Abstract

Background: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like Artificial Neural Network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the Power Loss Rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TL signal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the 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. DWT-GADNL Technique is measured with PLR, Feature Extraction Accuracy (FEA), and Fault Detection Time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-ofthe- art works.

Materials and Methods: An electric power system incorporates production, broadcast and distribution of electric energy. To send the electric power to massive load centers, transmission lines are exploited. The fast growth of electric power systems results in huge number of lines in operation and total length. TL are susceptible to faults in case of lightning, short circuits, mis-operation, human errors, overload, etc. Faults resulted in tiny to long power outages for customers. To protect the reliable power system operations, Fault identification, isolation and localization are imperative. The voltage lessened to minimal value, when fault occurs on TL. FD is an essential problem in power system engineering to minimize the PLR. DWT-GADNL Technique is introduced for FD in TL during transmission and distribution.

Results: Power Loss due to the fault occurrence during the transmission and distribution is a common problem in electrical power system. To lessen the PLR, the fault is detected in earlier stage. From the sample transmission line, the features are extracted and the values are calculated. When the observed value is lesser than the actual value, the fault is detected through performing the gradient ascent optimization process in transmission line. In this optimization process, the local maxima are identified to reduce the PLR. At different time instances, PLR gets changed. At instance 3, the PLR of proposed DWTGADNL framework is 12% where the PLR of Fuzzy Logic Based Algorithm and Fault Diagnosis Framework are 27% and 19% respectively. Through comparing all the ten instances, PLR is reduced in GWMD-DE technique by 59% and 40% compared to existing respectively.

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.

Keywords: Deep neural learning, electrical power system, fault detection optimization, feature extraction, gradient ascent, normalization.

Graphical Abstract