A group sparse Bayesian learning algorithm for harmonic state estimation in power systems

Zhou, Wei; Wu, Yue; Huang, Xiang; Lu, Renzhi; Zhang, Haitao

doi:10.1016/j.apenergy.2021.118063

Cited by 8 publications

(1 citation statement)

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“…In addition, the robustness of data-driven approaches to system and topology changes as well as missing or inaccuracies in system information has made them the focus of many researchers recently [1][2][3][4][5][6]. Examples of data-driven and machine-learning-based state estimation techniques in power systems include techniques based on neural networks [4,15], the Bayesian approach [3,16], minimum mean squared error (MMSE) [5,17], and the Kalman filter (KF) [2,18]. Since KF and its variations, including the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), have a great ability to model and express dynamic systems, many dynamic state estimation algorithms for power systems are developed based on these techniques [2,14,19,20].…”

Section: Related Workmentioning

confidence: 99%

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Hossain

Naeini

2022

Energies

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Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.

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