基于空洞因果卷积网络的风电机组异常检测

WIND TURBINE ANOMALY DETECTION BASED ON DILATED CAUSAL CONVOLUTION NETWORK

准确可靠的异常检测对于保障风电机组安全高效运行尤为重要。然而,由于风电机组内部结构复杂,运行工况复杂多变,导致所获取的数据采集与监控(SCADA)系统数据往往呈现出复杂的非线性和关联耦合特性。为了更加有效地捕获不同传感器变量之间的空间相关性,提出基于空洞因果卷积网络的风电机组异常检测方法,并采用Focal Loss改进损失函数解决了数据不平衡问题对模型性能的影响。该方法可通过不同的感受野大小从多尺度角度提取丰富的空间相关特征,有效建模并挖掘不同传感器数据间存在的空间因果关系。同时,该模型提供了一种端到端的异常检测方案,可直接从原始SCADA数据中提取空间特征,建立数据与状态标签之间的非线性映射关系,从而输出异常检测结果。通过某风场的SCADA数据实例分析验证了所提出方法的可行性和有效性。

Accurate and reliable anomaly detection is of great significance to ensure the safe and efficient operation of wind turbines.However,due to the complex structure and variable operation conditions of wind turbines,the measure SCADA data usually present complex nonlinear and strongly correlated and coupling characteristics.To better capture spatial correlations among different sensor variables,a new wind turbine anomaly detection approach based on dilated causal convolution network is proposed.Specifically,Focal Loss is introduced to improve the traditional loss function to address the data imbalance issue.The proposed approach can extract effective multiscale spatially correlated features with different receptive field sizes and effectively model the hidden spatial causality among different sensor variables.Furthermore,it can provide an end-to-end anomaly detection solution for wind turbines,which can directly learn useful spatial features from raw SCADA data and build the nonlinear mapping relationship between original data space and condition label space,thus finally outputting the corresponding detection results.A real case study with SCADA data from a wind farm is used to verify the feasibility and effectiveness of the proposed approach.