scispace - formally typeset
Journal ArticleDOI

Taguchi method-based probabilistic load flow studies considering uncertain renewables and loads

Ying-Yi Hong, +2 more
- 01 Feb 2016 - 
- Vol. 10, Iss: 2, pp 221-227
Reads0
Chats0
TLDR
In this article, the authors proposed a load flow analysis based on Taguchi's orthogonal arrays (OAA) to estimate the means and standard deviations of bus voltages, phase angles, line flows, and other metrics.
Abstract
Load flow studies are crucial in investigations of operation and planning problems in the power systems. Traditional methods for determining load flow are based on deterministic approaches. However, the parameters of a power system (such as load and renewable power generation) may be uncertain. An exact probabilistic load flow (PLF) study requires a long CPU time due to many convolution computations involving probability density functions. This paper proposes a novel PLF method that is based on Taguchi's orthogonal arrays. The proposed method utilises a few deterministic load flow solutions that are obtained using Taguchi's method to estimate the means and standard deviations of bus voltages, phase angles, line flows, and other metrics. A load flow calculation corresponds to an experiment in Taguchi's method. An optimal experiment is also specified by considering the largest deviation from the nominal load flow solution. A 25-bus standalone power system and a modified Institute of Electrical and Electronics Engineers (IEEE) 118-bus system are tested. The simulation results show that the proposed method not only requires fewer deterministic load flow solutions to perform PLF analysis than the traditional point-estimate method but also yields accurate means and standard deviations of bus voltages and line flows.

read more

Citations
More filters
Journal ArticleDOI

A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach

TL;DR: An efficient analytical method named multivariate-Gaussian mixture approximation is proposed for precise estimation of probabilistic load flow results and is justified in terms of accuracy and execution time.
Journal ArticleDOI

Application of RBF neural networks and unscented transformation in probabilistic power-flow of microgrids including correlated wind/PV units and plug-in hybrid electric vehicles

TL;DR: The ability of radial basis function artificial neural networks for nonlinear mapping is exploited with an acceptable level of accuracy, and even exact to solve nonlinear equation set of power-flow analysis, and the speed of the algorithm is improved.
Journal ArticleDOI

Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: possibilistic–probabilistic power flow based on RBFNNs

TL;DR: In this article, a new method based on fuzzy unscented transform and radial basis function neural networks (RBFNN) was proposed for possibilistic-PPF in the micro-grids including uncertain loads, correlated wind and solar distributed energy resources and plug-in hybrid electric vehicles.
Journal ArticleDOI

Optimal design of permanent magnet linear synchronous motors based on Taguchi method

TL;DR: In this article, the authors focus on optimisation design of permanent magnet linear synchronous motors that are applied in laser engraving machines with no cutting force and propose the Taguchi method based on orthogonal array to optimise the thrust and thrust ripple.
Journal ArticleDOI

A robust design of maximum power point tracking using Taguchi method for stand-alone PV system

TL;DR: In this paper, the authors proposed a robust maximum power point tracking (MPPT) robust design for solar photovoltaic (PV) cells, which relies on various combinations of insolations, temperatures, and tilt angles.
References
More filters
Journal ArticleDOI

Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion

TL;DR: In this paper, a probabilistic load flow analysis of transmission line flows is proposed for the purpose of using it as a quick screening tool to determine the major investment on improving transmission system inadequacy.
Journal ArticleDOI

Point Estimate Schemes to Solve the Probabilistic Power Flow

TL;DR: In this article, four different Hong's point estimate schemes are presented and tested on the probabilistic power flow problem and compared against those obtained from the Monte Carlo simulation, showing that the use of the scheme provides the best performance when a high number of random variables, both continuous and discrete, are considered.
Journal ArticleDOI

Probabilistic load-flow computation using point estimate method

TL;DR: A new probabilistic load-flow solution algorithm based on an efficient point estimate method that can be used directly with any existing deterministic load- flow program and compared with those obtained from Monte Carlo simulation technique and combined simulation and analytical method.
Journal ArticleDOI

An efficient point estimate method for probabilistic analysis

TL;DR: In this article, a new and efficient point estimate method is developed to calculate the statistical moments of a random quantity, Z, that is a function of n random variables, X. The method is an extension of Rosenblueth's two-point concentration method.
Journal ArticleDOI

Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition

TL;DR: LHS-CD sampling method combined with Cholesky decomposition method is found to be robust and flexible and has the potential to be applied in many power system probabilistic problems.
Related Papers (5)