A Bayesian Regularized Artificial Neural Network for Up-Scaling Wind Speed Profile

Maximizing gains from wind energy potential is the principle objective of the wind power sector. Consequently, wind tower size is radically increasing. However, choosing an appropriate wind turbine for a selected site requires having an accurate estimation of vertical wind profile. This is also imperative from the cost and maintenance strategy point of view. Installing tall towers or other expensive devices such as LIDAR or SODAR raises the costs of a wind power project. In this work, we aim to investigate the ability of a Neural Network trained using the Bayesian Regularization technique to estimate wind speed profile up to a height of 100m based on knowledge of wind speed at lower heights. Results show that the proposed approach can achieve satisfactory predictions and prove the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights

A Probabilistic Assessment Approach for Wind Turbine-Site Matching

This article provides a new methodology for wind turbine-site matching by using a probabilistic approach. The random behavior of the wind speed climate and the uncertainties of wind turbine characteristics are important to take into account in models used to evaluate the performance of the wind turbine. The proposed formulation of the wind turbine-site matching is derived based on the probabilistic reliability assessment approach. It was experimented using different power curve approximation models, for different random conditions, using time series of wind speed in two sites in Morocco: Dakhla and Essaouira. A comparison based on methods used in literature for the estimation of two-parameter of the Weibull function to fit the wind speed distribution is also carried out. The results revealed that the introduced performance indicators are less sensitive to the models used to approximate the wind power curves compared to the deterministic conventional indicator that leads to different rankings and problems of over-sizing or under-sizing. However, those performance indicators are more sensitive to the variation of the wind speed distribution parameter’s and can help on accurately estimate the wind power. Moreover, the proposed formulation allows a global sensitivity analysis using Sobol’s indices to observe the influence of each input parameter on the observed variances of the performance of a wind turbine. A numerical application illustrates the interpretation of sensitivity indices and shows the impact of the wind speed and the rated wind speed on the variance of the wind turbine performance. This method can help wind energy developers and manufacturers to optimally select WTGs for their future project and accurately forecast the performance of their WTGs for monitoring and maintenance scheduling under uncertainty

Bayesian estimation in accelerated life testing

A common problem of high-reliability computing is, on the one hand, the magnitude of total testing time required, particularly in the case of high-reliability components; and, on the other hand, the number of devices under testing. In both cases, the objective is to minimise the costs involved in testing without reducing the quality of the data obtained. One solution is based on Accelerated Life Testing (ALT) techniques which permit decreasing the testing time. Another solution is to incorporate prior beliefs, engineering experience, or previous data into the testing framework. It is in this spirit that the use of a Bayesian approach can, in many cases, significantly reduce the number of devices required. This paper presents a study of the Arrhenius-Exponential model by an evaluation of parameters using Maximum Likelihood (ML) and Bayesian methods. A Monte Carlo simulation is performed to examine the asymptotic behaviour of these different estimators

Comparative analysis for wind resource assessment : Bayesian Regularization and Levenberg-Marquardt Training Algorithm for Neural Network

International audienc

Applied Bayesian regularized artificial neural network based approach for up-scaling wind speed profil and distribution

International audienc

Reliability Estimation of Mechanical Components Using Accelerated Life Testing Models

This chapter presents an overview of using accelerated life testing (ALT) models for reliability estimation on mechanical components. The reliability is estimated by considering two test plans: a classical one testing a sample system under accelerated conditions only and a second plan with previous accelerated damage. The principle of the test plan with previous accelerated damage is testing the sample under step-stress. In the beginning (until time N 1), the sample is tested under stress s 1 (accelerated testing: s1>s0); when the tested units have used many of their “resources,” the stress s 1 is replaced by the operating conditions s 0 (until the time N 2). Therefore, failure times under the accelerated conditions can be used to estimate reliability function in operating conditions. The time transformation function is considered as log-linear and four types of estimation are studied: parametric, Extended Hazard Regression (GPH), semi-parametric, and nonparametric models. The chapter is illustrated by a simulation example of ball bearings testing. The results are used to analyze and compare these estimation methods. The simulations have been performed both with censored data and without censoring, in order to examine the asymptotic behavior of the different estimates