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基于混合算法的油浸式变压器中检查微机器人的3D路径优化研究
Junji Feng1, Xinghua Liu2, Hongxin Ji3
1State Grid Tianjin Electric Power Research Institute, Tianjin 300180, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一种混合算法,用于微机器人检查浸油变压器,优化检查路径,以减少长度52.6%并提高故障检测效率.
科学领域:
- 机器人和自动化机器人与自动化
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 手动检查油浸式变压器的绝缘故障是低效和潜在的危险.
- 微型机器人为自动检查提供了一个有希望的替代方案,但由于电池寿命有限,它们在路径规划方面面临着挑战.
研究的目的:
- 开发和验证一种混合算法框架,以优化油浸式变压器中微型机器人的3D检查路径.
- 为了提高检测绝缘故障的效率和准确性,例如排放碳痕迹.
主要方法:
- 通过殖民地优化 (ACO) 解决的受约束旅行销售员问题 (CTSP) 形式的检查点访问.
- 采用混合路径规划策略,结合A*算法,快速探索随机树 (RRT) 和粒子集群优化 (PSO) 来避免障碍.
- 使用B-spline插值进行轨道平滑.
主要成果:
- 与未经优化的A*算法相比,实现了52.6%的路径长度减少.
- 经过A*-ACO组合证明具有特殊的稳定性.
- 产生了带有有限路径曲率 (<0.033) 和扭矩 (<0.026) 的光滑轨迹.
结论:
- 拟议的混合算法显著提高了油浸式变压器微机器人检查路径规划效率.
- 这种方法为在变压器维护中推进微型机器人检查技术提供了实用价值和理论支持.
- 改进的路径优化导致更有效,更准确的故障检测.
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