Genetic Algorithm Based Optimal Placement of T csc and Upfc in the Nigeria 330KV Integrated Transmission Line Network at Different Reactive Power Loadings

Eseosa, Omorogiuwa; ical, Electr; Roland, Ulf

doi:10.25103/jestr.065.012

Cited by 1 publication

(2 citation statements)

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“…Genetic Algorithm (GA) is considered as one of the most important evolutionary algorithms based on mechanism of natural selection and genetics for solving the constrained and unconstrained optimization problems [24,25]. It is worth noting that GA can search simultaneously several possible solutions without require to prior knowledge or special properties of the objective function [24,26,27]…”

Section: Genetic Algorithm Process and Problem Formulation 31 Genetmentioning

confidence: 99%

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Optimal number and location of UPFC devices to enhence voltage profile and minimizing losses in electrical power systems

Sekhane

Djamel

2019

IJECE

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The fast increase of loads around the world has made electrical networks more and more complex and difficult to operate close to its capacities. This is has led to many problems such as voltage collapse and energy losses. Therefore, flexible alternating current transmission systems (FACTS) are considred as a best solution for solving these problems. Unified Power Flow Controller UPFC is one of the most important and powerful FACTS devices due to its ability to increase the transmission capacity of the power system and reduce the total line losses. The problem of optimizing its number, location and size has become an important requirement for best advantages of this device. In this paper, a proposed relationship to identify the maximum number of FACTS devices that can be installed for a given power network is introduced in the search process code to determine the optimal number, optimal placement and size of UPFC device to enhance voltages profile and reduce overall system losses in the standard IEEE 14 bus test system using genetic algorithm (GA). The obtained results show clearly that all control parameters of UPFCs in each case are within their limits, and whenever the number of UPFCs installed increases, both voltage deviation and total losses well decreases. They also show that the application of the proposed relationship in the search process code facilitates greatly the search for optimal number, optimal placement and size of UPFC devices and reduces the calculation time. On the other hand, the obtained results has been scientifically justified and compared with other works reported in the literature.

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Section: Genetic Algorithm Process and Problem Formulation 31 Genetmentioning

confidence: 99%

“…Firstly, the algorithm generates an initial population of a random size [7,24]. For more explanation, assume two individuals (chromosomes) as an initial population as shown in Figure 2(a).…”

Section: Initialize a Population Of Chromosomesmentioning

confidence: 99%

Optimal number and location of UPFC devices to enhence voltage profile and minimizing losses in electrical power systems

Sekhane

Djamel

2019

IJECE

View full text Add to dashboard Cite

show abstract

Genetic Algorithm Based Optimal Placement of T csc and Upfc in the Nigeria 330KV Integrated Transmission Line Network at Different Reactive Power Loadings

Cited by 1 publication

References 9 publications

Optimal number and location of UPFC devices to enhence voltage profile and minimizing losses in electrical power systems

Optimal number and location of UPFC devices to enhence voltage profile and minimizing losses in electrical power systems

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