This paper considers the allocation of reliability and redundancy to parallel-series systems, while minimizing the cost of the system. It is proven that under usual conditions satisfied by cost functions, a necessary condition for optimal reliability allocation of parallel-series systems is that the reliability of the redundant components of a given subsystem are identical. An optimal algorithm is proposed to solve this optimization problem. This paper proves that the components in each stage of a parallel-series system must have identical reliability, under some nonrestrictive condition on the component's reliability cost functions. This demonstration provides a firm grounding for what many authors have hitherto taken as a working hypothesis. Using this result, an algorithm, ECAY, is proposed for the design of systems with parallel-series architecture, which allows the allocation of both reliability and redundancy to each subsystem for a target reliability for minimizing the system cost. ECAY has the added advantage of allowing the optimal reliability allocation in a very short time. A benchmark is used to compare the ECAY performance to LM-based algorithms. For a given reliability target, ECAY produced the lowest reliability costs and the optimum redundancy levels in the successive reliability allocation for all cases studied, viz, systems of 4, 5, 6, 7, 8, 9 stages or subsystems. Thus ECAY, as compared with LM-based algorithms, yields a less costly reliability allocation within a reasonable computing time on large systems, and optimizes the weight and space-obstruction in system design throughout an optimal redundancy allocation.

This paper deals with the prediction of wind turbines power output and proposes an approach to building a prediction model using the Artificial Neural Networks (ANN). The wind speed and output power measured on the site of Sendou, in Senegal, were used to identify the structure of the ANN. Spatiotemporal data on the climatic variables (wind speed, solar radiation, temperature, humidity, wind direction) collected on the same site were used to train the ANN. Data collected on three other sites (Goback, Keur Abdoul Ndoye and Sine Moussa Abdou), located on the northwest coast of Senegal, were used to validate the model and to analyze the influence of the spatial climatic variables on the performance of the model. Results showed the interest of considering climatic variables (wind speed, wind direction, solar radiation, temperature and humidity) as inputs to the ANN for wind turbines output power prediction. Further, this study showed that the prediction of the produced power depends strongly on the characteristics of the sites and the direction of the wind.