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...
Saved in:
Main Authors: | Yuting Chen, Ning Zhou, Ziang Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10819390/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A modified proximal point algorithm for solving variational inclusion problem in real Hilbert spaces
by: Thierno M. M. Sow
Published: (2020-06-01) -
Mathematical Modeling of Tuberculosis Transmission Dynamics With Reinfection and Optimal Control
by: Francis Oketch Ochieng
Published: (2025-01-01) -
Human resource evaluation using a GRA-based decision model in an interval-valued fuzzy environment
by: Seyed Mahdi Ghanizadeh
Published: (2024-06-01) -
Relevant SMS Spam Feature Selection Using Wrapper Approach and XGBoost Algorithm
by: Diyari Jalal Mussa, et al.
Published: (2019-11-01) -
Psychometric properties of the arabic version of occupational value with pre-defined ítems
by: Mona Eklund, et al.
Published: (2024-12-01)