Publication

Robust fault diagnosis of wind turbines based on MANFIS and zonotopic observers

Journal Article (2024)

Journal

Expert Systems with Applications

Pages

121095

Volume

235

Doc link

https://doi.org/10.1016/j.eswa.2023.121095

File

Download the digital copy of the doc pdf document

Authors

Abstract

Wind turbines have become one of the essential sources of energy generation due to their contribution to energy security, economic development, job creation, and technological innovation. This work proposes a methodology for designing robust fault diagnosis systems based on a bank of zonotopic state estimators built upon Takagi–Sugeno (TS) models. The TS models with associated parametric uncertainty are obtained using a Multiple Output Adaptive Neuro-fuzzy Inference System (MANFIS), an extended and improved version of single-input, single-output ANFIS. Its main difference is its multi-output architecture, which allows generalized weighting functions to be obtained, reducing training times, uncertainties estimation, and reduced complexity. As a result, a set of Linear Matrix Inequalities is obtained with the criterion to adjust the parameter of the zonotopic estimator considering the modeling uncertainty. Overall, the work contributes to improving the safety of WT through diagnostic methods that improve its operability. A well-known certified reference case study of a wind turbine system is considered to validate the proposed method.

Categories

control theory.

Scientific reference

E.d. Pérez, V. Puig, F.R. Lopez, G. Valencia, I. Santos-Ruiz and G.L. Osorio-Gordillo. Robust fault diagnosis of wind turbines based on MANFIS and zonotopic observers. Expert Systems with Applications, 235: 121095, 2024.