Bayesian Inertia Estimation via Parallel MCMC Hammer in Power Systems

Weidong Zhong, Chun Li, Minghua Chu, Yuanhong Che, Shuyang Zhou, Zhi Wu and Kai Liu ()
Additional contact information
Weidong Zhong: State Grid Zhejiang Electric Power Co., Ltd., Jiaxing Power Supply Company, Jiaxing 314033, China
Chun Li: State Grid Zhejiang Electric Power Co., Ltd., Jiaxing Power Supply Company, Jiaxing 314033, China
Minghua Chu: State Grid Zhejiang Electric Power Co., Ltd., Haining Power Supply Company, Haining 314400, China
Yuanhong Che: State Grid Zhejiang Electric Power Co., Ltd., Jiaxing Power Supply Company, Jiaxing 314033, China
Shuyang Zhou: The Electrical Engineering Department, Southeast University, Nanjing 210096, China
Zhi Wu: The Electrical Engineering Department, Southeast University, Nanjing 210096, China
Kai Liu: The Electrical Engineering Department, Southeast University, Nanjing 210096, China

Energies, 2025, vol. 18, issue 15, 1-18

Abstract: The stability of modern power systems has become critically dependent on precise inertia estimation of synchronous generators, particularly as renewable energy integration fundamentally transforms grid dynamics. Increasing penetration of converter-interfaced renewable resources reduces system inertia, heightening the grid’s susceptibility to transient disturbances and creating significant technical challenges in maintaining operational reliability. This paper addresses these challenges through a novel Bayesian inference framework that synergistically integrates PMU data with an advanced MCMC sampling technique, specifically employing the Affine-Invariant Ensemble Sampler. The proposed methodology establishes a probabilistic estimation paradigm that systematically combines prior engineering knowledge with real-time measurements, while the Affine-Invariant Ensemble Sampler mechanism overcomes high-dimensional computational barriers through its unique ensemble-based exploration strategy featuring stretch moves and parallel walker coordination. The framework’s ability to provide full posterior distributions of inertia parameters, rather than single-point estimates, helps for stability assessment in renewable-dominated grids. Simulation results on the IEEE 39-bus and 68-bus benchmark systems validate the effectiveness and scalability of the proposed method, with inertia estimation errors consistently maintained below 1 % across all generators. Moreover, the parallelized implementation of the algorithm significantly outperforms the conventional M-H method in computational efficiency. Specifically, the proposed approach reduces execution time by approximately 52 % in the 39-bus system and by 57 % in the 68-bus system, demonstrating its suitability for real-time and large-scale power system applications.

Keywords: inertia estimation; Bayesian inference; MCMC hammer; PMU measurement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/15/3905/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/15/3905/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:15:p:3905-:d:1707207

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().