A Bayesian Approach to Real-Time Dynamic Parameter Estimation Using Phasor Measurement Unit Measurement

Journal Article · Wed Sep 18 00:00:00 EDT 2019 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2019.2942133· OSTI ID:1727267

Xu, Yijun  ^[1]; Mili, Lamine  ^[1]; Chen, Xiao  ^[2]; Korkali, Mert  ^[2]; Min, Liang ^[3]

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1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
3. Stanford Univ., CA (United States)

Here, we develop a polynomial-chaos-expansion (PCE)-based approach for decentralized dynamic parameter estimation through Bayesian inference. Using this approach, the non-Gaussian distribution of the inverted parameters is obtained. More specifically, we first represent the decentralized generator model with the PCE-based surrogate. This surrogate allows us to efficiently evaluate the time-consuming dynamic solver at parameter values through Metropolis-Hastings (M-H)-based Markov chain Monte Carlo (MCMC). Then, we propose a two-stage hybrid Markov chain Monte Carlo (MCMC) to recover a posteriori distribution of the decentralized generator model parameters. In the first stage, we use the gradient-enhanced Langevin MCMC algorithm to characterize an intermediate posterior parameter distribution. This algorithm is computationally scalable to the high-dimensional parameter space. Based on the intermediate posterior distribution, during the second stage, we use the adaptive MCMC algorithm to fine-tune the strong correlations between the parameters. Finally, the fully recovered a posterior distribution is obtained in the end. The simulation results show that the proposed PCE-based hybrid MCMC algorithm can accurately and efficiently estimate the high-dimensional generator dynamic model parameters with full probabilistic distribution provided.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity (OE)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1727267
• Report Number(s): LLNL-JRNL-763718; 953612
• Journal Information: IEEE Transactions on Power Systems, Vol. 35, Issue 2; ISSN 0885-8950
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
42 ENGINEERING
dynamic parameter estimation
polynomial chaos expansion
Bayesian inference
Metropolis-Hastings algorithm
Langevin MCMC
adaptive MCMC
analysis of variance