Simultaneous Identification and Correction of Multiple Network Parameter Errors by Mixed-Effects Models

Liang, Defu; Zeng, Lin; Chiang, Hsiao‐Dong

doi:10.1109/tcns.2021.3124899

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…Based on the SE theory, this section presents the LME method (Liang et al, 2021), which is then improved in the next section to improve the computation efficiency. This LME method has shown to be very effective for simultaneous identification and correction of multiple network parameters.…”

Section: The Lme Methods For Estimation Of Mevsmentioning

confidence: 99%

“…It is also referred to as a "false alarm" because correct parameters are misidentified as erroneous. To this end, the ESA method (Liang et al, 2021) is further used to move misidentified but correct parameters from the suspicious parameter set back to the correct parameter set while correcting the identified erroneous parameters.…”

Section: Step 4: Suspicious Parameter Selection By Hypothesis Testingmentioning

confidence: 99%

“…In (Zhao et al, 2018), the projection statistics is used for selection of suspicious parameters associated with leverage points, which is shown to be insensitive to the smearing and masking effects. In (Liang et al, 2021), a linear mixed-effects (LME)-based method is proposed for simultaneous identification of multiple network parameter errors. Suspicious parameter set is constructed by solving an LME model and hypothesis testing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Identification of Multiple Parameter Errors in Power Grids by Mixed-Effects Models and Generalized Least Squares

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Accurate knowledge of static parameters forms the basis for nearly all applications in the energy management system. This paper proposes an efficient method for the simultaneous identification and correction of multiple network parameter errors based on a linear mixed-effects (LME) model and the generalized least squares (GLS) method. An LME model for parameter error identification is formulated using the residual equations of multiple-snapshot state estimation with equality constraints. The parameter errors are considered as the fixed effects and the measurement errors are considered as the random effects. Then, using the measurement error variances estimated by the LME model, the GLS method is used to estimate the parameter errors along with a hypothesis testing to infer whether each parameter error is zero. The semi-supervised adversarial autoencoder is used for bad data detection in the presence of erroneous parameters and limited labels such that only measurement snapshots that are free of any bad data are used. The proposed methodology is efficient in that the LME model is only used to estimate the variances of the measurement errors using a small number of measurement snapshots, therefore the huge computation burden needed to solve a large-scale LME model is avoided. In addition, the GLS only involves inversion of low-dimension matrices, which is very efficient even a large number of measurement snapshots are used. Thorough tests of the proposed methodology on a large number of scenarios are provided to show the effectiveness of the proposed methodology with promising results.

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Section: The Lme Methods For Estimation Of Mevsmentioning

confidence: 99%

Section: Step 4: Suspicious Parameter Selection By Hypothesis Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Identification of Multiple Parameter Errors in Power Grids by Mixed-Effects Models and Generalized Least Squares

Liang

Cheng

et al. 2022

Front. Energy Res.

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Online Handwritten Chinese Character Recognition and Real-Time Error Correction Based on Deep Learning and Intelligent System

Lai

2024

2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON)

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Simultaneous Identification and Correction of Multiple Network Parameter Errors by Mixed-Effects Models

Cited by 2 publications

References 29 publications

Efficient Identification of Multiple Parameter Errors in Power Grids by Mixed-Effects Models and Generalized Least Squares

Efficient Identification of Multiple Parameter Errors in Power Grids by Mixed-Effects Models and Generalized Least Squares

Online Handwritten Chinese Character Recognition and Real-Time Error Correction Based on Deep Learning and Intelligent System

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