The traditional Monte Carlo sampling method has several deficiencies in evaluating the reliability of more-electric aircraft (MEA), such as more sampling times and longer simulation time, due to the complex topology and low probability of failure of the aircraft. In this Letter, the optimal probability distribution of multi-component systems is introduced by cross-entropy, which theoretically makes the variance of reliability evaluation zero. Based on this, an evaluation method for the power-supply reliability of MEA is presented. For further analysis of the system vulnerabilities, the sensitivity of MEA is modelled based on reliability calculation. Finally, the hybrid MEA and high-voltage direct current MEA power systems are used as application scenarios to calculate the reliability and sensitivity. The results show that the proposed method has advantages in evaluating small probability events, and it is useful for the optimisation of MEA's power-supply system structure.

In order to overcome the shortcomings of traditional power supply reliability evaluation model of multi-electric aircraft in the process of multi-component system research, this paper introduces the approximate probability distribution of multi-component system by cross-entropy, it proposes a Monte Carlo method based on information entropy to evaluate the power supply reliability of multi-electric aircraft, and obtains the approximate probability distribution by differential evolution to make the reliability evaluation. The estimated variance is approximately zero. Finally, taking an aircraft power supply system as an example, the convergence and accuracy of several reliability analysis methods are compared and analyzed. The results show the superiority of this method.

No abstract

No abstract

Aiming at the shortcomings of traditional MPPT method, such as slow tracking speed and oscillation at maximum power point, this paper combines neural network with dichotomy to propose a new maximum power point tracking method for photovoltaic power generation system. And the traditional neural network uses the gradient descent method to solve the problem that the parameters are easy to enter the local optimal solution. In this paper, the improved differential evolution method is used to solve the global optimal solution. The neural network is used to track the vicinity of the maximum power point, and then the dichotomy is used to further approach the maximum power point. The simulation results show that compared with the traditional MPPT method, BP neural network and dichotomy can track the maximum power point faster, avoid the oscillation phenomenon, and have faster tracking speed and higher tracking accuracy.