In this paper, a wind speed prediction method was proposed based on the maximum Lyapunov exponent (Le) and the fractional Levy stable motion (fLsm) iterative prediction model. First, the calculation of the maximum prediction steps was introduced based on the maximum Le. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the fLsm iterative prediction model was established by stochastic differential. Meanwhile, the parameters of the fLsm iterative prediction model were obtained by rescaled range analysis and novel characteristic function methods, thereby obtaining a wind speed prediction model. Finally, in order to reduce the error in the parameter estimation of the prediction model, we adopted the method of weighted wind speed data. The wind speed prediction model in this paper was compared with GA-BP neural network and the results of wind speed prediction proved the effectiveness of the method that is proposed in this paper. In particular, fLsm has long-range dependence (LRD) characteristics and identified LRD by estimating self-similarity index H and characteristic index α. Compared with fractional Brownian motion, fLsm can describe the LRD process more flexibly. However, the two parameters are not independent because the LRD condition relates them by αH > 1.
<abstract> <p>Failure interruption often causes large blackouts in power grids, severely impacting critical functions. Because of the randomness of power failure, it is difficult to predict the leading causes of failure. ASAI, an essential indicator of power-supply reliability, can be measured from the outage time series. The series is non-stationary stochastic, which causes some difficulty in analyzing power-supply reliability. Considering that the time series has long-range dependence (LRD) and self-similarity, this paper proposes the generalized Cauchy (GC) process for the prediction. The case study shows that the proposed model can predict reliability with a max absolute percentage error of 8.28%. Grey relational analysis (GRA) has proved to be an effective method for the degree of correlation between different indicators. Therefore, we propose the method, which combines both GC and GRA to obtain the correlation coefficients between different factors and ASAI and to get the main factors based on this coefficient. The case study illustrates the feasibility of this approach, which power enterprises can employ to predict power-supply reliability and its influencing factors and help them identify weaknesses in the grid to inform employees to take protective measures in advance.</p> </abstract>
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