Adaptive Neuro-Fuzzy Inference System to Predict the Voltage Magnitudes and Voltage Phase Angles

Fikri, Meriem; Cheddadi, Bouchra; Sabri, Osama

doi:10.1145/3372938.3372942

Cited by 1 publication

(3 citation statements)

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“…The good choice of the ANFIS structure facilitate learning and adaptation [4]. For more details check reference [23].…”

Section: Adaptive Neuro-fuzzy Inference Systems Structurementioning

confidence: 99%

“…ANFIS applies the mechanism of learning neural networks on fuzzy inference techniques. ANFIS uses a two-pass learning cycle: forward propagation and back propagation [23]. During the training process, membership functions parameters are adjusted using the backpropagation learning algorithm, or in combination with another type of algorithms such as the least square to identify the best prediction value.…”

Section: Anfis's Learning Processmentioning

confidence: 99%

“…We succeed to do also two other international communications. One was about predicting Moroccan real network's power flow employing the ANN [22] and the other one was about predicting the voltage magnitudes and voltage phase angles using ANFIS [23]. The main goal of this article is to compare the two intelligent methods Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System using two real electrical networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…The good choice of the ANFIS structure facilitate learning and adaptation [4]. For more details check reference [23].…”

Section: Adaptive Neuro-fuzzy Inference Systems Structurementioning

confidence: 99%

Section: Anfis's Learning Processmentioning

confidence: 99%