Fast fault detection and location for a marine power system using system power converters and active impedance estimation

2008 ◽  
Author(s):  
J. Wang ◽  
M. Sumner ◽  
D.W.P. Thomas ◽  
R.D. Geertsma
Keyword(s):  
Fault Detection ◽  
Power System ◽  
Power Converters ◽  
Active Impedance ◽  
System Power

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.

Multi-function, Bi-Directional and Compact Power Converters for Aircraft Power System Applications

2002 ◽  
Author(s):  
Jie Chang ◽  
Tom Sun ◽  
Anhua Wang ◽  
Jiajia Zhang
Keyword(s):  
Power System ◽  
Power Converters

scholarly journals Design of Optimal Power System Stabilizer Using ETAP

2012 ◽  
pp. 221-224
Author(s):  
Raja Nivedha. R ◽  
Sreevidya. L ◽  
V. Geetha ◽  
R. Deepa
Keyword(s):  
Power System ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Transient Behavior ◽  
Steel Plant ◽  
Power System Stabilizer ◽  
Optimal Power ◽  
Clearing Time ◽  
System Stabilizer ◽  
System Power

The main objective of this paper is to improve the critical clearing time of the Steel Plant 35 MW Turbo generator. In order to enhance the transient behavior of the system, Power System Stabilizer is added so that proper damping is done. Damping intra area and inter area oscillations are critical to optimal power flow and stability on a system. Power system stabilizer is an effective damping device, as they provide auxiliary control signals to the excitation system of the generator. Transient stability analysis was carried out for the Steel plant. The three phase to ground and line to ground fault was simulated. The critical clearing time was found to be more when Power System Stabilizer was added and when Power System Stabilizer was not added the critical clearing time has considerably reduced.

scholarly journals Approach to Stochastic Modeling of Power Systems

2010 ◽  
Vol 26 (1) ◽  
pp. 37-40 ◽  
Author(s):  
Alexander Rubtsov
Keyword(s):  
Power Systems ◽  
Power System ◽  
Stochastic Modeling ◽  
Transfer Functions ◽  
Dynamic Properties ◽  
Stochastic Disturbance ◽  
Modeling Power ◽  
Linearized Model ◽  
System Power ◽  
Made In

Approach to Stochastic Modeling of Power SystemsThis paper presents an approach to modeling power system that contains sources of stochastic disturbance. It is based on frequency analysis of linearized model of power system. Power system dynamic properties are accounted by equivalent transfer functions of machines and their control equipment. This will allow more accurate calculations for different analysis tasks. Methodology of system linearization is proposed and results of linearized model test are delivered.The research was made in frame of a project with funding participation of the European Commission.

Fault Detection and Classification in Power System using Machine Learning

2021 ◽  
Author(s):  
Manojna ◽  
Sridhar H. S ◽  
Nikhil Nikhil ◽  
Anand Kumar ◽  
Pratyay Amrit
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Power System ◽  
Fault Detection And Classification
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