Biochar addition can improve the physical and hydraulic characteristics of sandy soil. This study investigated the effects of biochar on water holding capacity and water movement in sandy soil under drip irrigation. By indoor simulation experiments, the effects of biochar application at five levels (0%, 1%, 2%, 4% and 6%) on the soil water retention curve, infiltration characteristics of drip irrigation and water distribution were tested and analyzed. The results showed that biochar addition rate was positively correlated with water holding capacity of sandy soil and soil available water. Within the same infiltration time, with an increasing amount of added biochar, the diffusion distance of the horizontal wetting front (HWF) tended to decrease, while the infiltration distance of vertical wetting front (VWF) initially declined and then rose. The features of wetted bodies changed from “broad-shallow” to “narrow-deep” type. The relationship between the transport distances of HWF and VWF and the infiltration time was described by a power function. At the same distance from the point source, the larger the amount of added biochar, the higher the soil water content. Biochar had a great influence on the water content of the layer with biochar (0–200 mm) and had some effects at 200–250 mm without biochar; but it had less influence on the soil water content deeper than 250 mm. For the application rate of biochar of 4%, most water was retained within 0–250 mm soil layer. However, when biochar application amount was high (6%), it would be helpful for water infiltration. During the improvement of sandy soil, biochar application rate of 4% in the plow layer had the best effect.

This article proposes a fast adaptive S-transform (FAST) to improve the time-frequency resolution and computational efficiency of power quality disturbances (PQDs) feature extraction. By directly controlling the standard deviation instead of other parameters, FAST can reduce the difficulty of optimizing time-frequency resolution. Based on the frequency spectrum of PQD signals, FAST only needs to calculate characteristic frequency points determined by maximum envelope curve, which can eliminate redundant calculation without losing effective feature information. In fact, the computational complexity of parameter optimization step is often higher than that of S-transform (ST) calculation step. To address this problem, a window matching spectrum (WMS) method is proposed to optimize the time-frequency resolution. Matching the effective window width with the main spectrum energy interval of signals, WMS determines the standard deviation without iterative calculation. Based on the time-frequency representation of FAST, four features are extracted as the feature vectors and applied to the support vector machine, probabilistic neural network, extreme learning machine (ELM), convolutional neural network, decision tree (DT-C4.5) and random forest classifiers. Classification results of the six classifiers show that FAST has better time-frequency resolution and lower computational complexity than that of generalized S-transform and ST. In addition, the FAST-ELM method has stronger noise immunity and better performance than other combination methods with the simulation signals and experimental signals.