access icon free Prediction of salt contamination in the rotating blade of wind turbine under lightning strike occurrence using fuzzy c-means and k-means clustering approaches

© The Institution of Engineering and Technology
Received 19/12/2018, Accepted 29/10/2019, Revised 03/10/2019, Published 31/10/2019

This study proposes an alternative methodology for predicting salt contamination in rotating blade of wind turbine under lightning strike using fuzzy c-means (FCM) and k-means (KM) clustering approaches. The salt contamination states of wind turbine blades are classified with four different levels of equivalent salt deposit density (ESDD) classes. The lightning strike experiments are set up for simulating the condition when the blades are struck by lightning with four rotational speeds under various ESDD classes. Then, the absolute peak value of the measured current signals in grounding line using the high-frequency current transformer and the average power are used to represent the input vectors of FCM and KM to predict the class of the salt contamination. The experimental results validated that the proposed approach can effectively classify the measured current signal and accurately predict the ESDD class on lightning strike occurrence.

Inspec keywords: fuzzy set theory; power engineering computing; insulator contamination; blades; wind turbines; pattern clustering

Other keywords: measured current signal; rotating blade; salt contamination states; FCM clustering approach; k-means clustering; rotational speeds; fuzzy c-means; ESDD class; KM clustering approach; equivalent salt deposit density classes; wind turbine blades; lightning strike experiments; ESDD classes; lightning strike occurrence

Subjects: Wind power plants; Combinatorial mathematics; Data handling techniques; Power line supports, insulators and connectors; Combinatorial mathematics; Power engineering computing

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