Fuzzy Diagnosis of Faults for DFIG used in a Wind Energy Conversion System
Currently, the diagnosis of possible faults in the wind power chain is a major priority for industrialists in particular and scientists in general. In addition to that, wind systems must be able to provide consistent service for a considerable amount of time, even if there is an electrical failure in the grid or any part of the conversion system. This paper is focused on introducing a diagnostic approach based on fuzzy logic. Its core purpose is to enable the continuous monitoring and timely detection of inter- turn short circuits and open-phase circuits occurring within the stator windings of wind turbines that employ double-fed induction generators (DFIG). The proposed approach relies exclusively on phase currents for real-time fault detection and localization in machines that incorporate double-fed induction generators (DFIGs) in wind turbines. This dependency is established through the acquisition of stator currents and the subsequent calculation of their average absolute values. To bring this innovative approach to life, the study leverages the powerful modeling capabilities of MATLAB/SIMULINK, Furthermore, the research presents simulation results to effectively demonstrate how the monitoring approach performs under distinct operating conditions.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.