A comparative study between two open-circuit fault detection and localization techniques in a threephase inverter fed induction motor

2016 ◽  
Author(s):  
Bilal Djamal Eddine Cherif ◽  
Mokhtar Bendjebbar ◽  
Noureddine Benouzza ◽  
H. Boudinar ◽  
Azeddine Bendiabdellah
Keyword(s):  
Fault Detection ◽  
Comparative Study ◽  
Induction Motor ◽  
Open Circuit ◽  
Detection And Localization

scholarly journals Machine Learning as an Efficient Diagnostic Tool for Fault Detection and Localization in Solar Photovoltaic Arrays

10.29007/34bz ◽  
2019 ◽  
Author(s):  
Masoud Alajmi ◽  
Sultan Aljahdali ◽  
Sultan Alsaheel ◽  
Mohammed Fattah ◽  
Mohammed Alshehri
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Fault Detection ◽  
Solar Energy ◽  
Hot Spot ◽  
Low Cost ◽  
Short Circuit ◽  
Renewable Energy Sources ◽  
Open Circuit ◽  
Detection And Localization

Solar energy, one of many types of renewable energy, is considered to be an excellent alternative to non-renewable energy sources. Its popularity is increasing rapidly, especially because fuel energy consumes and depletes finite natural resources, polluting the environment, whereas solar energy is low- cost and clean. To produce a reliable supply of energy, however, solar energy must also be consistent. The energy we derive from a photovoltaic (PV) array is dependent on changeable factors such as sunlight, positioning of the array, covered area, and status of the solar cell. Every change adds potential for the creation of error in the array. Therefore, thorough research and a protocol for fast, efficient location and correction of all kinds of errors must be an urgent priority for researchers.For this project we used machine learning (ML) with voltage and current sensors to detect, localize and classify common faults including open circuit, short circuit, and hot-spot. Using the proposed algorithm, we have improved the accuracy of fault detection, classification and localization to 100%. Further, the proposed method can execute all three tasks (detection, classification, and localization) simultaneously.

scholarly journals Study by modeling and simulation of open-switch fault diagnosis for five-level converters

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Florent Becker ◽  
Ehsan Jamshidpour ◽  
Philippe Poure ◽  
Shahrokh Saadate
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Tolerant ◽  
Fault Localization ◽  
Open Circuit ◽  
System Reconfiguration ◽  
Open Switch ◽  
Diagnosis Method ◽  
Control Orders ◽  
Detection And Localization

In this paper, an open-switch fault diagnosis method for five-level H-Bridge Neutral Point Piloted (HB-NPP) or T-type converters is proposed. While fault tolerant operation is based on three steps (fault detection, fault localization and system reconfiguration), a fast fault diagnosis, including both fault detection and localization, is mandatory to make a suitable response to an open-circuit fault in one of the switches of the converter. Furthermore, fault diagnosis is necessary in embedded and safety critical applications, to prevent further damage and perform continuity of service.In this paper, we present an open-switch fault diagnosis method, based on the switches control orders and the observation of the converter output voltage level. In five-level converters such as HB-NPP and T-type topologies, some switches are mostly 'on' at the same time. Therefore, the fault localization is quite complicated. The fault diagnosis method we proposed is capable to detect and localize an open-switch fault in all cases. Computer simulations are carried out by using Matlab Simulink and SimPowerSystem toolbox to validate the proposed approach.

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