LAUSR

In Internet of Things (IoT)-enabled modern power grids, advanced IoT devices, e.g., synchronous phasor measurement units (PMUs), have been widely deployed to closely monitor the grids’ states and dynamics. In practice, however, PMU measurements are often contaminated with anomalous (low-quality) data, e.g., data spikes, unchanged data, data losses/dropouts, and high-level data errors. To ensure the reliability of various PMU data-based applications, it is imperative to efficiently implement PMU data anomaly identification (PDAI). Focusing on performing online PDAI in a cost-effective way, this article develops an intelligent data-driven PDAI approach for practical power grids. Given the defect that the majority of the existing data-driven PDAI efforts necessitate costly domain expertise-based data annotation to start offline learning, the PDAI approach in this article is realized by designing an auto-starting semisupervised learning (SSL) scheme that automatically starts to learn from totally unlabeled PMU data. First, on the basis of the inherent spatial–temporal correlations in regional PMU measurements, sequential PMU data acquired from a specific power grid are characterized in a discriminative manner by profiling their spatial–temporal nearest neighbors (STNNs). With the exploration of the discriminability of STNN profiles, part of the obviously anomalous/normal data is reliably labeled on the basis of a statistical prior knowledge-based rule. Such an STNN-based preprocessing technique for partial data labeling enables the desirable auto-starting functionality of the whole SSL scheme. Then, taking both labeled and unlabeled data as inputs, a recurrent SSL machine for PDAI is efficiently built in two steps, i.e., unsupervised pretraining and supervised fine-tuning. Numerical test results with simulated PMU data from the Nordic test system and actual PMU data from two practical power grids illustrate the excellent PDAI performances of the proposed approach during the online application.