel optimization problem that explores the trade-offs between the strategic placement of phasor measurement units (PMUs) and state estimation accuracy (SEA) in electrical grid networks. State estimation in power networks has extensively employed PMU measurements and has demonstrated its efficacy in improving the accuracy of state estimation. The binary brown-bear optimization (BBOA) method is utilized to identify the strategic locations of PMUs. The proposed objective function framework includes strategic PMU locations, measurement redundancy (MR), and the normalized state estimation error. The Newton–Raphson (NR) method is used for load flow analysis, and the weighted least squares (WLS) method is employed for state estimation of power networks. The difference between the root mean square of the WLS state estimation without PMU data and with PMU data yields the state estimation error. The proposed framework ensures that improvements in MR and SEA are balanced against the objective of obtaining strategic placement of PMUs (SPP), thereby finding a feasible solution. The results are verified by testing the proposed frameworks on the IEEE 14-bus, 30-bus, and 57-bus systems. Furthermore, outcomes are contrasted with other approaches, revealing improved results.