Titolo della tesi: Open Circuit and Current Sensor Fault Diagnosis, Localization and Compensation of DC Converters and AC Machines Drives
This thesis consolidates innovative methodologies in fault diagnosis and fault-tolerant control within renewable energy systems, focusing particularly on interleaved DC-DC converters and AC motor drive systems. Given the critical nature of maintaining system integrity and optimizing operations in renewable energy applications, this research introduces three distinct methodologies to advance fault diagnosis capabilities.
The first study proposes an open-switch fault diagnosis and current sensor fault-tolerant control for a DC-DC interleaved boost converter using a \gls{gpio}. This method designs an observer model to estimate and compensate for current sensor faults within the closed-loop control, allowing for effective fault diagnosis under faulty current measurements. The robustness and effectiveness of this approach are validated through simulation and experimental results, demonstrating its capability to maintain operation even with the faulted current sensors.
The second approach investigates the challenges due to model mismatch, external disturbances, and load fluctuations in traditional \gls{mpc} systems. It introduces a state observer-based \gls{mfpc} technique, employing two types of state observers: a \gls{pio} and a proposed \gls{smo}. This approach enhances the robustness and dynamic performance of a three-stage DC-DC converter by effectively rejecting model disturbances and assessing control resilience under adverse conditions. Simulation studies confirm the superior performance and resilience of the MFPC over conventional MPC methods.
The third study delves into motor drive systems, where it develops a novel methodology to promptly diagnose and localize semiconductor open switch faults. Utilizing a signal-based approach, this study introduces a power-based diagnostic algorithm to identify and localize $27$ different types of open-switch faults in power converters driving synchronous reluctance motor drives. The fault detection process is remarkably swift, diagnosing faults in less than $2 ms$, which corresponds to $2\%$ of the motor’s current fundamental period. Simulations and experiments validate the technique’s efficacy in enhancing the reliability of motor drive systems.
These findings provide substantial advancements in fault diagnosis techniques for renewable energy systems, emphasizing the integration of innovative observer models and diagnostic algorithms to enhance the fault tolerance and reliability of power converters and motor drives.