A fault detection and isolation (FDI) approach based on nonlinear sliding mode observers for a wind turbine model is presented. Problems surrounding pitch and drive train system FDI are addressed. This topic has generated great interest because the early detection of faults in these components allows avoiding irreparable damage in wind turbines. A fault diagnosis strategy using nonlinear sliding mode observer banks is proposed due to its ability to handle model uncertainties and external disturbances. Unlike the reported solutions, the solution approach does not need a priori knowledge of the faults and considers system uncertainty. The robustness to disturbances, uncertainties, and measurement noise is shown in the dynamic of the generated residuals, which is sensible to only one kind of fault. To show the effectiveness of the proposed FDI approach, numerical examples based on a wind turbine benchmark model, considering closed loop applications, are presented.

Continuous production processes and an adequate supply of raw materials are necessary to satisfy the growing demand for biodiesel. The use of different feed oils could be necessary to ensure sufficient supply for biodiesel production in certain circumstances; however, changing feed oil during the operation of a continuous reactor causes process disturbances. The present study analyses the effect of feed oil changes on the continuous operation of a industrial continuous stirred-tank reactor (CSTR) using a model which takes the dynamics of oil changes into account. The models previously reported only consider the operation of reactor with only one vegetable oil. The model in this work was developed by mass and energy balances. A methodology to model oil changes is presented. Glycerides and esters were characterized using adequate approaches. Moreover, accurate methods for predicting essential properties in the biodiesel manufacturing were used for the estimation of their thermo-physical properties. The kinetic parameters of the transesterifications were calculated from consistent studies selected from an exhaustive literature revision. The results show that temperature is practically not affected after oil changes; however, the ester concentration varies considerably. The ester mass fraction varying by up to 22.07% after an disturbance.

Fossil fuel combustion produces around 98% of coal emissions. Therefore, liquid and gaseous biofuels have become more attractive due to their environmental benefits. The biodiesel production process requires measurements that help to control and supervise the variables involved in the process. The measurements provide valuable information about the operation conditions and give estimations about the critical variables of the process. The information from measurements is essential for monitoring the state of a process and verifying if it has an optimal performance. The objective of this study was the conception of a virtual sensor based on the Extended Kalman Filter (EKF) and the model of a batch biodiesel reactor for estimating concentrations of triglycerides (TG), diglycerides (DG), monoglycerides (MG), methyl ester (E), alcohol (A), and glycerol (GL) in real-time through measurement of the temperature and pH. Estimation of the TG, DG, MG, E, A, and Gl through this method eliminates the need for additional sensors and allows the use of different types of control. For the performance analysis of the virtual sensor, the data obtained from the EKF are compared with experimental data reported in the literature, with the mean square error of the estimate then being calculated. In addition, the results of this approach can be implemented in a real system, since it only uses measurements available in a reactor such as temperature and pH.

This paper proposes a fault detection and isolation (FDI) scheme for a wind turbines subject to actuator faults in both the pitch system and the drive train system. The proposed scheme addresses fault detection and isolation problems using a fault estimation approach. The proposed approach considers the use of a particular class of sliding mode observers (SMOs) designed to maintain the sliding motion even in the presence of actuator faults. The fault detection problem is solved by reconstructing the actuator faults through an appropriate analysis of the nonlinear output error injection signal, which is required to keep the SMO in a sliding motion. To ensure accurate fault reconstruction, only two conditions are required, namely that the faults are bounded and they meet the matching condition. A scheme based on a bank of SMOs is proposed to solve the fault detection and isolation problem in the pitch system. For the drive train system, a scheme using only one SMO is proposed. The performance of the proposed FDI scheme is validated by using a wind turbine benchmark model subjected to several actuator faults. Normalized root mean square error (NRMSE) analysis is performed to evaluate the accuracy of the actuator fault estimations.