基于Hilbert-Huang变换的生物触电电流检测模型

Detection model of biological electric shock current based on Hilbert-Huang transform

为了检测触电时刻剩余电流中生物体触电支路电流信号的难题,应用Hilbert-Huang变换方法,确定了生物触电时剩余电流的固有模态函数中相关系数最大的IMF分量的局部幅值达34.02 m A,且与原信号相关性系数达到0.99,同时剩余电流与触电电流暂态过程频谱特性具有相似变化规律。以此为基础,应用生物电流信号高频IMF分量幅值的突变特征,作为触电故障时刻确定判据,建立生物触电故障时刻判定方法,实际数据的仿真处理正确率为94.17%;筛选剩余电流分解的相关性较高的有限个数的低频固有模态IMF分量,应用逐步多元线性回归方法,提出基于剩余电流固有模态分量的生物触电支路电流幅值检测方法,仿真试验结果的平均相对误差值5.46%,具有良好的适应性和实用性,为研发基于生物体触电电流而动作的剩余电流保护装置提供参考。

The extensive application of residual current protection device in rural low-voltage power grid plays an important role to avoid electric shock casualties and fire accident caused by the leakage. Malfunction and failure action often occur in online residual current protection device due to the irrelevant between the setting value of action current and electric shock current of organism. Many researchers conducted a number of breakthrough research on detection of leakage current and hardware architectures of residual current protection technology, which improved the technology performance of residual current operated protective device to some extent, but it could not overcome the low efficiency of correct delivery rate. There were no mature technology and products at home and abroad on detection and characteristics of the law for biological shock signal when the organism was in electrical shock, which could not meet the need of reliable power system under many complicated factors. In this paper, detection model of biological electric shock current was researched based on Hilbert-Huang transformation. Therefore, aiming at how to detect electric shock time and recognize current signal of the biological electric shock branch in residual current, residual current and electric current signal of organism electric shock were set for example, Hilbert-Huang transformation method was used to determine local amplitude of the IMF component with the largest correlation coefficient in the natural modal function of residual current when biological shock occurred, this local amplitude was 34.02 mA, which reached 0.99 correlation coefficient with the original signal, meanwhile, there was a similar law of changes of spectral characteristics between residual current and electric shock current transient process. Biological current signal were consisted of 5 IMF components and one residual component, which accounted for 60.64% of total samples. The IMF component with the biggest correlation coefficient has much bigger variation range of amplitude. In actual signal processing, mutations of high frequency IMF could be used to determine the biological shock time, and IMF component with high amplitude share and correlation coefficient could be used to extract current amplitude of electric shock branch. Hence, in this study, based on those results above, firstly, mutation characteristics of high frequency IMF component amplitude in biological current signal could be used as a criterion and judgment method for electric shock time, which could automatically identify the moment of failure and locate the calculation. Simulation of the actual data processing accuracy was 94.17%. Moreover, low frequency natural modal IMF component was extracted from residual current decomposition, which should be higher relevance and limited quantity. At last, method was established for detecting current amplitude of biological shock branch based on natural mode component of residual current through application of stepwise multiple linear regression method. Simulation result shows that the average relative error is 5.46%, which indicates that the method proposed in this paper has good potential rapid technique for developing a new generation-residual current protection device based on biological electric shock current and plays an important role to avoid personal electric shock casualties and electrical fire in as well as safe operation low voltage power grid.