A Multiple-Sensor Fault-Tolerant Control of a Single-Phase Pulse-Width Modulated Rectifier Based on MRAS and GPI Observers

Abstract

Due to their advantages in ensuring low harmonic distortion and high power factors, single-phase Pulse-Width Modulated (PWM) rectifiers are widely employed in several industrial applications. Generally, the conventional control loop of a single-phase PWM rectifier uses both voltage and current sensors. Hence, in case of sensor fault, the performance and the availability of the converter can be seriously compromised. Therefore, diagnosis approaches and fault-tolerant control (FTC) strategies are mandatory to monitor these systems. Accordingly, this paper introduces a novel multiple-sensor FTC scheme for a single-phase PWM rectifier. The proposed fault diagnosis approach relies on joining several Generalized Proportional Integral (GPI) and Model Reference Adaptive System (MRAS) observers with a residual generation technique to detect and isolate sensor faults in a simple and reliable manner. While conventional sensor FTC methods dedicated to PWM rectifiers can only deal with single faults, the suggested approach guarantees a very good effectiveness level of sensor fault detection, isolation (FDI) and FTC of multiple-sensor fault occurrence scenarios. Consequently, the single-phase PWM rectifier can work with only the survivable single sensor with the guarantee of very good performance as in healthy operation mode. The effectiveness of the proposed sensor FDI approach and its control reconfiguration performance are demonstrated through both extensive simulation and experimental results.

Current sensor unanticipated fault detection and compensation of 14.5-meter optical/infrared telescope drive system based on algebraic transformations

“…As per existing literature, [4][5][6][7][8][9][10] adopt the above approach. An example of such work is [4].…”

“…Oumar et al [9] introduced an FTC strategy based on two voltage observers for combating the current sensor faults, which guarantees the continuous operation of double star induction machine (DSIM) and maintains its performance at the desired level. In [10], the proposed fault diagnosis solution relied on joining several generalized proportional integral (GPI) and MRAS observers with a residual generation technique to detect and isolate sensor faults. Once the estimated state values have been obtained through GPI and MRAS observers, they will compare with the measured value to determine if residual generation is present.…”