南方能源建设 (Jan 2025)
Application Analysis of Intelligent Robot Inspection System at Offshore Step-up Substation
Abstract
[Objective] The offshore step-up substation serves as the central hub for power collection in offshore wind farms, and its inspection, operation and maintenance are crucial to ensuring safe production and improving efficiency. As offshore wind farms continue to expand into deeper and farther waters, the safety risks, operation and maintenance costs, inspection efficiency and other problems brought by the manual inspection mode become even more prominent, leading to the increasing demand for intelligent inspection of offshore wind farms. To effectively address the challenges of frequent inspections, high difficulty and low efficiency in offshore wind power step-up substation, an intelligent robot inspection system is designed in this paper. [Method] Firstly, a three-layer system architecture of the intelligent inspection system was designed, comprising perception-layer, network-layer and application-layer. Subsequently, detailed information about the robot inspection system was provided, including background management system, robot system design, communication power supply system design, and instrument image recognition technology. Finally, the application steps of the robot were designed from the transformation of the step-up substation, the installation mode of the robot and the inspection task planning. [Result] The developed robot inspection system is successfully applied to both new and old offshore step-up substations. It realizes remote inspections of equipment status at offshore step-up substations and facilitates intelligent analysis of inspection data. [Conclusion] The advantages of the proposed robot intelligent inspection system include high efficiency in inspections, reduces management costs and enhanced emergency response capabilities. These improvements significantly enhance operation and maintenance efficiency while reducing costs associated with offshore wind power generation. The research findings have important implications for advancing intelligence in offshore wind power operation and maintenance.
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