LAUSR

In modern Internet of Things-enabled smart grids, while existing data-driven efforts have made remarkable progress in wind turbine (WT) condition monitoring, the majority of them overlook potential data label errors w.r.t. WT fault information. To address this inadequacy, this article develops a two-stage automatic data label calibration (ADLC) approach via cost-effective time series (TS) data analytics. First, by profiling TS subsequence nearest neighbors (SNNs), most of the data labels are credibly calibrated by exploring the inherent temporal similarities within measured WT dynamics. To enhance the reliability of label calibration, ensemble labeling decisions are robustly made based on the diversity of multiplex variables. Then, for the remaining data labels not ascertained yet, a $k$ SNN profiling method inspired by classical $k$ nearest neighbor search is introduced to efficiently determine them from a complementary perspective. With no need for expensive domain expertise or computationally costly training procedures, the proposed approach can reliably calibrate WT fault information with a high efficiency, which makes it suitable for deploying into existing WT monitoring systems. Experimental test results with field data acquired from an actual WT illustrate the superior performances of the proposed approach.