• Bommidi Sujatha
• Dr. A. Lakshmi Devi

Classification and location of faults are the most challenging jobs in power system networks. This paper explores the fault location and its classification in a practical 5 bus IEEE 9 Bus by using PMU data. Bus associated with the fault, and location of this fault in a branch is computed using wavelet analysis. Radial Basis Function Neural Networks (RBFNN) technique is used as a hybrid model, from wavelet energy entropy (WEE), it applies Daubechies (Db4) where training and testing data were taken from the wavelet coefficients. From the 5 Bus power system and IEEE 9 Bus transmission system, analyzed and computed real time Phasor Measurement Unit (PMU) data are trained and tested using artificial neural networks for determining fault classification, fault detection and fault inception angle.

PMU DATA BASED FAULT DETECTION TECHNIQUE USING A RBFNN

"PMU DATA BASED FAULT DETECTION TECHNIQUE USING A RBFNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.f479-f486, November-2022, Available :http://www.jetir.org/papers/JETIR2211561.pdf

"PMU DATA BASED FAULT DETECTION TECHNIQUE USING A RBFNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppf479-f486, November-2022, Available at : http://www.jetir.org/papers/JETIR2211561.pdf