基于轨迹表示学习的电网巡检智能策略设计

Design of Intelligent Strategy for Power Grid Inspection Based on Trajectory Representation Learning

针对传统无人机电网巡检方法在复杂电网环境以及恶劣天气等情况下出现的巡检效率较低、系统鲁棒性较弱等问题,设计了一种基于轨迹表示学习和模糊优化控制策略相融合的新型智能电网巡检策略。利用轨迹表示学习算法对历史巡检数据进行分析,且根据实时电网状态进行信息预测,对巡检路径和任务进行动态调整,并通过提取关键轨迹特征,构建电网状态的表示模型,对电网运行状态的时空变化规律进行准确捕捉,不仅为巡检路径规划提供了更精准的数据驱动预测,而且为巡检系统巡检效率的提升奠定了重要基础。进一步,引入模糊优化算法,对巡检过程中的不确定性和模糊性问题进行优化,生成了最优巡检策略,提高了对突发时间的灵活性和响应速度。为进一步验证所设计策略的正确性和优异性,对本文策略和其他智能化的巡检策略进行横向对比,实验测试结果显示,本文所提策略的巡检效率可达97.8%,巡检过程中的故障识别准确率可达98.3%,为提升电网巡检的智能化水平提供了重要途径。

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.