Fault detection method for phase-to-ground faults in three-phase inverter applications

In this study, a low-voltage three-phase inverter was used alongside a shunt resistor to measure the current. However, it is known that this type of inverter and shunt resistor system has a region where the measurement of current is impossible due to structural limitations. As a result, many studies have focused on this region through the use of additional algorithms. Most studies measured current by forcibly adjusting the PWM duty in order to measure the current at the region where it could not be sensed. However, unfortunately, the total harmonic distortion (THD) increases in the current due to PWM adjustment. This causes an increase in torque ripple and inverter control instability. Therefore, in this paper, current was measured using the Rds(on) value between the drain source resistor when MOSFET was turned on and the Kalman filter in a low-voltage three-phase inverter with a single shunt. Additionally, the value was verified via comparison with the values achieved when a Hall-type current sensor and single shunt were used. As a result, this study confirmed that the inverter with a single shunt performs the same as a Hall-type sensor at the region where current cannot be detected.

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Fault Detection and Diagnosis System for a Three-Phase Inverter Using a DWT-Based Artificial Neural Network

International Journal of Fuzzy Logic and Intelligent Systems ◽

10.5391/ijfis.2016.16.4.238 ◽

2016 ◽

Vol 16 (4) ◽

pp. 238-245 ◽

Cited By ~ 7

Author(s):

Ali Rohan ◽

Sung Ho Kim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Diagnosis System ◽

Phase Inverter ◽

Three Phase ◽

Artificial Neural ◽

Detection And Diagnosis ◽

Three Phase Inverter

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Neural Network Based Fault Detection and Diagnosis System for Three-Phase Inverter in Variable Speed Drive with Induction Motor

Journal of Control Science and Engineering ◽

10.1155/2016/1286318 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Furqan Asghar ◽

Muhammad Talha ◽

Sung Ho Kim

Keyword(s):

Neural Network ◽

Fault Detection ◽

Induction Motor ◽

Fault Detection And Diagnosis ◽

Diagnosis System ◽

Multiple Faults ◽

Phase Inverter ◽

Three Phase ◽

Detection And Diagnosis ◽

Three Phase Inverter

Recently, electrical drives generally associate inverter and induction machine. Therefore, inverter must be taken into consideration along with induction motor in order to provide a relevant and efficient diagnosis of these systems. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduces overall efficiency. In this paper, fault detection and diagnosis based on features extraction and neural network technique for three-phase inverter is presented. Basic purpose of this fault detection and diagnosis system is to detect single or multiple faults efficiently. Several features are extracted from the Clarke transformed output current and used in neural network as input for fault detection and diagnosis. Hence, some simulation study as well as hardware implementation and experimentation is carried out to verify the feasibility of the proposed scheme. Results show that the designed system not only detects faults easily, but also can effectively differentiate between multiple faults. These results prove the credibility and show the satisfactory performance of designed system. Results prove the supremacy of designed system over previous feature extraction fault systems as it can detect and diagnose faults in a single cycle as compared to previous multicycles detection with high accuracy.

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