Izvorni znanstveni članak
https://doi.org/10.30765/er.2779
Radial basis neural network and discrete wavelet transform based RMS estimation for fault assessment in three phase transmission line
Aveek Chattopadhyaya
; Department of Electrical Engineering, Guru Nanak Institute of Technology, Kolkata, India
Suman Ghosh
; Department of Electrical Engineering, Guru Nanak Institute of Technology, Kolkata, India
*
Sumangal Bhaumik
; Department of Electrical Engineering, Abacus Institute of Engineering and Management, Hooghly, India
Surajit Chattopadhyay
; Department of Electrical Engineering, Ghani Khan Choudhury Institute of Engineering and Technology, Malda, India
* Dopisni autor.
Sažetak
Quick identification, accurate classification and proper estimation of location of faults in transmission line are utmost required for smooth and reliable operation of electrical system. This work deals with the line-to-ground (LG) fault assessment in transmission line. At one end three phase current signals have been analyzed by discrete Wavelet transform (DWT) based root-mean-square (rms) analysis for fast detection of faults. Then computed rms values have been used in Radial Basis Neural Network (RBNN) for classification of faulty phase and estimation of fault locations. Using the proposed technique, very quickly fault has been detected and with high accuracy almost 100% fault has been classified and distance of fault properly estimated. In different cases algorithm have been tested successfully and the results are very much optimistic.
Ključne riječi
solar photovoltaic systems; rooftop solar PV; carbon dioxide emissions; sustainable energy advancement; performance metrics; yearly energy output
Hrčak ID:
335596
URI
Datum izdavanja:
16.5.2025.
Posjeta: 723 *