Publication

Existence and Uniqueness of Load-Flow Solutions in Three-Phase Distribution Networks

Jean-Yves Le Boudec, Mario Paolone, Andrey Bernstein, Cong Wang
2017
Journal Articles

Abstract

We present sufficient conditions for the existence and uniqueness of load-flow solutions in three-phase distribution networks. The conditions can be efficiently verified for real distribution systems.

Official source

https://infoscience.epfl.ch/entities/publication/326c16d2-a00b-4d82-9dac-32e389c3d724

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Jean-Yves Le Boudec, Mario Paolone, Andrey Bernstein, Cong Wang
2017
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/326c16d2-a00b-4d82-9dac-32e389c3d724

About this result

Related concepts (14)

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Normal distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.

Pearson distribution

The Pearson distribution is a family of continuous probability distributions. It was first published by Karl Pearson in 1895 and subsequently extended by him in 1901 and 1916 in a series of articles on biostatistics. The Pearson system was originally devised in an effort to model visibly skewed observations. It was well known at the time how to adjust a theoretical model to fit the first two cumulants or moments of observed data: Any probability distribution can be extended straightforwardly to form a location-scale family.

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