Train control system of high speed railway is the core equipment and technology to ensure the safe and reliable operation of high-speed trains, so reliability assessment of train control system counts for much. In this paper, firstly, Bayesian Networks (BN) technology is introduced for the limitation of traditional fault tree analysis (FTA) method. Secondly, reliability model of CTCS-3 train control system is established by BN transformed from the fault tree (FT) of CTCS-3. Finally, reliability of CTCS-3 and its redundant configuration is assessed by BN's advantage of expressing uncertainty knowledge and its bidirectional reasoning ability. It is not only convenient to identify the failure combination mode leading to the failure of the train control system, but also be able to find out the weak links in CTCS-3 system. Through the case analysis, BN shows great superiority in reliability assessment of system compared with FTA. What is more, it provides a new foundation for reliability assessment of train control system.
Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.
A comprehensive study of electron collisions with the X 1Σg + ground state as well as the metastable A 3Σu + and a 1Πg excited states of the N2 molecule is reported using the fixed-nucleus R-matrix method. Integral elastic scattering and electronic excitation cross sections from the X 1Σg + ground state to the eight lowest electronic states, A 3Σu +, B 3Πg, W 3Δu, B′ 3Σu −, a 1Πg, a′ 1Σu −, w 1Δu and C 3Πu, overall agree well with the available experimental and theoretical results although updates of some recommended values are suggested. Accurate electron impact electronic transition cross sections starting from the A 3Σu + and a 1Πg metastable excited states are reported. The total summed electronic transition cross sections from the a 1Πg state is dominant: an order of magnitude higher than those of the X 1Σg + ground state. The de-excitation cross sections generally show a downward trend with increasing incident electron energy, which is different from the elastic and electronic excitation cross sections which generally increase with collision energy. There is a prominent 2Πu symmetry resonance peak at 2.8 eV for electronic de-excitation scattering of a 1Πg → B 3Πg, which significantly contributes to the total summed cross sections from the a 1Πg excited state. The present results provide a new insight which will aid understanding of electron spectra in the atmosphere of the Earth and Titan.
The binding energy is of great importance in understanding the formation and stability of noncovalent interactions. However, the determination of the binding energy with high precision and efficiency in medium- and long-range noncovalent interactions is still challenging for quantum chemistry. Here, we assess the performance of random-phase approximation (RPA), a fully non-local fifth-rung of the Jacob ladder functional, in determining the binding energy of cation-π systems (cation = Li, Na, Be, Mg, Al, and NH; π = CH), which, to the best of our knowledge, has not been investigated. Using experimental results as the benchmark, we systematically compared the RPA method to the other ab initio methods (DFT/B3LYP, MP2, CCSD(T), and QCISD(T)) both in calculation accuracy and efficiency. From the perspective of accuracy, RPA is the best among these approaches, followed by the CCSD(T) and QCISD(T) methods. DFT/B3LYP and MP2 provide the worst accuracy. In addition, the computational efficiency of RPA is much faster than that of CCSD(T) and QCISD(T). We believe that RPA is a robust method for the precise description of medium- and long-range noncovalent interactions and is capable of providing benchmarking data. The interaction strength and interaction nature of cation-π systems are further analyzed by atoms in molecules (AIM) and the color-mapped reduced density gradient (RDG) isosurface, which are consistent with the characteristics of a typical cation-π interaction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.