带电高压线路对其他线路参数测定影响的探析

高压输电线路作为电网的重要组成部分,其参数测试的准确性会直接影响整个电力系统的可靠性与安全性,因此如何精确测量高压输电线路参数具有其重要的实际意义。对直流电阻、正序参数、零序参数、部分电容以及互感参数五个方面进行研究,基于传统测量方法进行改进,实现高压输电线路的带电测量,并通过理论修正,设立修正项或修正系数,使所测量的参数数据满足相应的工程需求,从而保证电网的正常运作,具有一定的经济与社会效益。

Influence of Model Parameters on Hadron-Quark Phase Transition in Neutron Star Matter

We study the influence of the model parameters on the phase transitions, the equation of state （EOS）, and the corresponding mass-radius relations in the interior of neutron stars. The numerical analysis shows that the coupling constants of hyperons have a slight influence on the phase transitions and EOS, but an obvious influence on the particle fractions, while the bag constant B and coupling constant g have an important influence on the phase transitions, the EOS, and the mass-radius relations. We find that both the bag constant B and coupling constant g play the same role in the description of the interactions between quarks of hybrid stars. The maximum mass calculated by using the bag constant determined with experimental data （ranging from 175 to 200 MeV） falls in the interval of 1.4 ～1.7 solar mass. The corresponding radius is between 9.3 and 12 km. These results are in agreement with observed values of neutron stars. The possibility of the existence of a third family is discussed. The detection of a third family may provide a signature for a phase transition inside neutron stars.

A novel shapelet transformation method for classification of multivariate time series with dynamic discriminative subsequence and application in anode current signals

Classification of multi-dimension time series(MTS) plays an important role in knowledge discovery of time series. Many methods for MTS classification have been presented. However, most of these methods did not consider the kind of MTS whose discriminative subsequence was not restricted to one dimension and dynamic. In order to solve the above problem, a method to extract new features with extended shapelet transformation is proposed in this study. First, key features is extracted to replace k shapelets to calculate distance, which are extracted from candidate shapelets with one class for all dimensions. Second, feature of similarity numbers as a new feature is proposed to enhance the reliability of classification. Third, because of the time-consuming searching and clustering of shapelets, distance matrix is used to reduce the computing complexity. Experiments are carried out on public dataset and the results illustrate the effectiveness of the proposed method. Moreover, anode current signals(ACS) in the aluminum reduction cell are the aforementioned MTS, and the proposed method is successfully applied to the classification of ACS.

A New Kind of Two-Fold Integration Transformation in Phase Space and Its Uses in Weyl Ordering of Operators

We propose a new two-fold integration transformation in p-q phase space∫∫^∞-∞dpdq/π e^2i（p-x）（q-y）f（p,q）≡G（x,y）,which possesses some well-behaved transformation properties. We apply this transformation to the Weyl ordering of operators, especially those Q-P ordered and P-Q ordered operators.

A New Formula to Obtain Exact Green＇s Functions of Time-Dependent SchrōdingerEquation

We obtain a new relation between Green's functions of the time-dependent Schrōdinger equation forstationary potentials and Green's functions of the same equation for certain time-dependent potentials. The relationobtained here emerges very easily from a transformation introduced by Ray [J.R. Ray, Phys. Rev. A26 (1982) 729] andgeneralizes former work of Dodonov et al. [V.V. Dodonov, V.I. Man'ko, and D.E. Nikonov, Phys. Lett. A162 (1992)359.]

State Sampling Dependence of Hopfield Network Inference

The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations.We present the system in the glassy phase with low temperature and high memory load.We find that the inference error is very sensitive to the form of state sampling.When a single state is sampled to compute magnetizations and correlations,the inference error is almost indistinguishable irrespective of the sampled state.However,the error can be greatly reduced if the data is collected with state transitions.Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.

From Allan Variance to Phase Noise:A New Conversion Approach

A new mathematical method is proposed to convert the oscillator instability parameters from Allan variance to Spectrum Density(SD)of random phase fluctuations,which is the inversion of the classic transformation formula from SD to Allan variance.Due to the fact that Allan variance does not always determine a unique SD function,power-law model of the SD of oscillator phase fluctuations is introduced to the translating algorithm and a constrained maximum likelihood solution is presented.Considering that the inversion is an ill-posed problem,a regularization method is brought forward in the process.Simulation results show that the converted SD of phase fluctuations from Allan variance parameters agrees well with the real SD function.Furthermore,the effects of the selected regularization factors and the input Allan variances are analyzed in detail.