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A Subspace-Guided Constrained Optimization Framework for M-Class Synchrophasor Estimation Under Nonstationary Conditions.

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Title: A Subspace-Guided Constrained Optimization Framework for M-Class Synchrophasor Estimation Under Nonstationary Conditions.
Authors: Altintasi, Cagri1,2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2537. 25p.
Subject Terms: *Constrained optimization, *Optimization algorithms, *Transient analysis, *Electric power system management, *Electric power systems, *Monte Carlo method, *Phasor measurement
Abstract: In recent years, the integration of renewable energy sources and the widespread use of nonlinear loads have increased dynamic uncertainties in modern power systems, making real-time and synchronized monitoring essential. Accurate M-class synchrophasor estimation under these nonstationary and spectrally uncertain conditions remains a challenging problem due to dynamic variations, harmonics/interharmonics, out-of-band interference, and measurement noise. This study proposes a suitably constrained optimization-based framework for M-class synchrophasor estimation, in which a hybrid structure integrating an ESPRIT-based subspace method with the Adaptive Fitness Distance Balance Artificial Rabbit Optimization (ES-AFDB-ARO) algorithm is employed. In this framework, the optimization stage is guided by spectral information obtained via the subspace stage to narrow the search space and improve convergence stability. Performance is evaluated under IEEE C37.118 steady-state and dynamic conditions via Monte Carlo simulations, showing that total vector error, frequency error, and rate-of-change-of-frequency error values remain within standard limits. Comparative analyses at 60 dB and 40 dB SNR demonstrate that the ES-AFDB-ARO method exhibits improved and more stable performance than the widely used interpolated discrete Fourier transform, Taylor weighted least squares and Taylor–Kalman filter methods. The results show that the proposed framework offers a reliable solution for synchrophasor estimation under dynamic operating conditions. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194587925
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A Subspace-Guided Constrained Optimization Framework for M-Class Synchrophasor Estimation Under Nonstationary Conditions.
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  Data: In recent years, the integration of renewable energy sources and the widespread use of nonlinear loads have increased dynamic uncertainties in modern power systems, making real-time and synchronized monitoring essential. Accurate M-class synchrophasor estimation under these nonstationary and spectrally uncertain conditions remains a challenging problem due to dynamic variations, harmonics/interharmonics, out-of-band interference, and measurement noise. This study proposes a suitably constrained optimization-based framework for M-class synchrophasor estimation, in which a hybrid structure integrating an ESPRIT-based subspace method with the Adaptive Fitness Distance Balance Artificial Rabbit Optimization (ES-AFDB-ARO) algorithm is employed. In this framework, the optimization stage is guided by spectral information obtained via the subspace stage to narrow the search space and improve convergence stability. Performance is evaluated under IEEE C37.118 steady-state and dynamic conditions via Monte Carlo simulations, showing that total vector error, frequency error, and rate-of-change-of-frequency error values remain within standard limits. Comparative analyses at 60 dB and 40 dB SNR demonstrate that the ES-AFDB-ARO method exhibits improved and more stable performance than the widely used interpolated discrete Fourier transform, Taylor weighted least squares and Taylor–Kalman filter methods. The results show that the proposed framework offers a reliable solution for synchrophasor estimation under dynamic operating conditions. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194587925
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        Value: 10.3390/en19112537
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 2537
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      – SubjectFull: Constrained optimization
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Transient analysis
        Type: general
      – SubjectFull: Electric power system management
        Type: general
      – SubjectFull: Electric power systems
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Phasor measurement
        Type: general
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      – TitleFull: A Subspace-Guided Constrained Optimization Framework for M-Class Synchrophasor Estimation Under Nonstationary Conditions.
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 11
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            – TitleFull: Energies (19961073)
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