Evaluation method for insulation degradation of power transformer windings based on incomplete internet of things sensing data

Abstract

This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real-world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data.