Details

Autor(en) / Beteiligte

• Souza, Ruhan Pontes Policarpo de
• Agulhari, Cristiano Marcos
• Goedtel, Alessandro
• Castoldi, Marcelo Favoretto

Titel

Inter-turn short-circuit fault diagnosis using robust adaptive parameter estimation

Ist Teil von

• International journal of electrical power & energy systems, 2022-07, Vol.139, p.107999, Article 107999

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• This paper presents a robust adaptive parameter estimation procedure, applied to stator inter-turn short-circuit (ITSC) fault detection in induction motors (IM). Using a fault state space model, it is proposed the application of an adaptive parameter estimation methodology, used to identify a parameter related to the failure severity. A linear parameter varying (LPV) robust H∞ filtering technique, obtained by linear matrix inequalities (LMI), is applied to estimate the unavailable states, being the proposed method robust to external disturbances, and also adequate to online applications. The proposed framework is validated through simulations and experimental tests — conducted in a modified IM that allows the emulation of ITSC faults. Tests are performed under several operational conditions, including different levels of fault severity, load torque and voltage unbalance, demonstrating the validity of the ITSC fault detection approach. •A non-invasive inter-turn short-circuit detection in induction motors is developed.•A new state-space model representation, affine to fault parameters is introduced.•A robust LPV filtering technique is applied to obtain motor states under disturbance.•A new parameter set is introduced, representing fault level and its evolution.•Fault severity under voltage unbalance and several torque levels is obtained.