scholarly journals Smart Fault-Tolerant Control System Based on Chaos Theory and Extension Theory for Locating Faults in a Three-Level T-Type Inverter

2019 ◽  
Vol 9 (15) ◽  
pp. 3071 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Long-Yi Chang ◽  
Fu-Qiang Xu
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Chaotic System ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Extension Theory ◽  
Power Transistor ◽  
Three Phase ◽  
Power Switches ◽  
Lorenz Chaotic System

This study proposes a smart fault-tolerant control system based on the theory of Lorenz chaotic system and extension theory for locating faults and executing tolerant control in a three-level T-type inverter. First, the system constantly monitors the fault states of the 12 power transistor switches of the three-level T-type inverter; if a power transistor fails, the corresponding output phase voltage waveform is converted by a Lorenz chaotic system. Chaos eye coordinates are then extracted from a scatter diagram of chaotic dynamic states and considered as fault characteristics. The system then executes fault diagnosis based on extension theory. The fault characteristic value is used as the input signal for correlation analysis; thus, the faulty power transistor can be located and the fault diagnosis can be achieved for the inverter. The fault-tolerant control system can maintain the three-phase balanced output of the three-level T-type inverter, thereby improving the reliability of the motor drive system. The feasibility of the proposed smart fault-tolerant control system was assessed by conducting simulations in this study, and the results verified its feasibility. Accordingly, after the occurrence of the fault in power switches, the balanced three-phase output line voltage remained unchanged, and the quality of the output voltage was not reduced by using the integration of the proposed fault diagnosis system and fault-tolerant control system for a three-level T-type Inverter.

Research on fault tolerant control system based on optimized neural network algorithm

2020 ◽  
Vol 39 (6) ◽  
pp. 9073-9083
Author(s):  
Xianming Shan ◽  
Huixin Liu ◽  
Yefeng Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Fault Diagnosis ◽  
Network Model ◽  
Fault Tolerant ◽  
Rapid Development ◽  
Fault Tolerant Control ◽  
Control Measures ◽  
Depth Sensor

Due to the strict personnel control measures in COVID-19 epidemic, the control system cannot be maintained and managed manually. This puts forward higher requirements for the accuracy of its fault-tolerant performance. The control system plays an increasingly important role in the rapid development of industrial production. When the sensor in the system fails, the system will become unstable. Therefore, it is necessary to accurately and quickly diagnose the faults of the system sensors and maintain the system in time. This paper takes the control system as the object to carry out the fault diagnosis and fault-tolerant control research of its sensors. A network model of wavelet neural network is proposed, and an improved genetic algorithm is used to optimize the weights and thresholds of the neural network model to avoid the deficiencies of traditional neural network algorithms. For the depth sensor of a certain system, an online fault diagnosis scheme based on RBF (Radial Basis Function) neural network and genetic algorithm optimized neural network was designed. The disturbance fault, “stuck” fault, drift fault and oscillation fault of the depth sensor are simulated. Simulation experiments show that both online fault diagnosis schemes can accurately identify sensor faults and the genetic algorithm optimized neural network is superior to RBF neural network in both recognition accuracy and training time under the influence of COVID-19.

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