Fault Diagnosis and Tolerant Control of Power Converters in Renewable Energy Systems Using Observer-Based Estimation and Model Predictive Control

This project aims to make power electronic converters in renewable‑energy systems more reliable and stable. Converters can suffer serious problems, such as an open‑circuit in a semiconductor switch or incorrect sensor readings. These faults can lower performance or cause total failure. To combat this, we propose a complete real‑time fault‑diagnosis and fault‑tolerant control (FTC) framework. First, a robust observer estimates the converter’s internal states and produces residual signals. Then, a threshold‑based decision rule examines those residuals to detect and isolate faults in switches or sensors. After a fault is isolated, a Model Predictive Control (MPC) algorithm adjusts the control actions so the converter keeps running smoothly despite the fault. We

test the combined fault‑detection‑isolation (FDI) and FTC approach with many simulation cases. We evaluate fault‑detection accuracy, detection delay, false‑alarm rate, and the system’s response after a fault to show that the observer‑MPC design can keep power delivery continuous.

Message: Hi FE, I need help with “Fault Diagnosis and Tolerant Control of Power Converters in Renewable Energy Systems Using Observer-Based Estimation and Model Predictive Control” in “Electrical Engineering”