Sag Estimation of Real Transmission Systems for Faults Along the Lines in the Presence of Distributed Generation

Mambro, Enrica Di; Caramia, P.; Varilone, P.; Verde, P.

doi:10.1109/eeeic.2018.8493787

Cited by 2 publications

(1 citation statement)

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“…also, AOV can be achieved by using open and bracket methods to solve voltage equations, which are not appropriate due to convergence problems [18,19], and to estimate the information of other unmonitored buses. In [20,21], a method is suggested to solve an undetermined analytical set of equations by integer linear programming or singular value decomposition, but in these methods, each fault on the network must stimulate at least one monitored bus, which requires more monitored buses. In [22], to estimate the voltage information of unmonitored buses, the most probable fault location is used using the set of potential fault locations, which is based on the fault type.…”

Section: Introductionmentioning

confidence: 99%

Optimal placement of STATCOM using a reduced computational burden by minimum number of monitoring units based on area of vulnerability

Jalalat

Liasi

Bina

et al. 2023

IET Generation Trans & Dist

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Various Flexible Alternating Current Transmission System (FACTS) devices are used to improve the power quality and reliability of power system. In addition to the technical constraints, the installation and maintenance costs limit the exploitation of such devices. To maximize the efficiency of FACTS, the least possible number of devices must be placed optimally in the network. In this vein, many types of research have been conducted to offer optimal placement solutions. Although these methods lead to optimal placement, they usually suffer from a huge amount of computational burden. Therefore, here, an intelligent optimal placement approach is presented, focusing on reducing computational volume. For this aim in the suggested method, the monitoring buses are limited, while monitoring other buses is carried out using an estimation approach. To avoid increasing calculations for this selection, applying the worst fault condition instead of all fault types, the least number of monitoring buses are selected. Moreover, high‐risk zones are indicated for each monitoring bus so that by applying different fault conditions in only these areas, the study is conducted, which results in an additional decrement in computational burden. Finally, the optimal placement problem is solved by employing the genetic algorithm.

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Section: Introductionmentioning

confidence: 99%