Recognition of electric shock signal based on FIR filtering and RBF neural networks

Residual current protection device (RCD) has been widely used in low-voltage, rural power grids because it plays a very important role in avoiding physical shock, equipment damage, and electrical fires, etc, caused by leakage. At present, a setting value of leakage current can often be used as a key action for RCD. However, the electric shock current signal of the human body cannot be detected, and when unexpected current values close to or more than the setting value emerge, this will lead to the malfunction of RCD. In order to overcome the shortcomings above, we present a new recognition method for electric shock signal using digital filter technology and radial basis neural network. The method has three main stages. First, total leakage current and electric short current has been pre-processed using the finite impulse response digital filtering, which was designed by choosing suitable window functions and filter order. Second, the pre-processed signals are trained to create a three-level radial basis neural network. Last, the electric short current can be recognized by inputting the filtered total leakage current signal to the radial basis neural network, thus establishing the detection model. Experiments showed that the proposed method achieves an average relative error of 3.76% between detected value and actual value. The robustness, adaptability, and practicality of the proposed method also have been proven by the results. The proposed method made a definite practical significance for developing a new device of residual current protection.