Reliability Assessment of IGBT through Modelling and Experimental Testing

Lifetime of power electronic devices, in particular those used for wind turbines, is short due to the generation of thermal stresses in their switching device e.g., IGBT particularly in the case of high switching frequency. This causes premature failure of the device leading to an unreliable performance in operation. Hence, appropriate thermal assessment and implementation of associated mitigation procedure are required to put in place in order to improve the reliability of the switching device. This paper presents two case studies to demonstrate the reliability assessment of IGBT. First, a new driving strategy for operating IGBT based power inverter module is proposed to mitigate wire-bond thermal stresses. The thermal stress is characterised using finite element modelling and validated by inverter operated under different wind speeds. High-speed thermal imaging camera and dSPACE system are used for real time measurements. Reliability of switching devices is determined based on thermoelectric (electrical and/or mechanical) stresses during operations and lifetime estimation. Second, machine learning based data-driven prognostic models are developed for predicting degradation behaviour of IGBT and determining remaining useful life using degradation raw data collected from accelerated aging tests under thermal overstress condition. The durations of various phases with increasing collector-emitter voltage are determined over the device lifetime. A data set of phase durations from several IGBTs is trained to develop Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models, which is used to predict remaining useful life (RUL) of IGBT. Results obtained from the presented case studies would pave the path for improving the reliability of IGBTs

The sensitivity of 5MW wind turbine blade sections to the existence of damage

Due to the large size of offshore wind turbine blades (OWTBs) and the corrosive nature of salt water, OWTs need to be safer and more reliable that their onshore counterparts. To ensure blade reliability, an accurate and computationally efficient structural dynamic model is an essential ingredient. If damage occurs to the structure, the intrinsic properties will change, e.g., stiffness reduction. Therefore, the blade’s dynamic characteristics will differ from those of the intact ones. Hence, symptoms of the damage are reflected in the dynamic characteristics that can be extracted from the damaged blade. Thus, damage identification in OWTBs has become a significant research focus. In this study, modal model characteristics were used for developing an effective damage detection method for WTBs. The technique was used to identify the performance of the blade’s sections and discover the warning signs of damage. The method was based on a vibration-based technique. It was adopted by investigating the influence of reduced blade element rigidity and its effect on the other blade elements. A computational structural dynamics model using Rayleigh beam theory was employed to investigate the behaviour of each blade section. The National Renewable Energy Laboratory (NREL) 5MW blade benchmark was used to demonstrate the behaviour of different blade elements. Compared to previous studies in the literature, where only the simple structures were used, the present study offers a more comprehensive method to identify damage and determine the performance of complicated WTB sections. This technique can be implemented to identify the damage’s existence, and for diagnosis and decision support. The element most sensitive to damage was element number 14, which is NACA_64_618

Damage identification of wind turbine blades - a brief review

The increasing size of these blades of wind turbines emphasises the need for reliable monitoring and maintenance. This brief review explores the detection and analysis of damage in wind turbine blades. The study highlights various techniques, including acoustic emission analysis, strain signal monitoring, and vibration analysis, as effective approaches for damage detection. Vibration analysis, in particular, shows promise for fault identification by analysing changes in dynamic characteristics. Damage indices based on modal properties, such as natural frequencies, mode shapes, and curvature, are discussed

Study of centrifugal stiffening on the free vibrations and dynamic response of offshore wind turbine blades

Due to their large and increasing size and the corrosive nature of salt water and high wind speeds, offshore wind turbines are required to be more robust, more rugged and more reliable than their onshore counterparts. The dynamic characteristics of the blade and its response to applied forces may be influenced dramatically by rotor rotational speed, which may even threaten the stability of the wind turbine. An accurate and computationally efficient structural dynamics model is essential for offshore wind turbines. A comprehensive model that takes the centrifugal stiffening effect into consideration could make rapid and accurate decisions with live data sensed from the structure. Moreover, this can enhance both the performance and reliability of wind turbines. When a rotating blade deflects in its plane of rotation or perpendicular to it, the centrifugal force exerts an inertia force that increases the natural frequencies and changes the mode shapes, leading to changes in the dynamic response of the blade. However, in the previous literature, studies of centrifugal stiffening are rarely found. This study investigates the influence of centrifugal stiffening on the free vibrations and dynamic response of offshore wind turbine blades. The National Renewable Energy Laboratory (NREL) 5 MW blade benchmark was considered to study the effect of angular speed in the flap-wise and edge-wise directions. The results demonstrate that the angular speed directly affects the modal features, which directly impacts the dynamic response. The results also show that the angular velocity effect in the flap-wise direction is more significant than its effect in the edge-wise direction

Frequency Adaptive Parameter Estimation of Unbalanced and Distorted Power Grid

Grid synchronization plays an important role in the grid integration of renewable energy sources. To achieve grid synchronization, accurate information of the grid voltage signal parameters are needed. Motivated by this important practical application, this paper proposes a state observer-based approach for the parameter estimation of unbalanced three-phase grid voltage signal. The proposed technique can extract the frequency of the distorted grid voltage signal and is able to quantify the grid unbalances. First, a dynamical model of the grid voltage signal is developed considering the disturbances. In the model, frequency of the grid is considered as a constant and/or slowly-varying but unknown quantity. Based on the developed dynamical model, a state observer is proposed. Then using Lyapunov function-based approach, a frequency adaptation law is proposed. The chosen frequency adaptation law guarantees the global convergence of the estimation error dynamics and as a consequence, ensures the global asymptotic convergence of the estimated parameters in the fundamental frequency case. Gain tuning of the proposed state observer is very simple and can be done using Matlab commands. Some guidelines are also provided in this regard. Matlab/Simulink based numerical simulation results and dSPACE 1104 board-based experimental results are provided. Test results demonstrate the superiority and effectiveness of the proposed approach over another state-of-the art technique

