scholarly journals Inter-turn short circuit stator fault identification for induction machines using computational intelligence algorithms

2015 ◽  
Author(s):  
S. A. Ethni ◽  
S. M. Gadoue ◽  
B. Zahawi
Keyword(s):  
Computational Intelligence ◽  
Short Circuit ◽  
Induction Machines ◽  
Fault Identification ◽  
Stator Fault

scholarly journals Model Predictive Controller Utilized as an Observer for Inter-Turn Short Circuit Detection in Induction Machines - Preprint Version

2021 ◽  
Author(s):  
İlker Şahin ◽  
Ozan Keysan
Keyword(s):  
Short Circuit ◽  
Induction Machine ◽  
Main Idea ◽  
Optimization Procedure ◽  
Induction Machines ◽  
Voltage Source ◽  
Non Invasive ◽  
Finite Control ◽  
Predictive Controller ◽  
Stator Fault

<p>In this paper, a novel and non-invasive stator inter-turn short circuit (ITSC) online detection method is presented for an induction machine (IM), driven by a two-level voltage source inverter (2L-VSI) via finite control set model predictive control (FCS-MPC). The main idea of the proposed method is to utilize the controller itself as an observer: under the presence of a fault, the distribution of inverter switching states significantly deviates from the original balanced case. Therefore, by inspecting the inverter switching vectors, which are the outcomes of the FCS-MPC routine's online optimization procedure, a stator fault can be detected efficiently. It is observed that both the zero-vector allocation over the complex plane and the allocation among the active vectors are influenced by the presence of a stator short-circuit fault. The proposed fault detection strategy introduces little to no extra burden for processor and memory. Experimental results verify the proposed method, and inter-turn short circuits of two and three turns are confidently detected and located for a 500 W, two-pole IM.</p>

scholarly journals Model Predictive Controller Utilized as an Observer for Inter-Turn Short Circuit Detection in Induction Machines - Preprint Version

2021 ◽  
Author(s):  
İlker Şahin ◽  
Ozan Keysan
Keyword(s):  
Short Circuit ◽  
Induction Machine ◽  
Main Idea ◽  
Optimization Procedure ◽  
Induction Machines ◽  
Voltage Source ◽  
Non Invasive ◽  
Finite Control ◽  
Predictive Controller ◽  
Stator Fault

<p>In this paper, a novel and non-invasive stator inter-turn short circuit (ITSC) online detection method is presented for an induction machine (IM), driven by a two-level voltage source inverter (2L-VSI) via finite control set model predictive control (FCS-MPC). The main idea of the proposed method is to utilize the controller itself as an observer: under the presence of a fault, the distribution of inverter switching states significantly deviates from the original balanced case. Therefore, by inspecting the inverter switching vectors, which are the outcomes of the FCS-MPC routine's online optimization procedure, a stator fault can be detected efficiently. It is observed that both the zero-vector allocation over the complex plane and the allocation among the active vectors are influenced by the presence of a stator short-circuit fault. The proposed fault detection strategy introduces little to no extra burden for processor and memory. Experimental results verify the proposed method, and inter-turn short circuits of two and three turns are confidently detected and located for a 500 W, two-pole IM.</p>

scholarly journals Wavelet Transform Based Fault Identification and Reconfiguration for a Reduced Switch Multilevel Inverter Fed Induction Motor Drive

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1023
Author(s):  
Arigela Satya Veerendra ◽  
Akeel A. Shah ◽  
Mohd Rusllim Mohamed ◽  
Chavali Punya Sekhar ◽  
Puiki Leung
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Short Circuit ◽  
Open Circuit ◽  
Multilevel Inverter ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
The Novel ◽  
Inverter Topology

The multilevel inverter-based drive system is greatly affected by several faults occurring on switching elements. A faulty switch in the inverter can potentially lead to more losses, extensive downtime and reduced reliability. In this paper, a novel fault identification and reconfiguration process is proposed by using discrete wavelet transform and auxiliary switching cells. Here, the discrete wavelet transform exploits a multiresolution analysis with a feature extraction methodology for fault identification and subsequently for reconfiguration. For increasing the reliability, auxiliary switching cells are integrated to replace faulty cells in a proposed reduced-switch 5-level multilevel inverter topology. The novel reconfiguration scheme compensates open circuit and short circuit faults. The complexity of the proposed system is lower relative to existing methods. This proposed technique effectively identifies and classifies faults using the multiresolution analysis. Furthermore, the measured current and voltage values during fault reconfiguration are close to those under healthy conditions. The performance is verified using the MATLAB/Simulink platform and a hardware model.

