A Situational Awareness Method for Initial Insulation Fault of Distribution Network Based on Multi-Feature Index Comprehensive Evaluation

Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.

Belowground Bud Bank Is Insensitive to Short-Term Nutrient Addition in the Meadow Steppe of Inner Mongolia

Human activities and industrialization have significantly increased soil nutrients,such as nitrogen(N)and phos-phorus(P),profoundly impacting the composition and structure of plant community,as well as the ecosystem functions,especially in nutrient-limited ecosystems.However,as the key propagule pool of perennial grasslands,how belowground bud bank and its relationship with aboveground vegetation respond to short-term changes in soil nutrients was still unclear.In this study,we conducted a short-term(2021–2022)soil fertilization experiment with N addition(10 g N m^(-2) yr^(-1))and P addition(5 g N m^(-2) yr^(-1))in the meadow steppe of Inner Mongolia,China,to explore the responses of belowground bud bank,aboveground shoot population and their relationships(represented by the ratio of bud to shoot density-meristem limitation index(MLI))for the whole community and three plant functional groups(perennial rhizomatous grasses-PR,perennial bunchgrasses-PB,and perennial forbs-PF)to nutrient addition.The short-term nutrient addition had no significant influences on belowground bud density,aboveground shoot density,and MLI of the whole plant community.Plant functional groups showed different responses to soil fertilization.Specifically,N addition significantly increased the bud density and shoot density of PR,especially in combination with P addition.N addition reduced the shoot density of PF but had no influence on its bud density and MLI.Nutrient addition had significant effects on the three indicators of PB.Our study indicates that the belowground bud bank and its relationship with aboveground vegetation in temperate meadow steppe are insensitive to short-term soil fertilization,but plant functional groups exhibit specific responses in terms of population regeneration,which implies that plant community composition and ecosystem functions will be changed under the ongoing global change.

Noise level estimation method with application to EMD-based signal denoising

This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.

Gut commensal bacteria in biological invasions

INTRODUCTION The interaction between the host and host-associated microbial communities has been an area of increasing interest during recent decades.In most animal species,microflora is known to inhabit the gut,surface or sur­roundings(Dillon&Dillon 2004;Engel&Moran 2013;Kovatcheva-Datchary et al.2013).The microflora en­compasses a range of microbial relationships,including pathogenic,obligately mutualistic and commensal rela­tionships.During the past few decades,beneficial micro­organisms have received much attention due to a myri­ad of mutualistic effects(e.g.modifying a nutrient-poor diet,protecting the host from parasites or pathogens,af­fecting the mating and breeding of hosts,and producing insect pheromones).Such studies have aided our under­standing of the diversification and evolutionary suc­cess of animals(Flórez et al.2015;Paniagua Voirol et al.2018).Furthermore,technological applications have been identified:for example,efficient microbial cellu­lases from termites are presently being used in industrial processes(Scharf&Tartar 2008).

Ecological niche shifts affect the potential invasive risk of Phytolacca americana(Phytolaccaceae)in China

Background:Predicting the potential habitat of Phytolacca americana,a high-risk invasive species,can help provide a scientific basis for its quarantine and control strategies.Using the optimized MaxEnt model,we applied the latest climate data,CMIP6,to predict the distribution of potential risk zones and their change patterns for P.americana under current and future(SSP126,SSP245,SSP585)climate conditions,followed by invasion potential analysis.Results:The predictions of MaxEnt model based on R language optimization were highly accurate.A significantly high area of 0.8703 was observed for working characteristic curve(AUC value)of subject and the kappa value was 0.8074.Under the current climate conditions,the risk zones for P.americana were mainly distributed in Sichuan,Chongqing,Guizhou,Hunan,and Guangxi provinces.The contribution rate of each climatic factor of P.americana was calculated using the jackknife test.The four factors with the highest contribution rate included minimum temperature of coldest month(bio6,51.4%),the monthly mean diurnal temperature difference(bio2,27.9%),precipitation of the driest quarter(bio17,4.9%),and the warmest seasonal precipitation(bio12,4.3%).Conclusion:Under future climatic conditions,the change in the habitat pattern of P.americana generally showed a migration toward the Yangtze River Delta region and the southeastern coastal region of China.This migration exhibited an expansion trend,highlighting the strong future invasiveness of the species.Based on the predictions,targeted prevention and control strategies for areas with significant changes in P.americana were developed.Therefore,this study emphasizes the need of an integrated approach to effectively prevent the further spread of invasive plants.