Intermittent wiring fault detection and diagnosis for SSPC based aircraft power distribution system

2012 ◽  
Author(s):  
A. Yaramasu ◽  
Y. Cao ◽  
G. Liu ◽  
B. Wu
Keyword(s):  
Fault Detection ◽  
Power Distribution ◽  
Distribution System ◽  
Fault Detection And Diagnosis ◽  
Power Distribution System ◽  
Detection And Diagnosis

scholarly journals Multi-Task Logistic Low-Ranked Dirty Model for Fault Detection in Power Distribution System

2020 ◽  
Vol 11 (1) ◽  
pp. 786-796
Author(s):  
Mostafa Gilanifar ◽  
Jose Cordova ◽  
Hui Wang ◽  
Matthias Stifter ◽  
Eren E. Ozguven ◽  
...  
Keyword(s):  
Fault Detection ◽  
Power Distribution ◽  
Distribution System ◽  
Power Distribution System

scholarly journals Intermittent Arc Fault Detection And Location For Aircraft Power Distribution System

2021 ◽  
Author(s):  
Anil Yaramasu
Keyword(s):  
Fault Detection ◽  
Power Systems ◽  
Power Distribution ◽  
Distribution System ◽  
Fault Location ◽  
Diagnostic Methods ◽  
Power Distribution System ◽  
Matrix Modeling ◽  
Non Destructive ◽  
Load Circuit

This thesis addresses a non-destructive diagnostic method for intermittent arc fault detection and location. Intermittent arc faults appear in aircraft power systems in unpredictable manners when the degraded wires are wet, vibrating against metal structures, or under mechanical stresses, etc. They could evolve into serious faults that may cause on-board fires, power interruptions, system damage and catastrophic incidents, and thus have raised much concern in recent years. Recent trends in solid state power controllers (SSPCs) motivated the development of non-destructive diagnostic methods for health monitoring of aircraft wiring. In this thesis, the ABCD matrix (or transmission matrix) modeling method is introduced to derive normal and faulty load circuit models with better accuracy and reduced complexity compared to the conventional differential equation approach, and an intermittent arc fault detection method is proposed based on temporary deviations of load circuit model coefficients and wiring parameters. Furthermore, based on the faulty wiring model, a genetic algorithm (GA) is proposed to estimate the fault-related wiring parameters, such as intermittent arc location and average intermittent arc resistance. The proposed method can be applied to both the alternating current (AC) power distribution system (PDS) and direct current (DC) PDS. Simulations and experiments using a DC power source have been conducted, and the results have demonstrated effectiveness of the proposed method by estimating the fault location with an accuracy of +/- 0.5 meters on 24.6 meters wire. Unlike the existing techniques which generally requires special devices, the proposed method only needs circuit voltage and current measurement at the source end as inputs, and is thus suitable for SSPC-based aircraft PDS.

scholarly journals A Method of DC Arc Detection in All-Electric Aircraft

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4190
Author(s):  
Teng Li ◽  
Zhijie Jiao ◽  
Lina Wang ◽  
Yong Mu
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Power Distribution ◽  
Distribution System ◽  
Detection Method ◽  
Discrete Wavelet ◽  
Power Distribution System ◽  
Time Frequency ◽  
Intelligent Recognition ◽  
Dc Arc

Arc faults in an aircraft’s power distribution system (PDS) often leads to cable and equipment damage, which seriously threatens the personal safety of the passengers and pilots. An accurate and real-time arc fault detection method is needed for the Solid-State Power Controller (SSPC), which is a key protection equipment in a PDS. In this paper, a new arc detection method is proposed based on the improved LeNet5 Convolutional Neural Network (CNN) model after a Time–Frequency Analysis (TFA) of the DC currents was obtained, which makes the arc detection more real-time. The CNN is proposed to detect the DC arc fault for its advantage in recognizing more time–frequency joint details in the signals; the new structure also combines the adaptive and multidimensional advantages of the TFA and image intelligent recognition. It is confirmed by experimental data that the combined TFA–CNN can distinguish arc faults accurately when the whole training database has been repeatedly trained 3 to 5 times. For the TFA, two kinds of methods were compared, the Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT). The results show that DWT is more suitable for DC arc fault detection. The experimental results demonstrated the effectiveness of the proposed method.

scholarly journals Series Arc Fault Detection of Indoor Power Distribution System Based on LVQ-NN and PSO-SVM

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 184020-184028 ◽  
Author(s):  
Na Qu ◽  
Jiankai Zuo ◽  
Jiatong Chen ◽  
Zhongzhi Li
Keyword(s):  
Fault Detection ◽  
Power Distribution ◽  
Distribution System ◽  
Power Distribution System

scholarly journals Intermittent Arc Fault Detection And Location For Aircraft Power Distribution System

2021 ◽  
Author(s):  
Anil Yaramasu
Keyword(s):  
Fault Detection ◽  
Power Systems ◽  
Power Distribution ◽  
Distribution System ◽  
Fault Location ◽  
Diagnostic Methods ◽  
Power Distribution System ◽  
Matrix Modeling ◽  
Non Destructive ◽  
Load Circuit

This thesis addresses a non-destructive diagnostic method for intermittent arc fault detection and location. Intermittent arc faults appear in aircraft power systems in unpredictable manners when the degraded wires are wet, vibrating against metal structures, or under mechanical stresses, etc. They could evolve into serious faults that may cause on-board fires, power interruptions, system damage and catastrophic incidents, and thus have raised much concern in recent years. Recent trends in solid state power controllers (SSPCs) motivated the development of non-destructive diagnostic methods for health monitoring of aircraft wiring. In this thesis, the ABCD matrix (or transmission matrix) modeling method is introduced to derive normal and faulty load circuit models with better accuracy and reduced complexity compared to the conventional differential equation approach, and an intermittent arc fault detection method is proposed based on temporary deviations of load circuit model coefficients and wiring parameters. Furthermore, based on the faulty wiring model, a genetic algorithm (GA) is proposed to estimate the fault-related wiring parameters, such as intermittent arc location and average intermittent arc resistance. The proposed method can be applied to both the alternating current (AC) power distribution system (PDS) and direct current (DC) PDS. Simulations and experiments using a DC power source have been conducted, and the results have demonstrated effectiveness of the proposed method by estimating the fault location with an accuracy of +/- 0.5 meters on 24.6 meters wire. Unlike the existing techniques which generally requires special devices, the proposed method only needs circuit voltage and current measurement at the source end as inputs, and is thus suitable for SSPC-based aircraft PDS.

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