Gradient Ascent Optimization for Fault Detection in Electrical Power Systems based on Wavelet Transformation

Conclusion: DWT-GADNL Technique is introduced for FD during transmission and distribution with minimal PLR. Sample power TL signal is taken and min-max normalization process performs the various rated values estimation of transmission lines. DWT decomposes normalized TL signal to different components for feature extraction with higher accuracy. Gradient Ascent Deep Neural Learning detects the local maximum from extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. The performance of DWT-GADNL technique is tested with the metrics such as PLR, FEA and FDT. With the simulations conducted for all techniques, the proposed DWT-GADNL technique presents better performance on FD during transmission and distribution as evaluated to state-of-the-art works. From simulations results, the DWT-GADNL technique lessens PLR by 50% and enhances FEA by 9% than the existing methods.
Source: Current Signal Transduction Therapy - Category: Molecular Biology Source Type: research