Reliability Evaluation for Aviation Electric Power System in Consideration of Uncertainty

• Published in: Energies (Basel) Vol. 13; no. 5; p. 1175
• Main Authors: Wang, Yao, Gao, Xinqin, Cai, Yuanfeng, Yang, Mingshun, Li, Shujuan, Li, Yan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2020
• Subjects: Aircraft; Aviation; aviation electric power system (aeps); Component reliability; Confidence intervals; Electric power; Electric power supplies; Electric power systems; Failure; failure rate; Failure rates; Flight; Flight conditions; Fly by wire control; minimal path set; Probability density functions; Random sampling; Random variables; reliability; Reliability analysis; Statistical analysis; Statistical sampling; System reliability; Uncertainty
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13051175

With the rapid development of more electric aircraft (MEA) in recent years, the aviation electric power system (AEPS) has played an increasingly important role in safe flight. However, as a highly reliable system, because of its complicated flight conditions and architecture, it often proves significant uncertainty in its failure occurrence and consequence. Thus, more and more stakeholders, e.g., passengers, aviation administration departments, are dissatisfied with the traditional system reliability analysis, in which failure uncertainty is not considered and system reliability probability is a constant value at a given time. To overcome this disadvantage, we propose a new methodology in the AEPS reliability evaluation. First, we perform a random sampling from the probability distributions of components’ failure rates and compute the system reliability at each sample point; after that, we use variance, confidence interval, and probability density function to quantify the uncertainty of system reliability. Finally, we perform the new method on a series–parallel system and an AEPS. The results show that the power supply reliability of AEPS is uncertain and the uncertainty varies with system time even though the uncertainty of each component’s failure is quite small; therefore it is necessary to quantify system uncertainty for safer flight, and our proposed method could be an effective way to accomplish this quantization task.