Differential evolution applied to line-connected induction motors stator fault identification

2018 ◽  
Vol 23 (21) ◽  
pp. 11217-11226 ◽  
Author(s):  
Jacqueline Jordan Guedes ◽  
Marcelo Favoretto Castoldi ◽  
Alessandro Goedtel ◽  
Cristiano Marcos Agulhari ◽  
Danilo Sipoli Sanches
Keyword(s):  
Differential Evolution ◽  
Induction Motors ◽  
Fault Identification ◽  
Stator Fault

scholarly journals Modeling and Evaluation of Stator and Rotor Faults for Induction Motors

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 133 ◽  
Author(s):  
Jing Tang ◽  
Jie Chen ◽  
Kan Dong ◽  
Yongheng Yang ◽  
Haichen Lv ◽  
...  
Keyword(s):  
Short Circuit ◽  
Induction Machine ◽  
Load Test ◽  
Discrete Models ◽  
Digital Implementation ◽  
Rotor Faults ◽  
Fault Severity ◽  
Three Phase ◽  
Stator Fault ◽  
Circuit Resistance

The modeling of stator and rotor faults is the basis of the development of online monitoring techniques. To obtain reliable stator and rotor fault models, this paper focuses on dynamic modeling of the stator and rotor faults in real-time, which adopts a multiple-coupled-circuit method by using a winding function approach for inductance calculation. Firstly, the model of the induction machine with a healthy cage is introduced, where a rotor mesh that consists of a few rotor loops and an end ring loop is considered. Then, the stator inter-turn fault model is presented by adding an extra branch with short circuit resistance on the fault part of a stator phase winding. The broken rotor bar fault is then detailed by merging and removing the broken-bar-related loops. Finally, the discrete models under healthy and faulty conditions are developed by using the Tustin transformation for digital implementation. Moreover, the stator and rotor mutual inductances are derived as a function of the rotor position according to the turn and winding functions distribution. Simulations and experiments are performed on a 2.2-kW/380-V/50-Hz three-phase and four-pole induction motor to show the performance of the stator and rotor faults, where the saturation effect is considered in simulations by exploiting the measurements of a no load test. The simulation results are in close agreement with the experimental results. Furthermore, magnitudes of the characteristic frequencies of 2f1 in torque and (1 ± 2s)f1 in current are analyzed to evaluate the stator and rotor fault severity. Both indicate that the stator fault severity is related to the short circuit resistance. Further, the number of shorted turns and the number of continuous broken bars determines the rotor fault severity.

Induction Motors Stator Fault Analysis based on Artificial Intelligence

2016 ◽  
Vol 2 (1) ◽  
pp. 69 ◽  
Author(s):  
Hussein Taha Hussein ◽  
Mohamed Ammar ◽  
Mohamed Moustafa Hassan
Keyword(s):  
Artificial Intelligence ◽  
Fault Detection ◽  
Short Circuit ◽  
Fault Analysis ◽  
Fault Detection And Diagnosis ◽  
Neuro Fuzzy ◽  
Detection Approach ◽  
Three Phase ◽  
Detection And Diagnosis ◽  
Stator Fault

This article presents a method for fault detection and diagnosis of stator inter-turn short circuit in three phase induction machines. The technique is based on the stator current and modelling in the dq frame using an Adaptive Neuro-Fuzzy artificial intelligence approach. The developed fault analysis method is illustrated using MATLAB simulations. The obtained results are promising based on the new fault detection approach.

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