Power Flow Calculations by Deterministic Methods and Artificial Intelligence Method

Fikri, Meriem; Haidi, Touria; Cheddadi, Bouchra; Sabri, Osama; Majdoub, Meriem; Belfqih, Abdelaziz

doi:10.22161/ijaers.5.6.25

Cited by 4 publications

(2 citation statements)

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“…The most common ANN structure consists of an input layer, one or more hidden layers and an output layer. For more details check references [20][21][22]. There are two training methods; supervised and unsupervised.…”

Section: Artificial Neural Network Structurementioning

confidence: 99%

“…In the communication, we discussed the use of the two numerical methods; Gauss-Seidel and Newton-Raphson and the artificial intelligence method neural networks to solve power flow problem [20]. Concerning the journal article, it dealt with three numerical methods; Gauss-Seidel, Newton-Raphson and fast decoupled in addition to the method of neural networks for analyzing the power flow [21]. We succeed to do also two other international communications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Artificial Neural Network Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%