Health Condition Assessment of Multi-Chip IGBT Module with Magnetic Flux Density

To achieve efficient conversion and flexible control of electronic energy, insulated gate bipolar transistor (IGBT) power modules as the dominant power semiconductor devices are increasingly applied in many areas such as electric drives, hybrid electric vehicles, railways, and renewable energy systems. It is known that IGBTs are the most vulnerable components in power converter systems. To achieve high power density and high current capability, several IGBT chips are connected in parallel as a multi-chip IGBT module, which makes the power modules less reliable due to a more complex structure. The lowered reliability of IGBT modules will not only cause safety problems but also increase operation costs due to the failure of IGBT modules. Therefore, the reliability of IGBTs is important for the overall system, especially in high power applications. To improve the reliability of IGBT modules, this thesis proposes a new health state assessment model with a more sensitive precursor parameter for multi-chip IGBT module that allows for condition-based maintenance and replacement prior to complete failure. Accurate health condition monitoring depends on the knowledge of failure mechanism and the selection of highly sensitive failure precursor. IGBT modules normally wear out and fail due to thermal cycling and operating environment. To enhance the understanding of the failure mechanism and the external characteristic performance of multi-chip IGBT modules, an electro-thermal finite element model (FEM) of a multi-chip IGBT module used in wind turbine converter systems was established with considerations for temperature dependence of material property, the thermal coupling effect between components, and the heat transfer process. The electro-thermal FEM accurately performed temperature distribution and the distribution electrical characteristic parameters during chip solder degradation. This study found an increased junction temperature, large change of temperature distribution, and more serious imbalanced current sharing during a single chip solder aging, thereby accelerating the aging of the whole IGBT module. According to the change of thermal and electrical parameters with chip solder fatigue, the sensitivity of fatigue sensitive parameters (FSPs) was analyzed. The collector current of the aging chip showed the highest sensitivity with the chip solder degradation compared with the junction temperature, case temperature, and collector-emitter voltage. However, the current distribution of internal components remains inaccessible through direct measurements or visual inspection due to the package. As the relationship between the current and magnetic field has been studied and gradually applied in sensor technologies, magnetic flux density was proposed instead of collector current as a new precursor for health condition monitoring. Magnetic flux density distribution was extracted by an electro-thermal-magnetic FEM of the multi-chip IGBT module based on electromagnetic theory. Simulation results showed that magnetic flux density had even higher sensitivity than collector current with chip solder degradation. In addition, the magnetic flux density was only related with the current and was not influenced by temperature, which suggested good selectivity. Therefore, the magnetic flux density was selected as the precursor due to its better sensitivity, selectivity, and generality. Finally, a health state assessment model based on backpropagation neural network (BPNN) was established according to the selected precursor. To localize and evaluate chip solder degradation, the health state of the IGBT module was determined by the magnetic flux density for each chip and the corresponding operating conduction current. BPNN featured good self-learning, self-adapting, robustness and generalization ability to deal with the nonlinear relationship between the four inputs and health state. Experimental results showed that the proposed model was accurate and effective. The health status of the IGBT modules was effectively recognized with an overall recognition rate of 99.8%. Therefore, the health state assessment model built in this thesis can accurately evaluate current health state of the IGBT module and support condition-based maintenance of the IGBT module

Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model

Photovoltaic power generation is greatly affected by weather conditions while the photovoltaic power has a certain negative impact on the power grid. The power sector takes certain measures to abandon photovoltaic power generation, thus limiting the development of clean energy power generation. This study is to propose an accurate short-term photovoltaic power prediction method. A new short-term photovoltaic power output prediction model is proposed Based on extreme learning machine and intelligent optimizer. Firstly, the input of the model is determined by correlation coefficient method. Then the chicken swarm optimizer is improved to strengthen the convergence. Secondly, the improved chicken swarm optimizer is used to optimize the weights and the extreme learning machine thresholds to improve the prediction effect. Finally, the improved chicken swarm optimizer extreme learning machine model is used to predict the photovoltaic power under different weather conditions. The testing results show that the average mean absolute percentage error and root mean square error of improved chicken swarm optimizer - extreme learning machine model are 5.54% and 3.08%. The proposed method is of great significance for the economic dispatch of power systems and the development of clean energy

Driving behavior-guided battery health monitoring for electric vehicles using machine learning

An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe and reliable operation of electric vehicles (EVs). Feature-based machine learning methods have exhibited enormous potential for rapidly and precisely monitoring battery health status. However, simultaneously using various health indicators (HIs) may weaken estimation performance due to feature redundancy. Furthermore, ignoring real-world driving behaviors can lead to inaccurate estimation results as some features are rarely accessible in practical scenarios. To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions. We first summarized and analyzed various individual HIs with mechanism-related interpretations, which provide insightful guidance on how these features relate to battery degradation modes. Moreover, all features were carefully evaluated and screened based on estimation accuracy and correlation analysis on three public battery degradation datasets. Finally, the scenario-based feature fusion and acquisition probability-based practicality evaluation method construct a useful tool for feature extraction with consideration of driving behaviors. This work highlights the importance of balancing the performance and practicality of HIs during the development of feature-based battery health monitoring algorithms

