![]() ![]() ![]() The optimal identification and detection of power disturbance are realized. The one-dimensional power disturbance function is mapped to the two-dimensional function of time scale and time shift by continuous wavelet transform, and the higher-order spectral characteristic quantity of the power disturbance signal is extracted, and the extracted characteristic quantity is automatically classified by neural network. The time domain and frequency domain decomposition are used to decompose the characteristic of the power disturbance signal, and the wavelet analysis method is used to improve the adaptive focusing performance of the power disturbance signal. A power disturbance detection algorithm based on wavelet analysis and neural network is proposed. In the presence of power disturbance, the test accuracy of power is not good, in order to improve the performance of power testing, it is necessary to carry out the power disturbance detection design. ![]()
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