DYNAMIC MATRIX AND FEATURE SIMILARITY-BASED AUTO ASSOCIATIVE KERNEL REGRESSION FOR CONDITION MONITORING OF WIND TURBINE

Tian Wenwen; Lyu Lixia; Liu Changliang; Liu Shuai

doi:10.19912/j.0254-0096.tynxb.2023-0942

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (10) : 536-543. DOI: 10.19912/j.0254-0096.tynxb.2023-0942

DYNAMIC MATRIX AND FEATURE SIMILARITY-BASED AUTO ASSOCIATIVE KERNEL REGRESSION FOR CONDITION MONITORING OF WIND TURBINE

Tian Wenwen¹, Lyu Lixia², Liu Changliang^1,3, Liu Shuai^2,3

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Abstract

A dynamic matrix and feature similarity-based Auto Associative Kernel Regression （DM-FS-AAKR） for condition monitoring of wind turbine is proposed to address the problems of high redundancy in the memory matrix selected by the traditional auto associative kernel regression model, inability to update in real-time based on online data, and failure to consider inconsistent feature parameter weights when calculating similarity. Firstly, based on the distance between samples, the original dataset is de-redundant to reduce computational complexity and form a dataset to be selected; Secondly, based on the k-nearest neighbor algorithm, the dynamic matrix is constructed by selecting the historical data that best meets the current operating conditions. To overcome the bias pollution of bad parameters during similarity calculation, a feature similarity calculation method is proposed to assign corresponding weights to different parameters to further improve prediction accuracy. Finally, taking the SCADA data of a wind farm in Hebei as an example, simulation verification experiments were conducted on the operating conditions before the unit malfunctions and shuts down. The results show that compared to the traditional AAKR model, the proposed algorithm reduces the average absolute error by about 15.6%, and the fault warning can be achieved 35 days in advance, with high accuracy and real-time performance.

Key words

gearbox / wind turbines / condition monitoring / auto associative kernel regression（AAKR） / dynamic matrix / feature similarity

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Tian Wenwen, Lyu Lixia, Liu Changliang, Liu Shuai. DYNAMIC MATRIX AND FEATURE SIMILARITY-BASED AUTO ASSOCIATIVE KERNEL REGRESSION FOR CONDITION MONITORING OF WIND TURBINE[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 536-543 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0942

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