基于STPSO-BP和CM-SA算法的部分放电检测操纵器的性能优化分析
Lisha Luo1, Junjie Huang1, Yuyuan Chen2
1School of Mechanical and Energy Engineering, Guangdong Ocean University, Yangjiang 529500, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
本研究引入了一种双层模型,用于优化部分放电 (PD) 检测中的六度自由度 (6-DOF) 操纵器. 这种新方法显著提高了定位准确性,并减少了用于增强开关设备检查的能源消耗.
科学领域:
- 机器人和自动化
- 电气工程
- 人工智能
背景情况:
- 在高压开关设备中检测部分放电 (PD) 对电网可靠性至关重要.
- 由于逆动力学 (IK) 冗余和缺乏协同优化,六度自由度 (6-DOF) 操纵器在PD检测方面面临挑战.
- 现有的方法难以平衡终端效应器定位精度和能源效率.
研究的目的:
- 开发PD检测中的6-DOF操纵器的自适应式双层优化模型.
- 解决IK解决方案冗余性和精度和能源消耗的协同优化方面的挑战.
- 提高机器人系统在关键电气基础设施检查中的性能.
主要方法:
- 一个双层自适应优化模型,集成基于空间时间相关性粒子内存的粒子群优化BP神经网络 (STPSO-BP) 和基于混乱映射的模拟回火 (CM-SA).
- 第一个层使用STPSO-BP具有长期短期记忆 (LSTM) 来增强 IK,提高定位精度和适应性.
- 第二层采用CM-SA,具有混乱的关节角度约束和动态调整,用于协同优化能耗和定位误差,使用立方线插曲来平滑轨迹.
主要成果:
- 与传统的BP神经网络算法相比,定位错误减少了68.9%.
- 与优化前状态相比,能源消耗下降了60.18%.
- 拟议的模型显示了精度和能效的显著协同优化.
结论:
- 开发的双层模型为PD检测中的6-DOF操纵器提供了协同精度-能量控制的创新解决方案.
- 这种方法显著提高了机器人检查高压开关设备的效率和有效性.
- 这些发现有助于推进机器人在关键电气基础设施维护和安全方面的应用.
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