Design of Intelligent Strategy for Power Grid Inspection Based on Trajectory Representation Learning
A new intelligent grid inspection strategy based on trajectory representation learning and fuzzy optimization control strategy is designed to address the problems of low inspection efficiency and weak system robustness of traditional unmanned aerial vehicle network inspection methods in complex power grid environments and adverse weather conditions.By using trajectory representation learning algorithms to analyze historical inspection data and predict information based on real-time power grid status,dynamic adjustments are made to inspection paths and tasks.By extracting key trajectory features,a representation model of power grid status is constructed to accurately capture the spatiotemporal changes in power grid operation status.This not only provides more accurate data-driven predictions for inspection path planning,but also lays an important foundation for improving the inspection efficiency of inspection systems.Furthermore,the fuzzy optimization algorithm was introduced to optimize the uncertainty and fuzziness issues in the inspection process,generating the optimal inspection strategy and improving the flexibility and response speed to unexpected times.To further verify the correctness and superiority of the designed strategy,a horizontal comparison was made between the strategy proposed in this paper and other intelligent inspection strategies.The experimental test results showed that the inspection efficiency of the proposed strategy could reach 97.8%,and the fault recognition accuracy during the inspection process could reach 98.3%,providing an important way to improve the intelligence level of power grid inspection.
power grid inspectionintelligent strategytrajectory representation learningfuzzy optimization algorithminspection efficiency
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