Chaotic Identification of Power Load Based on PPS Surrogate Method

The Lyapunov exponent and correlation dimension are often used to identify chaotic features of power load signals, but due to limitation of series length and noise interference, their application is limited. This article identifies chaotic features by the means of surrogate method. Based on a new null hypothesis : signal is periodical waveform with unrelated noise, and corresponding surrogate data generation method: pseudo-periodic surrogate data algorithm, the article generates surrogate data of power load, then does a hypothesis test by using Lempel-Ziv complexity which is more robust to noise. Through the comparison of the test result of Lorenz chaotic signal and that of sinusoidal periodical signal, it is demonstrated that chaotic features do exist in power load signal.