CHANGZHOU, China, Aug. 20, 2026 /PRNewswire/ — Timely elimination of defects in substation secondary systems is a critical link in ensuring the safe and stable operation of the power grid. However, traditional approaches have long relied on maintenance personnel sifting through extensive paper drawings, comparing historical cases one by one, and manually developing work plans—a cumbersome process heavily dependent on individual experience, with efficiency and quality hitting bottlenecks. The technical team at State Grid Changzhou Power Supply Company recently announced the successful development and deployment of an “Intelligent Assisted Defect Elimination System,” which leverages large AI models to restructure knowledge management and decision-support processes, reducing the average duration of a single defect-elimination task by approximately 40%.
The system consists of two core modules: a knowledge base and an intelligent Q&A component. The technical team utilized an open-source retrieval-augmented generation (RAG) engine to build a dedicated knowledge base for substation secondary maintenance. This involved vectorizing vast amounts of unstructured documents—including technical specifications, equipment manuals, historical defect reports, and retrofit drawings—to form an intelligently retrievable domain-specific knowledge graph. Meanwhile, a large language model was deployed in a local secure environment, ensuring data safety while enabling precise semantic understanding and information retrieval in response to natural language queries.
In practical application, maintenance staff simply need to input a description of the defect phenomenon (e.g., “communication interruption on a certain protection device”), and the system automatically generates a structured defect-elimination workflow within seconds. It simultaneously pushes relevant illustrations from device manuals, key handling points from similar historical cases, and related circuit diagram excerpts, providing on-site maintenance personnel with a one-stop decision-support reference. This model completely transforms the previously inefficient approach of “rote memory plus manual page-turning,” significantly lowering the experience threshold and enhancing the level of standardized operations.
To date, the system has been put into practical use in the comprehensive automation retrofits and routine maintenance of multiple substations, including the local 110 kV Rulin Substation. Statistical data shows that after the system’s deployment, the average time required for a single defect-elimination task has been reduced by approximately 40% compared with traditional methods, and the completeness and accuracy of work plans have received unanimous recognition from frontline teams.
State Grid Changzhou Power Supply Company stated that its next step will be to further deepen the application of AI technology across the full spectrum of substation operation and maintenance scenarios. This will include continuously expanding the knowledge base’s coverage and exploring collaborative integration with inspection robots, smart wearable devices, and other terminals, so as to accelerate the digital and intelligent evolution of power secondary system maintenance.