Research on Local Mean Decomposition Algorithms in Harmonic and Voltage Flicker Detection of Microgrid

In allusion to harmonic and voltage flicker in microgrid, the local mean decomposition algorithm is adopted to analyze harmonic and voltage flicker in power system. Complex original signals can be decomposed into a number of PF (product function) component, each layer of PF is composed of the envelope signal and frequency modulation function, which contains all the instantaneous amplitude and instantaneous frequency information. Further combinations can get the original signal time frequency distribution. Using the LMD to detect the harmonic signal and the multiple frequency voltage flicker signal in microgrid, The simulation results show that this Algorithm can adaptively decompose the signal and highlight the local characteristics of PF data, the method can be accurate analysis of multi frequency harmonic distortion signal, interharmonic signal and multiple frequency voltage flicker signal. Simulation waveform is not only influenced by "end effect" of small effect, and the instantaneous frequency is always positive. According to the actual analysis of a transformer with multi-frequency signal power in a microgrid system, using LMD algorithm and HHT algorithm, The result further prove the correctness of the proposed method, which provides the theoretical fundamental in a new way for the electrical energy detection in power system

Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline

For time series classification task using 1D-CNN, the selection of kernel size is critically important to ensure the model can capture the right scale salient signal from a long time-series. Most of the existing work on 1D-CNN treats the kernel size as a hyper-parameter and tries to find the proper kernel size through a grid search which is time-consuming and is inefficient. This paper theoretically analyses how kernel size impacts the performance of 1D-CNN. Considering the importance of kernel size, we propose a novel Omni-Scale 1D-CNN (OS-CNN) architecture to capture the proper kernel size during the model learning period. A specific design for kernel size configuration is developed which enables us to assemble very few kernel-size options to represent more receptive fields. The proposed OS-CNN method is evaluated using the UCR archive with 85 datasets. The experiment results demonstrate that our method is a stronger baseline in multiple performance indicators, including the critical difference diagram, counts of wins, and average accuracy. We also published the experimental source codes at GitHub (https://github.com/Wensi-Tang/OS-CNN/)

Disordered hyperuniformity signals functioning and resilience of self-organized vegetation patterns

In harsh environments, organisms may self-organize into spatially patterned systems in various ways. So far, studies of ecosystem spatial self-organization have primarily focused on apparent orders reflected by regular patterns. However, self-organized ecosystems may also have cryptic orders that can be unveiled only through certain quantitative analyses. Here we show that disordered hyperuniformity as a striking class of hidden orders can exist in spatially self-organized vegetation landscapes. By analyzing the high-resolution remotely sensed images across the American drylands, we demonstrate that it is not uncommon to find disordered hyperuniform vegetation states characterized by suppressed density fluctuations at long range. Such long-range hyperuniformity has been documented in a wide range of microscopic systems. Our finding contributes to expanding this domain to accommodate natural landscape ecological systems. We use theoretical modeling to propose that disordered hyperuniform vegetation patterning can arise from three generalized mechanisms prevalent in dryland ecosystems, including (1) critical absorbing states driven by an ecological legacy effect, (2) scale-dependent feedbacks driven by plant-plant facilitation and competition, and (3) density-dependent aggregation driven by plant-sediment feedbacks. Our modeling results also show that disordered hyperuniform patterns can help ecosystems cope with arid conditions with enhanced functioning of soil moisture acquisition. However, this advantage may come at the cost of slower recovery of ecosystem structure upon perturbations. Our work highlights that disordered hyperuniformity as a distinguishable but underexplored ecosystem self-organization state merits systematic studies to better understand its underlying mechanisms, functioning, and resilience.Comment: 34 pages, 6 figures; Supplementary Materials, 19 pages, 10 figures, 2 table