A novel algorithm for fault diagnosis of induction generators in wind power systems utilizing stator current signal crossing and finite element modeling - Belynda, Fares; Radia, Abdelli; Ahcene, Bouzida; Shoush, Kamel A.; Gedefie, Badmaw Abebe; Ali, Enas; Ghoneim, Sherif S.M. (Results in engineering, 2025)
•Autonomous wind power systems based on self-excited induction generators (SEIG) are widespread.•These generators’ simplicity of design and reliability make them an ideal choice for stand-alone wind power installations.•Diagnosing mechanical or electrical faults as soon as possible is important to ensure that the generator continues to avoid any unplanned downtime.•A real-time monitoring system, which will collect and analyze data to identify potential faults before they cause significant damage, was proposed.•An algorithm to detect and diagnose faults encountered by the self-excited induction generator was presented.•Numerical simulation results have confirmed the effectiveness of the proposed algorithm and the diagnosis method based on stator current signal crossing. Wind power systems are one of the main solutions to the world's energy problems, and their many advantages are making them increasingly popular. They are a renewable, environmentally friendly source of energy. What's more, installing wind turbines in remote or hard-to-reach areas makes it possible to tap previously unexplored wind resources. Autonomous wind power systems based on self-excited induction generators (SEIG) are widespread. These generators' simplicity of design and reliability make them an ideal choice for stand-alone wind power installations. Self-energized induction generators may present one or more mechanical or electrical faults when operating in the wind energy conversion chain. It is critical to diagnose these faults as soon as possible to ensure that the generator continues to operate correctly and avoid any unplanned downtime. In this paper, we propose a real-time monitoring system, which will collect and analyze data to identify potential faults before they cause significant damage. To this end, we have developed an algorithm to detect and diagnose faults encountered by the self-excited induction generator. This algorithm is based on the stator current signal crossing method associated with an in-depth modeling of the generator. Numerical simulation results have confirmed the effectiveness of the proposed algorithm and the diagnosis method based on stator current signal crossing. This approach offers a reliable and accurate solution for detecting and diagnosing potential problems in electrical systems.