Showing 3 results for Distribution Networks
M. Aliakbar-Golkar, Y. Raisee-Gahrooyi,
Volume 4, Issue 4 (12-2008)
Abstract
This paper compares fault position and Monte Carlo methods as the most
common methods in stochastic assessment of voltage sags. To compare their abilities,
symmetrical and unsymmetrical faults with different probability distribution of fault
positions along the lines are applied in a test system. The voltage sag magnitude in different
nodes of test system is calculated. The problem with these two methods is that they require
unknown number of iteration in Monte Carlo Method and number of fault position to
converge to an acceptable solution. This paper proposes a method based on characteristic
behavior of Monte Carlo simulations for determination required number of iteration in
Monte Carlo method.
O. Honarfar, A. Karimi,
Volume 16, Issue 3 (9-2020)
Abstract
Distribution load flow (DLF) calculation is one of the most important tools in distribution networks. DLF tools must be able to perform fast calculations in real-time studies at the presence of distributed generators (DGs) in a smart grid environment even in conditions of change in the network topology. In this paper, a new method for DLF in radial active distribution networks is proposed. The method performs a very fast DLF using zooming algorithm associated with a fast-decoupled reactive power compensation (ZAFDRC) technique, not in all of the buses of the grid, causes to reduce the solution time, which is the most important issue in the real-time studies. The proposed method is based on the zooming algorithm and does not require to calculate the bus-injection to branch-current (BIBC) matrix which reduces the computational burden and helps to decrease the solution time. The method is tested on the IEEE 69-bus systems as a balanced network and the IEEE 123-bus as a very unbalanced system. The results confirm the high accuracy and high speed of the proposed method.
M. Najjarpour, B. Tousi, S. Jamali,
Volume 18, Issue 4 (12-2022)
Abstract
Optimal power flow is an essential tool in the study of power systems. Distributed generation sources increase network uncertainties due to their random behavior, so the optimal power flow is no longer responsive and the probabilistic optimal power flow must be used. This paper presents a probabilistic optimal power flow algorithm using the Taguchi method based on orthogonal arrays and genetic algorithms. This method can apply correlations and is validated by simulation experiments in the IEEE 30-bus network. The test results of this method are compared with the Monte Carlo simulation results and the two-point estimation method. The purpose of this paper is to reduce the losses of the entire IEEE 30-bus network. The accuracy and efficiency of the proposed Taguchi correlation method and the genetic algorithm are confirmed by comparison with the Monte Carlo simulation and the two-point estimation method. Finally, with this method, we see a reduction of 5.5 MW of losses.