In order to reveal the genetic subdivision within population of common wild rice Wza rufpogm Griff., allozyme analysis was conducted using 22 loci on a typical population from Yunnan Province, China. Non-random distribution of genotypes and/or genetic variability was found among three subpopulations, and the result was further demonstrated by considerable genetic differentiation observed (FsT= 0.206) within the population. Microhabitat selection may not be an important factor in shaping intra-population genetic structure, and restricted gene flow (fV,,,=0.964<1) and genetic drift act together towards a genetic subdivision within the population. This genetic subdivision may enhance inbreeding and will ultimately lead to genetic depletion within the predominantly outcrossing (t=0.830) perennial population, and therefore, more attention should be paid to the conservation and genetic management of the population.

Smart grid is increasingly becoming the development trend of grid technology. Smart grid is an important part of smart grid. Power equipment is the core part of smart grid, and the normal or not of power equipment directly affects the safety and stability of the entire power system. Smart smart grid usually has relatively complete fault diagnosis and self-healing functions to improve the stability and reliability of the grid. The application and development of a practical fault monitoring and early warning system for power equipment is to carry out predictive maintenance of power equipment so that the equipment can operate more safely and reliably. Due to the time-varying nonlinearity, stochastic uncertainty and local observability of smart grid, it is difficult for traditional power system modeling and analysis methods to fully reflect the steady- state and transient characteristics of power system in the new form. In this paper, based on machine learning simulation technology, through the research of power equipment fault monitoring system in smart grid, provide early warning information and solutions for equipment management personnel maintenance.

Power systems often suffer from various large disturbances during operation, especially grounding and short-circuit faults of operating lines, which may lead to transient instability of the system. In view of the fact that the existing relay protection is difficult to be fully applied to the power system with high permeability distributed energy, the machine learning algorithm is applied to the relay protection of the power system. Enhance the robustness of the model to noise; In the training, more weight is given to the unstable samples to balance the influence caused by the difference in the number of samples. In addition, a regular term is introduced into the loss function to control the complexity of the model and reduce over-fitting, thus adapting to various operating conditions of the power system. By comparing the difference between measured data and estimated data to detect bad data, the machine learning method is more intelligent than the traditional method. The research results show that the transient stability evaluation method based on incremental learning of support vector machine greatly reduces the learning time while maintaining the evaluation performance, and is a promising online learning algorithm for transient stability evaluation.

Abstract-In recent years, fractional differential equations are widely used in the many academic disciplines--viscoelastic mechanics, Fractal theory and so on. Furthermore, fractional differential equations can be used to describe some abnormal phenomenon. For instance, fractional convection-diffusion equation can be used to describe the fluid of abnormal infiltration phenomenon in the medium. In this paper, by means of the Arzela-Ascoli fixed point theorem, we can prove the existence of solution for the time-fractional differential equations. The conclusion is given out in detail.