基于注意力机制和RCN-BiLSTM融合的风电机组故障识别

doi:10.11930/j.issn.1004-9649.202412081

摘要/Abstract

摘要：

为提高风电机组故障识别准确率，提出一种基于注意力机制（attention mechanism，AM）和残差胶囊网络（residual capsule network，RCN）与双向长短期记忆（bidirectional long short-term memory，BiLSTM）网络融合的RCN-BiLSTM-Attention风电机组故障识别方法。首先，风电机组监控与数据采集（supervisory control and data acquisition，SCADA）系统中的异常值通过基于密度的有噪声空间聚类（density-based spatial clustering of applications with noise，DBSCAN）算法剔除；然后，通过RCN提取故障数据中的空间关系特征，采用BiLSTM网络动态捕获RCN所提取空间特征的层次时间依赖关系，得到多种故障时序信息，并融合AM对BiLSTM的输出赋予不同权重，以提高风电机组故障识别准确性；最后，通过多个风电机组SCADA数据进行验证，所提模型具有较高的识别准确率和泛化能力。

关键词: 风电机组, 故障识别, 双向长短期记忆网络

Abstract:

To improve the identification accuracy of wind turbine faults, an RCN-BiLSTM-Attention wind turbine fault identification method based on the attention mechanism (AM) and the fusion of residual capsule network (RCN) and bidirectional long short-term memory (BiLSTM) network is proposed. First, the abnormal values in the wind turbine supervisory control and data acquisition (SCADA) system are eliminated by the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Then, RCN is employed to extract spatial relational features from the fault data, the BiLSTM is performed to dynamically capture the hierarchical temporal dependencies of the spatial features extracted by RCN to obtain the temporal information of multiple faults, and fusion AM assigns different weights to the outputs of BiLSTM to enhance the accuracy of wind turbine fault identification. Finally, the SCADA data of several wind turbines are used to verify that the proposed model provides high identification accuracy and generalization ability compared with other models.

Key words: wind turbine, fault identification, bidirectional long short-term memory

陈小乾, 尹亮, 展宗辉, 王放, 李旭涛. 基于注意力机制和RCN-BiLSTM融合的风电机组故障识别[J]. 中国电力, 2025, 58(8): 94-102.

CHEN Xiaoqian, YIN Liang, ZHAN Zonghui, WANG Fang, LI Xutao. Fault Identification for Wind Turbine Based on Attention Mechanism and RCN-BiLSTM Fusion[J]. Electric Power, 2025, 58(8): 94-102.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202412081

https://www.electricpower.com.cn/CN/Y2025/V58/I8/94

图/表 10

图 1 胶囊网络模型

Fig.1 Residual capsule network model

图 2 LSTM网络结构

Fig.2 LSTM network diagram

图 3 BiLSTM网络结构

Fig.3 BiLSTM network diagram

图 4 RCN-BiLSTM-Attention架构

Fig.4 RCN-BiLSTM-Attnetion architecture

图 5 注意力机制结构

Fig.5 Attention mechanism structure

图 6 基于RCN-BiLSTM-Attention故障识别流程

Fig.6 Fault identification process based on RCN-BiLSTM-Attention

图 7 原始风功率数据曲线

Fig.7 Original wind power data curve

图 8 经处理后的风功率数据曲线

Fig.8 Processed wind power data curve

图 9 RCN-BiLSTM-Attention模型收敛曲线

Fig.9 The convergence curve of the RCN-BiLSTM-Attention model

表 1 不同模型性能对比

Table 1 Performance comparison of different models 单位：%

模型	平均准确率	平均精确率	平均召回率	平均 F₁值
DT	85.71	70.90	71.43	71.16
LSTM	86.46	72.06	73.68	72.86
BiLSTM	88.68	77.27	76.69	76.98
CNN-BiLSTM	93.88	86.23	89.47	87.82
RCN-BiLSTM	95.73	91.67	90.98	91.32
RCN-BiLSTM- Attention	97.77	94.81	96.24	95.52

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