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A Robust Hybrid WLS-EKF Algorithm for Power System State Estimation.

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Title: A Robust Hybrid WLS-EKF Algorithm for Power System State Estimation.
Authors: Javid, Zahid1,2 (AUTHOR), Lohana, Kush2 (AUTHOR), Murtaza, Danial2 (AUTHOR), Holderbaum, William3 (AUTHOR) w.holderbaum@mmu.ac.uk
Source: Energy Engineering. 2026, Vol. 123 Issue 7, p1-23. 23p.
Subject Terms: *Kalman filtering, *Statistical weighting, *Outlier detection, *Algorithms, *Real-time computing, *Estimation theory, *Robust statistics
Abstract: This paper introduces a novel hybrid method for Power System State Estimation (PS-SE) that effectively integrates the strengths of Weighted Least Squares (WLS) and the Extended Kalman Filter (EKF) through an adaptive weighting mechanism. The proposed method addresses key challenges in modern PS-SE, including measurement uncertainties, bad data detection and handling, and convergence reliability. By incorporating an adaptive weighting mechanism, the hybrid approach dynamically adjusts estimation parameters based on the quality of the measurements, enabling it to maintain high accuracy for clean data while demonstrating exceptional resilience against outliers and noisy measurements. The performance of the proposed method is rigorously evaluated against established state estimation techniques, including WLS, EKF, Bayesian method, Huber-Adaptive Method (HAM) and Neural network-based Method. Simulations are performed on IEEE 14-bus, IEEE 34-bus and IEEE 342-bus test systems to assess estimation accuracy, convergence behavior, computational efficiency, and robustness in the presence of bad data. Results highlight the superior performance of the hybrid method, which achieves higher accuracy and robust convergence properties while requiring 40% fewer iterations than conventional WLS. Despite its enhanced capabilities, the computational burden remains comparable to traditional techniques, making it highly suitable for real-time applications. These findings underscore the proposed hybrid method as a significant advancement in power system state estimation, offering a reliable, efficient, and robust solution for modern power system monitoring and control. It represents a promising approach to address the increasing complexity and data uncertainties in contemporary power grids. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
FullText Text:
  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 195000822
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Robust Hybrid WLS-EKF Algorithm for Power System State Estimation.
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  Data: <searchLink fieldCode="AR" term="%22Javid%2C+Zahid%22">Javid, Zahid</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lohana%2C+Kush%22">Lohana, Kush</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Murtaza%2C+Danial%22">Murtaza, Danial</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Holderbaum%2C+William%22">Holderbaum, William</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> w.holderbaum@mmu.ac.uk</i>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energy+Engineering%22">Energy Engineering</searchLink>. 2026, Vol. 123 Issue 7, p1-23. 23p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+weighting%22">Statistical weighting</searchLink><br />*<searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br />*<searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper introduces a novel hybrid method for Power System State Estimation (PS-SE) that effectively integrates the strengths of Weighted Least Squares (WLS) and the Extended Kalman Filter (EKF) through an adaptive weighting mechanism. The proposed method addresses key challenges in modern PS-SE, including measurement uncertainties, bad data detection and handling, and convergence reliability. By incorporating an adaptive weighting mechanism, the hybrid approach dynamically adjusts estimation parameters based on the quality of the measurements, enabling it to maintain high accuracy for clean data while demonstrating exceptional resilience against outliers and noisy measurements. The performance of the proposed method is rigorously evaluated against established state estimation techniques, including WLS, EKF, Bayesian method, Huber-Adaptive Method (HAM) and Neural network-based Method. Simulations are performed on IEEE 14-bus, IEEE 34-bus and IEEE 342-bus test systems to assess estimation accuracy, convergence behavior, computational efficiency, and robustness in the presence of bad data. Results highlight the superior performance of the hybrid method, which achieves higher accuracy and robust convergence properties while requiring 40% fewer iterations than conventional WLS. Despite its enhanced capabilities, the computational burden remains comparable to traditional techniques, making it highly suitable for real-time applications. These findings underscore the proposed hybrid method as a significant advancement in power system state estimation, offering a reliable, efficient, and robust solution for modern power system monitoring and control. It represents a promising approach to address the increasing complexity and data uncertainties in contemporary power grids. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=195000822
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.32604/ee.2026.080073
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1
    Subjects:
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Statistical weighting
        Type: general
      – SubjectFull: Outlier detection
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Real-time computing
        Type: general
      – SubjectFull: Estimation theory
        Type: general
      – SubjectFull: Robust statistics
        Type: general
    Titles:
      – TitleFull: A Robust Hybrid WLS-EKF Algorithm for Power System State Estimation.
        Type: main
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            NameFull: Javid, Zahid
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            NameFull: Lohana, Kush
      – PersonEntity:
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            NameFull: Murtaza, Danial
      – PersonEntity:
          Name:
            NameFull: Holderbaum, William
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          Dates:
            – D: 01
              M: 07
              Text: 2026
              Type: published
              Y: 2026
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              Value: 01998595
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              Value: 123
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: Energy Engineering
              Type: main
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