应用小生境遗传算法优化导线钢芯断股漏磁检测传感器

Optimization Design of Detector for Steel Core Broken Strand Faults in Transmission Line Based on Niche Genetic Algorithm

输电线路的钢芯铝绞线在运行中可能发生钢芯损伤、断股现象,及时检测和诊断对于保证电网的安全运行具有重要意义。采用巡线机器人对钢芯损伤、断股进行检测是一种具有较好前景的方法。设计一种基于钢芯损伤、断股后形成的漏磁进行检测的传感器,采用48H号钕铁硼稀土永磁铁磁化输电导线钢芯,以永磁铁、导磁体沿导线的径向宽度和轴向长度为变量,以传感器质量作为优化目标,应用搜索能力强、收敛速度快的小生境自适应遗传算法优化传感器的结构尺寸。结果表明,在满足对导线钢芯磁化强度要求下,传感器的质量显著降低,有效提高了巡线机器人的携载能力,且所提方法具有通用性,适用于不同规格导线钢芯断股检测的漏磁传感器设计和优化。

Overhead transmission lines generally operate in the wild harsh environment and steel stranded wire in aluminum conductor steel-reinforced （ACSR） may be damaged by different kinds of reasons, so that detection for broken strands of steel core timely is an important mean to insure safety operation of transmission lines. The detection scheme for broken strands of ACSR by transmission lines inspection robot with detectorsis a method with good prospect. A detector based on magnetic flux leakage （MFL） theory for broken strand of steel core in ACSR was developed; the steel core in ACSR can be magnetized by 48H Nd-Fe-B rare-earth permanent magnet, and optimization design model of the detector was proposed, in which achieving minimal weight of detector was selected as the object function in constraint condition of the radial width and the radial width along the transmission line. The optimized model of detector was solved by the modified niche adaptive genetic algorithm （NAGA）. Theoretical analysis and application results show that the weight of detector is decreased and the carrying capacity of transmission line inspect robot is improved significantly in condition of magnetization requirement. The detector for broken strands proposed by this paper and its optimized method can be applied to different types of ACSR.