A simple open-circuit fault detection method for a fault-tolerant DC/DC converter

2015 ◽  
Author(s):  
John Long Soon ◽  
Dylan Dah-Chuan Lu
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Tolerant ◽  
Open Circuit

scholarly journals Asymmetric Multilevel Inverter Topology and Its Fault Management Strategy for High-Reliability Applications

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4302
Author(s):  
Mohammad Fahad ◽  
Mohd Tariq ◽  
Adil Sarwar ◽  
Mohammad Modabbir ◽  
Mohd Aman Zaid ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Fault Detection ◽  
Detection Method ◽  
Fault Tolerant ◽  
High Reliability ◽  
Open Circuit ◽  
Multilevel Inverter ◽  
Level Control ◽  
Machine Learning Classification ◽  
Inverter Topology

As the applications of power electronic converters increase across multiple domains, so do the associated challenges. With multilevel inverters (MLIs) being one of the key technologies used in renewable systems and electrification, their reliability and fault ride-through capabilities are highly desirable. While using a large number of semiconductor components that are the leading cause of failures in power electronics systems, fault tolerance against switch open-circuit faults is necessary, especially in remote applications with substantial maintenance penalties or safety-critical operation. In this paper, a fault-tolerant asymmetric reduced device count multilevel inverter topology producing an 11-level output under healthy conditions and capable of operating after open-circuit fault in any switch is presented. Nearest-level control (NLC) based Pulse width modulation is implemented and is updated post-fault to continue operation at an acceptable power quality. Reliability analysis of the structure is carried out to assess the benefits of fault tolerance. The topology is compared with various fault-tolerant topologies discussed in the recent literature. Moreover, an artificial intelligence (AI)-based fault detection method is proposed as a machine learning classification problem using decision trees. The fault detection method is successful in detecting fault location with low computational requirements and desirable accuracy.

Fault Detection for an Aileron Actuator under Variable Conditions Based on Bi-Step Neural Network

2015 ◽  
Vol 764-765 ◽  
pp. 740-746
Author(s):  
Hang Yuan ◽  
Chen Lu ◽  
Ze Tao Xiong ◽  
Hong Mei Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Simulation Model ◽  
Working Conditions ◽  
Detection Method ◽  
Fault Tolerant ◽  
Adaptive Threshold ◽  
Residual Error ◽  
The Other ◽  
Aerodynamic Loads

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

Open-Switch Fault-Tolerant Control of Power Converters in a Grid-Connected Photovoltaic System

2016 ◽  
Vol 7 (4) ◽  
pp. 1294 ◽  
Author(s):  
Bilal Boudjellal ◽  
Tarak Benslimane
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Mean Value ◽  
Boost Converter ◽  
Photovoltaic System ◽  
Current Form ◽  
Fault Indicator ◽  
Open Switch

This paper presents the study of an open switch fault tolerant control of a grid-connected photovoltaic system. The studied system is based on the classical DC–DC boost converter and a bidirectional 6-pulse DC–AC converter. The objective is to provide an open-switch fault detection method and fault-tolerant control for both of boost converter and grid-side converter (GSC) in a grid-connected photovoltaic system. A fast fault detection method and a reliable fault-tolerant topology are required to ensure continuity of service, and achieve a faster corrective maintenance. In this work, the mean value of the error voltages is used as fault indicator for the GSC, while, for the boost converter the inductor current form is used as fault indicator. The fault-tolerant topology was achieved by adding one redundant switch to the boost converter, and by adding one redundant leg to the GSC. The results of the fault tolerant control are presented and discussed to validate the proposed approach under different scenarios and different solar irradiances.

Open-circuit fault detection method for Grid-side Three-level NPC Inverter

2021 ◽  
Author(s):  
Aimad Alili ◽  
Ahmed Al Ameri ◽  
M. B. Camara ◽  
Brayima Dakyo
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Open Circuit ◽  
Grid Side

Research on Fault Detection Method of Converters

2012 ◽  
Vol 229-231 ◽  
pp. 1150-1153
Author(s):  
Wen Zhong Ma ◽  
Ke Cheng Chen ◽  
Yang Shan ◽  
Yan Li Wang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Location ◽  
Detection Method ◽  
Fault Tolerant ◽  
Experimental Results ◽  
The Neural Network ◽  
Operation Control ◽  
Fault Information

The influence of converter faults to the system is introduced and the fault detection method based on wavelet transform and neural network is proposed in this paper. The fault information can be decomposed by wavelet transform, then the fault eigenvectors can be extracted and put into neural network for training and testing. Finally the neural network outputs specific codes, and thus the fault location and fault components of converters are confirmed, which lays the foundation for the fault-tolerant operation control of converters. Simulation and experimental results show the correctness and effectiveness of the method.

Fault-tolerant relative navigation based on Kullback–Leibler divergence

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097912
Author(s):  
Jun Xiong ◽  
Joon Wayn Cheong ◽  
Zhi Xiong ◽  
Andrew G Dempster ◽  
Shiwei Tian ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Detection Method ◽  
Fault Tolerant ◽  
Relative Navigation ◽  
Fusion Procedure ◽  
Leibler Divergence ◽  
F Distribution ◽  
Hybrid Distribution ◽  
Exclusion Method

A fault-detection method for relative navigation based on Kullback–Leibler divergence (KLD) is proposed. Different from the traditional χ 2-based approaches, the KLD for a filter is following a hybrid distribution that combines χ 2 distribution and F-distribution. Using extended Kalman filter (EKF) as the estimator, the distance between the priori and posteriori data of EKF is calculated to detect the abnormal measurements. After fault detection step, a fault exclusion method is applied to remove the error observations from the fusion procedure. The proposed method is suitable for the Kalman filter-based multisensor relative navigation system. Simulation and experimental results show that the proposed method can detect the abnormal measurement successfully, and its positioning accuracy after fault detection and exclusion outperforms the traditional χ 2-based method.

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