Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 13, Issue 3, Pages 1853-1856Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2022.3159391
Keywords
Probability distribution; Wind power generation; Wind speed; Uncertainty; Probability density function; Load flow; Estimation; Gaussian mixture model; wind power; probabilistic power flow; uncertainty; parameter estimation
Categories
Funding
- National Natural Science Foundation of China [U2166201, 52077136, PESL-00276-2021]
Ask authors/readers for more resources
This article proposes a parameter estimation method for GMM with large component numbers and derives a closed-form solution for PPF calculation, validating the importance of large component numbers. The proposed uncertainty modeling method shows superiority in describing the details of wind power probability densities.
The Gaussian mixture model (GMM) is a powerful tool to establish the probability distributions of random variables in power system analyses. GMM can model arbitrary probability distributions by increasing the number of its Gaussian components, but the commonly used expectation-maximization (EM) algorithm fails to obtain accurate GMM for large component numbers, which limits the application of GMM to multivariate wind power modeling. In this letter, a parameter estimation method for GMM with large component numbers is proposed based on kernel density estimation (KDE) and the improved density-preserving hierarchical EM algorithm. Then, the closed-form solution to probabilistic power flow (PPF) calculation is derived based on piecewise linearization and GMM, which validates the importance of large component numbers. Finally, the proposed uncertainty modeling method is compared with EM-based GMM, Copula functions, KDE, k-nearest neighbors and block neural autoregressive flow on actual wind speed data to validate its superiority in describing the details of probability densities of wind power. PPF calculation is performed to show the efficiency and accuracy of the proposed uncertainty analysis method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available