Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval Analysis
The Bayesian framework is conventionally adopted in power system static state estimation (SSE) to quantify uncertainty via probability density functions (PDFs). However, the reliability of such PDFs is frequently undermined by the complex nature of noise in measurement systems, potentially leading t...
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IEEE
2025-01-01
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| author | Yuting Chen Ning Zhou Ziang Zhang |
| author_facet | Yuting Chen Ning Zhou Ziang Zhang |
| author_sort | Yuting Chen |
| collection | DOAJ |
| description | The Bayesian framework is conventionally adopted in power system static state estimation (SSE) to quantify uncertainty via probability density functions (PDFs). However, the reliability of such PDFs is frequently undermined by the complex nature of noise in measurement systems, potentially leading to significant estimation inaccuracies. To address this problem, this paper proposes a modified Krawczyk-forward-backward synthesis (MoK-FoBS) method to quantify uncertainty in SSE through interval analysis. The proposed MoK-FoBS method combines the strengths of the modified forward-backward propagation (FBP) method with the modified Krawczyk method to mitigate the overestimation problem. Employing simulation data derived from IEEE testing systems, it is verified through the Monte Carlo method that the MoK-FoBS method can estimate hard boundaries that invariably contain the true state values. In contrast, the true state values may lie outside the uncertainty boundaries estimated by the weighted least squares approach. A comparative analysis reveals that the MoK-FoBS method can achieve narrower state boundaries than the FBP method, thereby improving estimation precision. |
| format | Article |
| id | doaj-art-b0d3aa2406714834be744298c884da16 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b0d3aa2406714834be744298c884da162025-01-21T00:01:02ZengIEEEIEEE Access2169-35362025-01-0113108051081910.1109/ACCESS.2024.352456310819390Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval AnalysisYuting Chen0https://orcid.org/0000-0001-6516-2471Ning Zhou1https://orcid.org/0000-0003-3876-1799Ziang Zhang2https://orcid.org/0000-0003-1240-5710Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY, USADepartment of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY, USADepartment of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY, USAThe Bayesian framework is conventionally adopted in power system static state estimation (SSE) to quantify uncertainty via probability density functions (PDFs). However, the reliability of such PDFs is frequently undermined by the complex nature of noise in measurement systems, potentially leading to significant estimation inaccuracies. To address this problem, this paper proposes a modified Krawczyk-forward-backward synthesis (MoK-FoBS) method to quantify uncertainty in SSE through interval analysis. The proposed MoK-FoBS method combines the strengths of the modified forward-backward propagation (FBP) method with the modified Krawczyk method to mitigate the overestimation problem. Employing simulation data derived from IEEE testing systems, it is verified through the Monte Carlo method that the MoK-FoBS method can estimate hard boundaries that invariably contain the true state values. In contrast, the true state values may lie outside the uncertainty boundaries estimated by the weighted least squares approach. A comparative analysis reveals that the MoK-FoBS method can achieve narrower state boundaries than the FBP method, thereby improving estimation precision.https://ieeexplore.ieee.org/document/10819390/Forward-backward propagation methodinterval analysisKrawczyk methodstatic state estimation |
| spellingShingle | Yuting Chen Ning Zhou Ziang Zhang Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval Analysis IEEE Access Forward-backward propagation method interval analysis Krawczyk method static state estimation |
| title | Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval Analysis |
| title_full | Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval Analysis |
| title_fullStr | Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval Analysis |
| title_full_unstemmed | Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval Analysis |
| title_short | Quantifying Uncertainty in State Estimation: The MoK-FoBS Method via Interval Analysis |
| title_sort | quantifying uncertainty in state estimation the mok fobs method via interval analysis |
| topic | Forward-backward propagation method interval analysis Krawczyk method static state estimation |
| url | https://ieeexplore.ieee.org/document/10819390/ |
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