Universidad Tecnológica de Pereira. Perira (Colombia)
July 24th, 2018
Summary:
A probabilistic power flow (PPF) study is an essential tool for the analysis and planning of a power system when specific variables are considered as random variables with particular probability distributions. The most widely used method for solving the PPF problem is Monte Carlo simulation (MCS). Although MCS is accurate for obtaining the uncertainty of the state variables, it is also computationally expensive, since it relies on repetitive deterministic power flow solutions. On the other hand, MCS does not take into account the fact that previous knowledge of state variables might be available in terms of probability distributions. In this thesis, we frame the PPF as a probabilistic inference problem, and instead of repetitively solving optimization problems, we use Bayesian inference for computing posterior distributions over state variables. We specifically use prior distributions for the state variables, and a likelihood function that relates the observations to the state variables. We apply Bayes theorem to obtain the posterior distribution over the state variables. By using a Bayesian inference perspective, we can model the state variables as random variables, and we do not need to solve heavy computational optimization methods for computing posterior distributions over state variables.
Descriptors: Numerical analysis, Probability, Statistics, Electrical technology and engineering
Citation:
C.D. Zuluaga-Ríos (2018), Topics in bayesian inference applied to probabilistic power flow analysis. Universidad Tecnológica de Pereira. Perira (Colombia).
