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Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (ANFIS) and probabilistic neural network (PNN)

Mohamad Nor, Ahmad Fateh and Sulaiman, Marizan and Abdul Kadir, Aida Fazliana and Omar, Rosli (2017) Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (ANFIS) and probabilistic neural network (PNN). ARPN Journal of Engineering and Applied Sciences, 12 (5). pp. 1406-1412. ISSN 18196608

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Abstract

This paper presents the application of neural networks for analysing voltage stability of load buses in electric power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analysing voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power system. All of the neural network-based models used in this research is developed using MATLAB.

Item Type: Article
Uncontrolled Keywords: Voltage stability analysis; voltage and load power margin; artificial neural network; probabilistic neural network; ANFIS
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001-1841 Production of electric energy or power. Powerplants. Central stations
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Power Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 30 Sep 2019 02:58
Last Modified: 30 Sep 2019 02:58
URI: http://eprints.uthm.edu.my/id/eprint/11532
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