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A Simplified Bayesian Learning Technique for harmonic state estimation


Eslami, A and Negnevitsky, M and Franklin, Evan and Lyden, S, A Simplified Bayesian Learning Technique for harmonic state estimation, 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 12-15 June 2022, Manchester, United Kingdom, pp. 1-6. (2022) [Refereed Conference Paper]

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DOI: doi:10.1109/PMAPS53380.2022.9810588


Direct harmonic monitoring of a complete power system can be costly and impractical. Harmonic State Estimation (HSE) refers to indirect monitoring techniques, where unknown harmonic variables are estimated based on limited observations. HSE is crucial in developing harmonic monitoring systems and thus enabling high power quality. In this paper, a universal formulation for HSE is derived and a Simplified Bayesian Learning (SBL) technique based on Markov Chain Monte Carlo (MCMC) simulation is proposed to solve the problem for different cases of simultaneously operating harmonic sources. Metropolis Random Walk (MRW) and Importance Sampling (IS) are used for MCMC sampling, where the latter can be used to alleviate the problem of needing to choose the proposal distribution. Different cases in the presence of uncertainty in measurements and network parameters are studied, demonstrating the usefulness of the proposed method.

Item Details

Item Type:Refereed Conference Paper
Keywords:Bayesian learning, harmonic monitoring, harmonic state estimation, Markov Chain Monte Carlo
Research Division:Engineering
Research Group:Electrical engineering
Research Field:Electrical energy generation (incl. renewables, excl. photovoltaics)
Objective Division:Energy
Objective Group:Renewable energy
Objective Field:Renewable energy not elsewhere classified
UTAS Author:Eslami, A (Mr Ahmadreza Eslami)
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
UTAS Author:Franklin, Evan (Associate Professor Evan Franklin)
UTAS Author:Lyden, S (Dr Sarah Lyden)
ID Code:155401
Year Published:2022
Deposited By:Engineering
Deposited On:2023-02-17
Last Modified:2023-02-28

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