Classification recognition model of electric shock fault based on wavelet packet transformation and quantum neural network

Residual current operated protective devices (RCDs) have a wide range of application in low-voltage power grids. RCDs play an important role in preventing electric shock hazard and avoiding fire disaster caused by ground fault. In general, the root mean square (RMS) value of residual current detected is considered as the unique criterion to determine whether the protector acts or not. Theoretical analysis and operation experiences indicate that such a criterion is unavailable in identifying the shocking current signals from animals and human beings. Consequently, it makes human beings unsafe due to the electric shocking, and the disadvantages of the operation principle inherently exist. It results in the malfunction and tripping phenomenon, and greatly decreases the reliability and the rate of proper commissioning for RCDs. Many scholars at home and abroad have made a great deal of breakthrough research on the hardware structure and leakage current detection of residual current protection devices, which improved technical performance of residual current protection device. At present, related research about residual current protection technology focused on the application of a variety of signal processing methods and current amplitude calculation method of biological electrocution branch, which were not related to identification method of electric shock fault of neutral grounded power network. Thus, mathematical expression mechanism between the type of electric shock fault of organism and residual current needs to be studied to identify the type of electric shock fault in time and accurately in the technology of residual current protection. Therefore, time frequency digital feature of residual current signal, and correct identification of the type of electric shock fault are the important preconditions to discover and govern the problems of residual current protection device. In this paper, identification model of electric shock fault based on wavelet packet transform and quantum neural network was proposed to identify the law of nonlinear mapping between residual current and electric shock fault. First, wavelet packet transform was used to analyze energy spectrum fluctuation, and the fluctuation of energy spectrum below 312.475 Hz in residual current was obvious, and reached 9.05 and 9.00 respectively in 119.2-156.25 and 39.062 5-78.125 Hz. Moreover, 8-dimensional eigenvector of wavelet packet energy spectrum of residual current was extracted, and effective threshold for mutation amount of wavelet packet energy was set using average change rate for the difference of characteristic band energy possession ratio, which achieved accurate detection of electric shock fault for organisms. Finally, based on the combination of superposition state of quantum computing and adaptability of neural network computing, a quantum neural network was established as a decision-making system for the type of electric shock failure using energy eigenvectors of wavelet packet as valid sources of information. This network that adopted quantum neurons with multiple quantum levels overcame the problem of local minimum existing in traditional BP (back propagation) algorithm and speeded up the neural network training. The experimental results indicated that the accuracy of the network reached 0.000 998 92 when the number of iterations achieved 1 437, simulation time was 0.146 ms and the accuracy was 100% with the root mean square error (RSME) of 0.108 3, which was superior to EMD-FNN(empirical mode decomposition-fuzzy neural network) algorithm with training times of 125 8, simulation time of 0.398 ms and RSME of 0.193 8. Comparing to EMD-FNN, the time of decomposition of WPT-QNN (wavelet packet transformation-quantum neural network) saved 0.920 s, which could meet the need of actual requirement for quick and accurate action in residual current protection. The method proposed in the paper achieved the recognition of the type of electric shock quickly and efficiently, helpful to develop a new generation of adaptive residual current protection device based on current component action of electric shock for organisms. © 2018, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.