Automatic Verification Method for Virtual Circuits Based on Chinese Semantic Learning

This study addresses the challenges of verifying virtual circuits within smart substations, which traditionally suffer from inefficiencies due to manual verification methods and the subjective nature of current practices. We introduce an automated verification approach that leverages Chinese semantic learning combined with an optimized Word2vec model and incremental learning. By preprocessing virtual terminal descriptions and applying a similarity calculation method that integrates word frequency-inverse document frequency (TF-IDF) with cosine similarity, we map non-standard virtual terminals to their standard equivalents and establish a virtual circuit template Our method significantly enhances the adaptability and accuracy of virtual circuit verification, automates the process, and mitigates the impact of subjective factors, offering a more reliable and efficient verification process for smart substations.