Classes of Virtual Age Models Adapted to Systems With a Burn-In Period

International audienceThis paper proposes a new class of imperfect maintenance models for repairable systems subject to a burn-in period. Corrective maintenance and planned preventive maintenance are carried out over the course of the system's life. Bathtub shaped intensities are adapted to characterize the reliability of systems developing a burn-in period. Imperfect maintenance models, such as virtual age models, allow us to describe maintenance efficiency. A previous study has pointed out that classical virtual age models can only be applied to continuously degrading systems, and not to systems with a burn-in period. A first attempt to adapt virtual age models with bathtub shaped intensity has then been proposed considering one specific virtual age, a particular intensity, and one kind of maintenance. In this paper, a general framework for adapting virtual age models to bathtub shaped intensities is presented. In particular, the concept of optimal maintenance supersedes the notion of perfect maintenance. The models can be suitable to every bathtub shaped intensity, and can consider multiple kinds of maintenance. Finally, an application to real data sets is presented

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities