隔离开关合闸状态的非接触自动检测方法OA北大核心CSTPCD
Isolation switch on state of non-contact automatic detection method
为了提升隔离开关合闸状态自动检测效果,采用了基于激光位移传感器的隔离开关合闸状态非接触自动检测方法,使激光位移传感器的分辨率达到最大值,优化了激光位移传感器的光学参数,提升了其位移测量精度;通过光学参数优化后的激光位移传感器,采集隔离开关轮廓曲线,得到隔离开关轮廓曲线采样点集;利用最小二乘法拟合隔离开关轮廓曲线采样点的椭圆方程,并计算隔离开关导电臂与水平方向的夹角;当导电臂夹角处于规定夹角区间内,则判定合理开关合闸到位,完成隔离开关合闸状态非接触自动检测.结果表明,该方法计算的前、后导电臂夹角分别在0.4°与0.5°左右,均低于夹角规定区间.这一结果对实现隔离开关合闸状态非接触自动检测是有帮助的.
In order to improve the effect of automatic detection of isolation switch's closing state,a non-contact automatic detection method based on laser displacement sensor was adopted.The resolution of the laser displacement sensor was maximized,the optical parameters of the laser displacement sensor was optimized,and its displacement measurement accuracy was improved;Through the laser displacement sensor with optimized optical parameters,the isolation switch contour curve was collected,and the isolation switch contour curve sampling point set was obtained.Using the least square method,the elliptic equation of the sampling point of the isolation switch contour curve was fitted,and the angle between the conductive arm and the horizontal direction was calculated.When the angle of the conductive arm was within the specified interval,the reasonable switch closing was determined to be in place,and the non-contact automatic detection of the isolation switch closing state was completed.The experimental results show that the angle of the front and back conductive arms calculated by this method is about 0.4° and 0.5° respectively,which is lower than the specified interval of the angle.The results indicate that this result is helpful to realize the non-contact automatic detection of the isolation switch.
苑龙祥;汪华平;王阳;刘敬之;曲全磊
国网新疆电力有限公司 电力科学研究院,乌鲁木齐 830011,中国国网电力科学研究院 武汉南瑞有限责任公司,武汉 430074,中国国网青海省电力公司 电力科学研究院,西宁 810008,中国
动力与电气工程
激光技术测量与计量隔离开关激光位移传感器合闸状态非接触自动检测最小二乘法
laser techniquemeasurement and metrologyisolation switchlaser displacement sensorclosing statenon-contactautomatic detectionleast square method
《激光技术》 2024 (005)
734-738 / 5
国家重点研发计划资助项目(2017YFB0902500)
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