The insulated gate bipolar transistor (IGBT) is a crucial component of power converters (PCVs) and is commonly used in several PCVs topologies. On the other hand, the investigation and the study of the IGBT component show several changes within its behavior and lifetime, while this component is highly influenced by the operating conditions. Indeed, the monitoring of this component is necessary to minimize unexpected downtime of the wind energy system (WES). However, an accurate prediction of IGBTs remaining useful life (RUL) is the key enabler for life-time-optimized operation. Consequently, this work proposes a new prognostic approach for online IGBTs monitoring that adopts the time-domain analysis to extract useful information that is used as an input in the generation of the health indicator. Moreover, this approach is based on combining both of principal component analysis (PCA) technique and the feedforward neural network (FFNN) technique. PCA is used to reduce features extracted from IGBTs and the FFNN is implemented to achieve online regression of the trend parameter obtained from the PCA technique. To investigate and evaluate the performance of our idea we used the NASA Ames Laboratory Prognostics Center of Excellence IGBTs accelerated aging database. Finally, the achieved results clearly show the strength of the new trend parameter for IGBTs RUL prediction. The most notable strong correlation within the proposed approach is in relation to accuracy value, with an acceptable average accuracy rate of 60.4%.

With the developments in power electronic devices, there is increasing use of the insulated gate bipolar transistor devices in power converters. Power converters are more and more gaining attention in the wind energy conversion system. In this article, a grid-connected wind energy conversion system is considered. Wind energy conversion system optimal operation requires a diagnostic method for back-to-back power converter to be addressed in detail in this article. Therefore, an open-circuit switching fault diagnosis method for a back-to-back power converter is developed to improve the system reliability and optimize the produced power and also achieve its real operational cost. In this work, we investigate only the DC-Link voltage signal and we use the time–frequency analysis to achieve the diagnosis purpose. The scheduled diagnosis approach is divided into two tasks: the first one is the fault detection task and the second one is the fault localization task. The main novelty of the proposed diagnosis approach is to localize the appropriate faulty power converter, that is, rotor side power converter or grid side power converter. Also, this approach is able to localize faults when there is a simultaneous fault on both power converters. Simulations are carried out to verify the robustness of the proposed diagnosis approach. The achieved simulation results would help in diagnosing of Back-to-back power converter that would expectedly cancel the traditional diagnosis method.

This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO₂ NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective and widely used approach for the prediction of extremely complex problems. Various studies have been proposed to improve the weight training of ANN using metaheuristic algorithms. CSA is a latest and an effective metaheuristic method relies on the intelligent behavior of crows. CSA has been never proposed to improve the weight training of ANN. Therefore, CSA is adopted to optimize the initial weights and thresholds of the ANN model, in order to improve the training accuracy and prediction performance of functional properties of TiO₂ NPs coated cotton composites. Furthermore, our proposed algorithm i.e., multilayer perceptron with crow search algorithm (MLP-CSA) was applied to map out the complex input–output conditions to predict the optimal results. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and UPF were evaluated as output results. A sensitivity analysis was carried out to assess the performance of CSA in prediction process. MLP-CSA provided excellent result that were statistically significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.

This paper proposes a new approach for power electronic converters faults prognosis under-insulated gate bipolar transistor (IGBT) failures. Power electronic converters such as inverters and rectifiers are crucial parts of most renewable energy systems. Usually, the power converters are subjected to a high failure frequency rate and lead to a power system shut down. These faults, generally, start with thermal overstress, leading to IGBT wear-out over time. In the considered application, the power electronic converters are supplied by a generator. The unbalance IGBT degradation has been artificially created through high amplitude thermal overstress utilizing a DC at the gate. The proposed prognosis approach is based on the computation of the time-domain features to extract the degradation behavior of the IGBT device. Then, the Gaussian process regression technique is applied to the remaining useful life estimation. Prognostic results in three IGBT cases illustrate the effectiveness of the proposed prognostic approach, leading to an effective prognostic procedure for IGBT faults in power electronic converters.

Power electronic converters such as inverters and rectifiers are crucial parts of renewable energy systems. Usually, the power converters are subjected to a high failure frequency rate and lead to power system shut down. In an effort to predict power insulated gate bipolar transistor (IGBTs) device aging, this paper proposes a remaining useful life estimation algorithm for degraded power IGBTs, which are exposed to high amplitude of thermal overstress, through the Gaussian Process regression technique. The benefits of the proposed prognostic method are also illustrated with real device accelerated aging database set under thermal overstress utilizing a DC at the gate.