Integration and Control of Distributed Renewable Energy Resources

The deployment of distributed renewable energy resources (DRERs) has accelerated globally due to environmental concerns and an increasing demand for electricity. DRERs are considered to be solutions to some of the current challenges related to power grids, such as reliability, resilience, efficiency, and flexibility. However, there are still several technical and non-technical challenges regarding the deployment of distributed renewable energy resources. Technical concerns associated with the integration and control of DRERs include, but are not limited, to optimal sizing and placement, optimal operation in grid-connected and islanded modes, as well as the impact of these resources on power quality, power system security, stability, and protection systems. On the other hand, non-technical challenges can be classified into three categories—regulatory issues, social issues, and economic issues. This Special Issue will address all aspects related to the integration and control of distributed renewable energy resources. It aims to understand the existing challenges and explore new solutions and practices for use in overcoming technical challenges

Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation

[[abstract]]This study focuses on the aging evaluation of Insulated gate bipolar transistor (IGBT) modules to ensure their stability during operation. An aging degree evaluation model is proposed based on whale optimization algorithm optimized extreme learning machine (WOA-ELM) algorithm. This study is mainly concentrated on two aspects. One is to use WOA to optimize the input weights and hidden layer biases of ELM to improve its prediction performance. This study tested the performance of WOA-ELM on several benchmark datasets. The results show that the prediction performance of WOA-ELM is better than ELM, genetic algorithm optimized ELM, cuckoo search optimized ELM, and dandelion algorithm optimized ELM. The other is to measure the electrical and thermal characteristic data of IGBT module under different aging conditions by accelerated aging test. Based on the analysis of the experimental data under different aging degrees, a method for evaluating the aging degree of IGBT modules based on WOA-ELM is proposed. Simulation results based on experimental data show that WOA-ELM still has better accuracy and generalization performance than others. In summary, the WOA-ELM algorithm is applicable to the aging evaluation method of IGBT modules proposed in this study which has good practical value

INTER-ENG 2020

These proceedings contain research papers that were accepted for presentation at the 14th International Conference Inter-Eng 2020 ,Interdisciplinarity in Engineering, which was held on 8–9 October 2020, in Târgu Mureș, Romania. It is a leading international professional and scientific forum for engineers and scientists to present research works, contributions, and recent developments, as well as current practices in engineering, which is falling into a tradition of important scientific events occurring at Faculty of Engineering and Information Technology in the George Emil Palade University of Medicine, Pharmacy Science, and Technology of Târgu Mures, Romania. The Inter-Eng conference started from the observation that in the 21st century, the era of high technology, without new approaches in research, we cannot speak of a harmonious society. The theme of the conference, proposing a new approach related to Industry 4.0, was the development of a new generation of smart factories based on the manufacturing and assembly process digitalization, related to advanced manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, and manufacturing tools and equipment. The conference slogan was “Europe’s future is digital: a broad vision of the Industry 4.0 concept beyond direct manufacturing in the company”

Development of Robust and Dynamic Control Solutions for Energy Storage Enabled Hybrid AC/DC Microgrids

Development of Robust and Dynamic Control Solutions for Energy Storage Enabled Hybrid AC/DC Microgrid

A Systems Engineering Reference Model for Fuel Cell Power Systems Development

This research was done because today the Fuel Cell (FC) Industry is still in its infancy in spite over one-hundred years of development has transpired. Although hundreds of fuel cell developers, globally have been spawned, in the last ten to twenty years, only a very few are left struggling with their New Product Development (NPD). The entrepreneurs of this type of disruptive technology, as a whole, do not have a systems engineering \u27roadmap , or template, which could guide FC technology based power system development efforts to address a more environmentally friendly power generation. Hence their probability of achieving successful commercialization is generally, quite low. Three major problems plague the fuel cell industry preventing successful commercialization today. Because of the immaturity of FC technology and, the shortage of workers intimately knowledgeable in FC technology, and the lack of FC systems engineering, process developmental knowledge, the necessity for a commercialization process model becomes evident. This thesis presents a six-phase systems engineering developmental reference model for new product development of a Solid Oxide Fuel Cell (SOFC) Power System. For this work, a stationary SOFC Power System, the subject of this study, was defined and decomposed into a subsystems hierarchy using a Part Centric Top-Down, integrated approach to give those who are familiar with SOFC Technology a chance to learn systems engineering practices. In turn, the examination of the SOFC mock-up could gave those unfamiliar with SOFC Technology a chance to learn the basic, technical fundamentals of fuel cell development and operations. A detailed description of the first two early phases of the systems engineering approach to design and development provides the baseline system engineering process details to create a template reference model for the remaining four phases. The NPD reference template model\u27s systems engineering process, philosophy and design tools are presented in great detail. Lastly, the thesi