Electrical Power System Architectures for Military and Commercial Vehicular Applications

2002 ◽  
Author(s):  
M. A. Masrur ◽  
John Monroe ◽  
Vijay Garg ◽  
Joseph Fodera ◽  
Rakesh Patel
Keyword(s):  
Power System ◽  
Electrical Power ◽  
Electrical Power System ◽  
System Architectures

Nanosatellite electrical power system architectures: Models, simulations, and tests

2020 ◽  
Vol 48 (12) ◽  
pp. 2153-2189
Author(s):  
Leonardo Kessler Slongo ◽  
Sara Vega Martínez ◽  
Bruno Vale Barbosa Eiterer ◽  
Eduardo Augusto Bezerra
Keyword(s):  
Power System ◽  
Electrical Power ◽  
Electrical Power System ◽  
System Architectures

A Comprehensive Review on CubeSat Electrical Power System Architectures

2021 ◽  
pp. 1-1
Author(s):  
Amarendra Edpuganti ◽  
Vinod Khadkikar ◽  
Mohamed Shawky Elmoursi ◽  
Hatem Zeineldin ◽  
Naji Alsayari ◽  
...  
Keyword(s):  
Power System ◽  
Electrical Power ◽  
Electrical Power System ◽  
System Architectures ◽  
Comprehensive Review

Gradient Ascent Optimization for Fault Detection in Electrical Power System Based on Wavelet Transformation

2019 ◽  
Vol 14 ◽  
Author(s):  
Iyappan Murugesan ◽  
Karpagam Sathish
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Power System ◽  
Electrical Power ◽  
Daubechies Wavelet ◽  
Electrical Power System ◽  
Gradient Ascent ◽  
Neural Learning ◽  
Weight Value ◽  
Transmission And Distribution

: 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 TLsignal 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-of-the-art works.

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