姿态传感与神经网络融合的验电行为检测方法

Behavior Detection of Electricity Testing Based on BP Network and Attitude Sensor

在电网作业中,对作业人员是否执行了验电操作进行检测,可以有效防止因疏漏或故意等原因造成未按规定验电的违章行为。为此,提出一种基于反向传播(back propagation,BP)神经网络和多个加速度传感器融合的验电行为检测方法。采用加速度传感器分别采集施工人员手臂和验电杆处的加速度、角速度、姿态角等数据,经特征提取后,由BP神经网络对验电操作中的抽出验电头、收回验电头、进行验电、携带验电杆行走、携带验电杆上下攀爬5种关键动作进行识别。实验结果表明,所提方法对5种验电操作中关键动作的分类准确率达97.4%,优于已有研究中常见的支持向量机、决策树和K邻近算法等识别方法,能够满足验电行为检测要求,同时可与基于视觉的验电行为检测互为补充,在更为复杂多样的电网作业场景中实现对未验电违章行为的检测。

In power grid operation,it is able to effectively prevent the violation of not checking the power according to the regulations due to reasons such as omission or intentionality through detecting whether the operator has performed electricity testing operation.Therefore,this study proposes a method for detecting electricity testing behavior based on the fusion of back propagation(BP)neural network and multiple acceleration sensors.The proposed method uses acceleration sensors to collect the acceleration,angular velocity,and attitude angle data at the constructor’s arm and the electricity testing pole respectively,and after feature extraction,the BP neural network recognizes five key actions in electricity testing operation,such as pulling away the electricity testing pole,putting away the electricity testing pole,performing electricity testing,carrying the electricity testing pole to walk,and climbing up and down the pole to carry the electricity testing pole.The experimental results show that the classification accuracy of the five key actions in electricity testing operation reaches 97.4%,which is better than the common recognition methods such as support vector machines(SVM),decision tree(DT)and K-nearest neighbor(KNN)in the existing research,and can satisfy the purpose of the detection of electricity testing behaviors.At the same time,it can be complemented with the vision-based power checking behaviors,so that it can be adapted to the detection of the unchecked illegal behaviors in the more complex and diversified power grid operation scenarios.