Hybrid Fault Prognosis for Excitation Capacitors of Self-Excited Induction Generator for Wind Energy Applications

Abstract

This paper presents a new fault prognosis approach applied to wind turbine system based on self-excited induction generator (SEIG) for offshore and isolated areas. This generator is very sensitive to wind speed variation and excitation source. The SEIG is excited by a capacitor bank with an appropriate value to ensure the good operating of the production system. Capacitor bank faults are usually related to chemical aging, electrical and thermal stress conditions. These abnormalities can affect one or more properties of the system, which can lead to failures or even complete breakdown of the production system. Specifically, in this paper, we propose a saturated flux model for the SEIG and develop a hybrid monitoring method that detects faults occurrence gradually and estimates the remaining useful life (RUL). Such monitoring method applies data mining techniques in order to identify and track the faults using only useful data that captures the dynamics of the degradation. Moreover, to deploy efficient maintenance schedules, RUL is estimated by exploiting wind speed (variable and max speed) information. The proposed hybrid fault prognosis method is tested under variable excitation capacitors degradation scenarios. The obtained results confirm the robustness and accuracy of the proposed method.