Machine learning for forecasting a photovoltaic (PV) generation system

To mitigate the carbon print of buildings, they should have on-site renewable energy generation systems to supply energy for the buildings without relying on the national grid. Renewable generation sources rely on weather conditions and are therefore difficult to rely on as the only source of energy. Photovoltaic (PV) is forecasted through machine learning algorithms (MLA), but different methods have varied accuracy and have different training requirements such as more inputs or more data in general. No previous research has concluded an optimal MLA but to better apply them to PV systems, this must be established. To conclude an optimal MLA for a particular application, the dataset and required outputs must be determined, and how they affect the performance of the algorithm must be evaluated. The aim of this work is to compare benchmark MLA's through accuracy and usability for an operational University campus located in central Manchester, in the north of England. The MLA's including random forest (RF), neural networks (NN), support vector machines (SVM), and linear regression (LR) have been employed to forecast the PV system. If the power output of the renewables is accurately forecasted, a building management system (BMS) can be equipped to optimise on-site renewable energy generation. To accomplish this, sixty-four MLA models are created in total for forecasting at multiple horizons and dataset sizes which are validated against real-time data. Results in this work revealed that the RF algorithms have the lowest average error of the multiple tests at 32 root mean squared error (RMSE), whereas SVM, LR, and NN showed at 32.3 RMSE, 36.5 RMSE, and 38.9 RMSE respectively. Errors between forecasted and actual results are recorded in RMSE whereas changes in error are shown in mean actual percentage error (MAPE) to show the changes with respect to the original value. No MLA outperforms all others for accuracy and for requiring less data. No previous research is conducted to evaluate the performance of various MLA PV forecasting models through various sized data sets with critical analysis on the results. The comparison of benchmark algorithms when forecasting the PV generation of a local system allows the critical analysis of the models' accuracy and surrounding characteristics

Comparison of beam theories for characterisation of a NREL wind turbine blade flap-wise vibration

Offshore wind turbine blades significantly differ from their onshore counterparts. With the increasing sizes, the hostile weather operational conditions, and the need to protect them against damage and breakdown, structural dynamics analysis is an essential and popular approach. An accurate and computationally simple model is desirable in the application of online structural health monitoring. For example using a digital twin of such structure. Free vibration investigation is a fundamental step for the analysis of structural dynamics. When a rotating blade deflects either in the plane of rotation or perpendicular to it, the centrifugal force on each blade exerts inertia force along the blade span, which has the effect of stiffening the blade and, as a result increasing the natural frequencies compared with the stationary ones. However, the influence of different blade parameters on the flap-wise vibrations is not very well understood. In this paper, the blade of horizontal axis wind turbines (HAWT) is modelled using different beam theories to pursue the effect of adding the different parameters on the dynamic modal characteristics. The examined models have been used to determine the natural frequencies and mode shapes of the National Renewable Energy Laboratory (NREL) 5-MW wind turbine. Results demonstrate that increasing angular velocity has a significant impact on the natural frequencies and mode shapes. The rotary inertia is found to impact the free vibration responses of the studied blades. Moreover, increasing hub radius, pre-cone and pitch angles are found to have less influence on the natural frequencies. Compared to the other investigated methods, Bernoulli’s based algorithms are found to produce less accurate results

Damage identification in complex structures using vibration data

This study presents a damage identification procedure for complex structures based on analysing changes in vibration data between healthy and damaged conditions. The procedure involves calculating and comparing the first six mode shapes of both the intact and damaged structures using finite element analysis. The case study of 5Mega Watt National Renewable Energy Laboratory offshore wind turbine blade has been used to demonstrate the application of the procedure. The main objective is to utilise this procedure to identify and evaluate the severity of damage in different scenarios. Additionally, the procedure can be applied at various stages to detect and identify early signs of damage, serving as an early warning system

Acoustical Characteristics of Proton Exchange Membrane Fuel Cells

In order to optimise the performance of Proton Exchange Membrane Fuel Cells (PEMFCs) and to maximise their life cycle, water content has to be properly controlled. This paper presents a model-based method to determine the effect of load on PEMFC water content in PEMFCs using Acoustic Emission (AE) measurements. The developed model was implemented in COMSOL and verified using a single proton exchange membrane fuel cell (FC) operated under various loads. Acoustical events originating from water bubble formation have been identified and assessed using statistical parameters determined in the time and frequency domains. The feasibility of using AE techniques to detect and monitor the impact of a cell’s load variation on water content is assessed. As the model results were in good agreement with experimental data, it was concluded that an AE based method could serve as an effective monitoring and control tool of water content in PEMFCs. Statically, the root mean square for acoustic emission activity has a significant relation with load characteristics and can, potentially, be a candidate for determining the AE behavior in a PEMFC