Firefly Algorithm for Solving Load Flow Problem

Tazi, Abdelhak; Sabiri, Zakaria; Belbounaguia, Noureddine; Mkaddem, Kheddioui El; Bezza, Mohamed

doi:10.1109/repsgie.2018.8488871

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The FA approach to this problem used the parameters that have been used in the calculation of power flow as follows number of firefly=40, α=0.5, β 0 =1, β min =0.2, γ=1, Iter max =100 [16]. www.ijacsa.thesai.org The proposed algorithm has been tested in Matlab 2016a.…”

Section: Implementation Of the Fa Opf In Determining Optimal Dgmentioning

confidence: 99%

“…Performance of the firefly algorithm is measured using the Real Power Loss Index (RPLI) [21], Voltage Profile Index (VPI) [22], and Voltage Sensitivity Index (VSI) [23]. The equation of each index is shown in (16), (17), and (18). RPLI = (P loss after DG/P loss without DG)…”

Section: B Optimal Multiple Of Multi-type Dgmentioning

confidence: 99%

“…In this study, using techniques developed by Dr. Xin-She Yang named the firefly algorithm (FA) [14]. FA imitated the information of fireflies in solving problems where in previous studies have been proven to resolve technical issues by solving global solutions [15]- [16]. The flash produced by fireflies is formulated based on objective functions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selection of Sensitive Buses using the Firefly Algorithm for Optimal Multiple Types of Distributed Generations Allocation

Rahman¹,

Manjang²,

Yusran³

et al. 2019

IJACSA

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Power loss is one aspect of an electric power system performance indicator. Loss of power can have an impact on poor voltage performance at the receiving end. DG integration in the network has become one of the more powerful methods. To get the maximum benefit from synchronizing the system with DG, it is necessary to ascertain the size, location, and type of DG. This study aims to determine the capacity and location of DG connections for DG type I and type II. To address the aim of this paper, a metaheuristic solution based on a firefly algorithm is used. FA can cover up the lack of metaheuristic algorithms that require a long computational time. To ensure that the load bus location solution is selected as the best DG connection location, the input of the load bus candidate has been filtered based on stability sensitivity. The proposed method is tested on IEEE 30 buses. The optimization results show a decrease in power loss and an increase in bus voltage, which affects an increase in system stability by integrating three DG units. FA validation of the evolution-based algorithm shows a significant reduction in computational time.

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Section: Implementation Of the Fa Opf In Determining Optimal Dgmentioning

confidence: 99%

Section: B Optimal Multiple Of Multi-type Dgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Selection of Sensitive Buses using the Firefly Algorithm for Optimal Multiple Types of Distributed Generations Allocation

Rahman¹,

Manjang²,

Yusran³

et al. 2019

IJACSA

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show abstract

“…ANFIS and other method were used to calculate the short-term demand forecasting of a multi-carrier energy system and to optimize its energy flow [14]. There is other methods that were used in power system, for example a modified models of PSO algorithm was used to get the estimation solution of power system state [15], in reference [16], the authors used the continuous Newton's for power flow analysis, reference [17] proposed the firefly algorithm for solving load flow problem and authors of paper [18] discussed the optimal power flow based congestion management using enhanced genetic algorithms. Reference [19] proposed a study about strengths, solutions and limitations of ANFIS.…”

Section: Introductionmentioning

confidence: 99%

Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques

Fikri¹,

Sabri²,

Cheddadi³

2020

IJECE

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In the field of electrical network, it is necessary, under different conditions, to learn about the behavior of the system. Power Flow Analysis is the tool per excellent that allow as to make a deep study and define all quantities of each bus of the system. To determine power flow analysis there is a lot of methods, we have either numerical or intelligent techniques. Lately, researchers always work on finding intelligent methods that allow them to solve their complex problems. The goal of this article is to compare two intelligent methods that are capable of predicting quantities; Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System using real electrical networks. To do that we used few significant discrepancies. These methods are characterized by giving results in real time. To make this comparison successful, we implemented these two methods, to predict the voltage magnitudes and the voltage phase angles, on two Moroccan electrical networks. The results of the comparison show that the method of Adaptive Neuro-Fuzzy Inference System have more advantages than the method of Artificial Neural Network.

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Bitcoin Cryptocurrency Price Prediction Using IFA-BiLSTM

Pratama

2024

2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA)

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Firefly Algorithm for Solving Load Flow Problem

Cited by 3 publications

References 8 publications

Selection of Sensitive Buses using the Firefly Algorithm for Optimal Multiple Types of Distributed Generations Allocation

Selection of Sensitive Buses using the Firefly Algorithm for Optimal Multiple Types of Distributed Generations Allocation

Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques

Bitcoin Cryptocurrency Price Prediction Using IFA-BiLSTM

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