Voltage Stability Analysis Of Load Buses In Electric Power System Using Adaptive Neuro-Fuzzy Inference System (Anfis) And Probabilistic Neural Network (Pnn)

• Main Authors: Mohamad Nor, Ahmad Fateh, Sulaiman , Marizan, Abdul Kadir, Aida Fazliana, Omar, Rosli
• Format: Article
• Language: English
• Published: Asian Research Publishing Network (ARPN) 2017
• Subjects: Q Science (General); QA Mathematics
• Online Access: http://eprints.utem.edu.my/id/eprint/18656/2/marizan_57.pdf; http://eprints.utem.edu.my/id/eprint/18656/; http://www.arpnjournals.org/jeas/research_papers/rp_2017/jeas_0317_5775.pdf

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 analyzing 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.