Cohort: 2 

Location: Beijing, China 

Application Title: AI monitoring and control for renewable energy infrastructure and robotic intervention safety 

Team Members: Bin Huang, Enmin Wang, Xin Ren, Ge Ge, Angang Wei, Jiazheng Yu 

The challenge 

Large-scale renewable energy infrastructure is difficult to operate and maintain. Renewable energy facilities like photovoltaic plants occupy vast areas in remote locations, making manual inspections inefficient. Equipment such as substations, transformers, and switchgear in offshore wind farms must function reliably in harsh environments, often far from shore and exposed to variable weather conditions. 

Traditionally, inspection and maintenance rely on scheduled manual checks supported by basic sensor data. Infrared imaging may be used to detect overheating, while visible-light cameras help identify surface issues. These methods are effective in isolation, but each captures only part of the picture. Operational data from equipment is often stored separately from inspection imagery, making it difficult to understand how visual anomalies relate to real-time performance. In addition, many facilities deploy inspection robots and automated devices without centralized oversight, resulting in fragmented data and underused assets. 

As offshore renewable capacity expands, these limitations increase operational risk and slow response times, particularly in situations where human access is restricted for safety reasons. 

The solution 

A unified platform for diagnostics, data management and autonomous response 

China Huaneng Clean Energy Research Institute, with Huaneng Jilin Power Generation and China Huaneng Group, developed an AI-based platform to manage monitoring, fault detection and unmanned equipment across renewable energy sites. 

The platform brings together inspection data, operational signals and equipment management within a single system. It supports operations teams by providing a consolidated view of asset condition across geographically distributed sites. 

Rather than treating diagnostics and data processing as separate functions, the system links them through a layered architecture that connects sensors, AI models and operational workflows. This allows issues to be addressed more consistently. 

How it works 

From inspection to intervention 

The platform supports three connected use cases. 

The first focuses on fault diagnosis for offshore substations. With numerous densely packed electrical devices within the substation, the integration of automated equipment, devices, and algorithms enables automated inspections to cover the primary content of weekly maintenance personnel inspections at offshore booster stations. 

Huaneng’s system processes images captured by robotic inspection through a deep learning pipeline, enabling it to flag anomalies and pinpoint equipment-level fault locations. Technicians can obtain a more comprehensive view of the system status without needing to visit the substation. 

The second use case integrates inspection imagery with operational data. Traditional inspections rely on infrared imaging or visible-light cameras, both of which have inherent limitations. The Huaneng system integrates infrared and visible light imaging into a unified diagnostic layer, simultaneously merging visual observations with operational data. When abnormal heat patterns align with irregular load behaviour, the system flags the issue as higher priority. This linkage allows maintenance teams to move from isolated observations to component-level fault analysis, even when data quality varies across sites. 

The third use case addresses management of unmanned equipment. Inspection robots, sensors and automated devices often operate independently and generate data in different formats. The platform introduces centralized oversight across group, regional and site levels. Advanced data cleansing algorithms are used to remove noise and correct inconsistencies before data is transformed and integrated into the broader system. This supports coordinated scheduling, lifecycle management and more reliable use of automated assets. 

Across all use cases, edge–cloud collaboration allows data to be processed locally where needed while supporting centralized analysis and control. 

How it differs from conventional approaches 

Reducing fragmentation across sites and systems 

Conventional renewable infrastructure management treats inspection, operations and equipment control as separate activities. Data is collected in silos and manual coordination is required to link findings across teams. 

Huaneng’s approach reduces this fragmentation by embedding AI into the core operational workflow. This makes it possible to move from detection to response more quickly, particularly in environments where human access is limited. 

The system also supports autonomous intervention. In emergency scenarios, robotic systems can perform actions such as operating switches or valves in areas that are unsafe for personnel. 

Impact 

• Data cleansing accuracy increased to 92%, improving reliability of operational datasets 
• Data processing speed increased by 25% through edge–cloud coordination 
• Detection accuracy for small and medium-sized pointer and dial defects exceeded 90% 
• Dual-spectrum diagnostics achieved high precision in identifying equipment anomalies 
• Autonomous robotic intervention enabled safe emergency operations in inaccessible environments 

Why it matters 

Supporting reliable operation of renewable assets 

As renewable energy infrastructure becomes more distributed and complex, maintaining reliability requires closer integration between data, diagnostics and control. 

China Huaneng Clean Energy Research Institute’s platform shows how AI can support this integration by linking inspection, operational analysis and unmanned equipment management within a single system. This approach helps reduce response times, improve asset visibility and support safer operations as renewable capacity continues to expand.