Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)
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 divide...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2017
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2456/1/AJ%202019%20%2823%29.pdf |
| Summary: | 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. |
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