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Unexpected faults that occur in the sensors of the single-phase grid-connected converter (SGC) may deteriorate the control performance ofthe grid current and dc-link voltage and pose risks to the connected distributed generation system. This article proposes an unknown input sliding mode observer (UI-SMO) based sensor fault identification and remediation strategy. Therein, an UI-SMO is established that integrates the observations of the grid current and dc-link voltage with the reconstruction of the unknown input (i.e., load condition of the SGC). Such an observer-based analytical redundancy generates the residuals that indicate the abnormalities of the sensors for fault identification. An adaptive fault-tolerant control (AFTC) strategy for the SGC is developed, where an automated control system reconfiguration with unknown input adaptation is seamlessly implemented by substituting the data reported by the faulty sensors with their observed values. Extensive simulation and experimental studies validate the effectiveness of the proposed UI-SMO-based sensor fault identification and AFTC strategy of the SGC. [ABSTRACT FROM AUTHOR]

Single-phase pulse-width modulation (PWM) rectifier is commonly used in a high-speed railway electric traction system. Occurrence of unexpected failure in the sensors of the detection system may lead to feedback values deviation and system degradation, which can be extremely detrimental to the operation safety of the electric locomotive. This paper presents a fast and reliable fault diagnosis and fault resilient control strategy for catenary current and dc-link voltage sensor faults in the control system for an electric traction single-phase PWM rectifier. Sliding mode observers are designed to produce analytical redundancy. In order to avoid unobservable states and fluctuation introduced by discrete dynamics in observer design, a novel description for switching variables is presented in system modeling. Normalized residuals are generated using measured and observed values. The fault diagnosis unit proposed can detect and isolate three types of sensor faults online by comparing residuals with thresholds. System reconfiguration is realized by substituting the observed values for the information of faulty sensors. Simulation and experimental results are demonstrated to validate the effectiveness of the strategy. [ABSTRACT FROM AUTHOR]