Wind turbine doubly fed induction generator rotor electrical asymmetry detection based on an adaptive least mean squares filtering of wavelet transform. Issue 2 (April 2021)

• Record Type: Journal Article
• Authors: Dameshghi, Adel; Refan, Mohammad Hossein; Amiri, Parviz
• Is Part Of: Wind engineering. Volume 45:Issue 2(2021)
• Page Start: 138
• Page End: 159
• Publication Date: 2021-04
• Subjects: Noise cancelation -- adaptive filter -- least mean squares -- wavelet transform -- rotor electrical asymmetry; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X19877525
• Languages: English
• ISSNs: 0309-524X

Abstract

A new strategy for the detection of rotor electrical asymmetry in wind turbine doubly fed induction generators is presented. In comparison with the previous work, the major novelty of this article is the addition of an adaptive noise cancelation block in the process of detecting the rotor winding fault. This work demonstrates an improvement in the signal-to-noise ratio using robust least mean squares filtering of the wavelet transform method. In the new strategy, robust least mean squares filtering of wavelet transform is used for the first block of fault detection before the fault estimation block. The proposed strategy is verified with the experimental data measured on doubly fed induction generator test rigs and real wind turbine. A comparative study is carried out based on experimental measurements (stator current and power output) under the steady-state and variable-speed operation representative of real wind turbine operations for both healthy and faulty generator conditions.