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Asumadu, Johnson
Dept. of Electrical and Computer Engineering, Western Michigan University
Electrical & Electronics Engineering

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Wavelet Analysis and Radial Basis Function Neural Network Based Stability Status Prediction Scheme Frimpong, Emmanuel Asuming; Okyere, Philip Yaw; Asumadu, Johnson
JURNAL NASIONAL TEKNIK ELEKTRO Vol 7, No 3: November 2018
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (701.144 KB) | DOI: 10.25077/jnte.v7n3.559.2018

Abstract

This paper presents a technique for predicting the transient stability status of a power system. Bus voltages of system generators are used as input parameter. The bus voltages are processed using wavelet transform. Daubechies 8 mother wavelet is employed to extract wavelet entropy of detail 1 coefficients. The sum of wavelet entropies is used as input to a trained radial basis function neural network which predicts the transient stability status. The IEEE 39-bus test system was used to validate the effectiveness and applicability of the technique. The technique is simple to apply and can be implemented in real-time. The prediction accuracy was found to be 86.5% for 200 test cases. Keywords : Radial basis function, Transient analysis and Wavelet transform
Co-Authors Frimpong, Emmanuel Asuming Okyere, Philip Yaw