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The purpose of this paper is the investigation of the main aspects of optimal reliability allocation with respect to the design of hybrid electric vehicles. In particular, with reference to the hybrid electric vehicle propulsion system, the problem of data uncertainty, due to a scarce knowledge of the components' reliabilities, is taken into account. This problem is crucial for new technology systems and it is faced with a Bayesian approach: components' reliabilities are considered as random variables, characterised in the paper by negative log‐gamma distributions.

The main aspects of optimal reliability allocation with the design of hybrid electric vehicles are presented, pointing out the opportunity of a reliability evaluation in the planning stage.

The topic of a series hybrid vehicle reliability is addressed, nevertheless results can be easily extended to the parallel configuration. In particular, the opportunity of a reliability evaluation of the propulsion system in the design stage is highlighted, mainly when new technology components are involved.

The value of the paper consists in the methodology allowing to express the system reliability uncertainty as a function of component uncertain data. Then, as far as concern the practical implications, the optimal allocation of the components' reliabilities can be efficiently performed, in order to minimise the system total cost respecting a prefixed value of the system reliability. In the final part of the paper, a numerical application, related to a series hybrid electric vehicle propulsion system, is presented to show the feasibility of the approach.

Series capacitive compensation in electrical power systems is generally recognized as a very economical and powerful means for increasing the transmission capability of long‐distance transmission lines, resulting in relevant technical advantages in power system behavior: increased steady‐state and transient stability margins, reduced voltage drop in receiving systems during occurrence of severe contingencies and reduction of transmission losses. In this paper, a general method for choosing the series compensation degree is proposed, focusing the attention on the transient stability aspect. The approach, based upon a probabilistic framework, allows to properly select – at the design stage – the optimal degree of series compensation in order to contain the instability risk at an acceptable value. The transient stability problem is formulated by using the transient energy function method. In order to show the feasibility of the proposed approach, a numerical application to the Cigre test network is performed in the final part of the paper.

An optimal maintenance program for electrical power system components should be based on their reliability. Since, for components characterized by high reliability and cost such as HV circuit breakers, available statistical data are in limited number, a physical model for their ageing is opportune. In the paper a Predictive Maintenance Program (PMP), for determining when a HV circuit‐breaker should be rebuilt, is formalized; it is based upon an adequate stochastic model of electrical wear associated with breaking operations due to system faults. In the model, both fault times and amplitudes are described by means of random variables, in order to deduce a reliability function used as input data for a Bayesian discriminant analysis which dynamically estimates, also in the presence of observation errors, the state of the component, determining the optimal times to perform a maintenance action.