Deep Learning‐Based Anomaly Diagnosis Method for Capacitive Voltage Transformer Measurement Error in <scp>PIoT</scp>

ABSTRACT In the era of power Internet of Things (PIoT), the accuracy of capacitive voltage transformers (CVTs) is crucial for maintaining the reliability of voltage measurement and protection systems in smart grids, thereby contributing to overall grid stability and efficiency. Accurate and timely detection of anomalies in CVT measurement errors is essential for preventing equipment failures, reducing maintenance costs, and improving overall system reliability. However, existing anomaly diagnosis methods often rely on statistical analysis and rule‐based approaches, which have limitations in capturing complex patterns and adapting to evolving anomaly types. This paper proposes a novel deep learning‐based anomaly diagnosis method for CVT measurement error in PIoT, called LSTM‐CVT. The proposed method leverages a long short‐term memory (LSTM) neural network architecture with three key strategies: bidirectional temporal dependency capture, hierarchical feature learning, and joint anomaly diagnosis and error estimation. The experimental results demonstrate the superior performance of LSTM‐CVT compared to state‐of‐the‐art baseline methods.