基于WOA-GRU的风电机组发电机故障预警方法

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关键词：风电机组发电机；SCADA数据；鲸鱼优化算法；门控循环单元;故障预警DOI：10.15938/j. emc. 2025.06.006中图分类号：TM315 文献标志码：A 文章编号：1007-449X（2025）06-0054-09

Fault early warning method of wind turbine generator based on WOA-GRU

XING Zuoxia， MA Yanxi， GUO Shanshan， CHEN Mingyang， LUO Shimao （School of Electrical Engineering，Shenyang University of Technology， Shenyang ，China）

Abstract：In order to realize the early capture of wind turbine generator fault and improve the accuracy of fault warning，a fault warning method of wind turbine generator based on WOA-GRU model was proposed.Firstly，outlier data of wind turbine generator temperature was removed through box plot analysis， and the grey correlation analysis method was applied to extract feature parameters with high correlation with wind turbine generator temperature from high-dimensional SCADA data as model inputs. Subsequently，by using the whale optimization algorithm to optimize the hyperparameter group of the gated recurrent unit neural network，the obtained optimal parameter gated recurrent unit neural network model was used to predict the temperature of the wind turbine generator.Based on setting the alarm threshold through adaptive threshold algorithm，fault warning of wind turbine generators was carried out accordingly. Finally，taking the SCADA data of a wind turbine unit in a domestic wind farm as an example，the WOA-GRU model was compared with BP，ELM，RF，GRU，and LSTM models. The results show that the WOA-GRU model had higher prediction accuracy than other models and could more accurately capture early faults of wind turbine generators.

Keywords： wind turbine generator; SCADA data; whale optimization algorithm； gated recurrent unit ; fault early warning

0引言

风电场恶劣的运行环境导致风电机组具有较高的故障率，其中陆上风机的运维成本约占风电场总收入的 15%～20% ，海上风电运维成本则占到其总收益 25%～35%[1] 。（剩余13094字）