Yuting Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yuting Chen has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Ning Zhou
Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY, USA
3 shared publications - Ziang Zhang
Binghamton University
3 shared publications - Zhenhuan Ding
Anhui University
1 shared publication
Latest Publications
- Open Access
- 31 December 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 13, 10805-10819
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 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.
- 31 December 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 13, 10805-10819
Open AccessQuantifying 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 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.
- 11 August 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Power Systems
- Vol. 39 (2) , 4549-4560
Modified Eigen-Decomposition-Based Interval Analysis (MEDIA) for Power System Dynamic State Estimation
The Bayesian approach has been used for the dynamic state estimation (DSE) of a power system. However, due to the complexity of noise resources, it is difficult to quantify measurement and process noise using probability density functions (PDFs). To overcome the difficulty, the authors of this article propose a modified eigen-decomposition-based interval analysis (MEDIA) method, which employs bounds instead of PDFs to quantify the noise, and uses the eigen decomposition method to reduce the negative impact of the overestimation problem. Using the simulation data generated from IEEE 16-machine and IEEE 10-machine systems, it is shown that the proposed MEDIA method can estimate the hard boundaries of dynamic states in real time. Comparison with the forward-backward propagation method and the extended set-membership filter also shows that the proposed MEDIA method performs better by providing narrower boundaries in the DSE.
- 11 August 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Power Systems
- Vol. 39 (2) , 4549-4560
Modified Eigen-Decomposition-Based Interval Analysis (MEDIA) for Power System Dynamic State Estimation
The Bayesian approach has been used for the dynamic state estimation (DSE) of a power system. However, due to the complexity of noise resources, it is difficult to quantify measurement and process noise using probability density functions (PDFs). To overcome the difficulty, the authors of this article propose a modified eigen-decomposition-based interval analysis (MEDIA) method, which employs bounds instead of PDFs to quantify the noise, and uses the eigen decomposition method to reduce the negative impact of the overestimation problem. Using the simulation data generated from IEEE 16-machine and IEEE 10-machine systems, it is shown that the proposed MEDIA method can estimate the hard boundaries of dynamic states in real time. Comparison with the forward-backward propagation method and the extended set-membership filter also shows that the proposed MEDIA method performs better by providing narrower boundaries in the DSE.