scholarly journals A Fusion Algorithm for Online Reliability Evaluation of Microgrid Inverter IGBT

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1294 ◽  
Author(s):  
Chuankun Wang ◽  
Yigang He ◽  
Chenyuan Wang ◽  
Xiaoxin Wu ◽  
Lie Li
Keyword(s):  
Aging Process ◽  
Short Term Memory ◽  
Coupling Model ◽  
Reliability Evaluation ◽  
Junction Temperature ◽  
Stable Operation ◽  
Practical Case ◽  
Fusion Algorithm ◽  
Insulated Gate Bipolar Transistor ◽  
Damage Degree

Due to the diversity of distributed generation sources, microgrid inverters work under complex and changeable conditions. The core device of inverters, an insulated gate bipolar transistor (IGBT), bears a large amount of thermal stress impact, so its reliability is related to the stable operation of the microgrid. The effect of the IGBT aging process cannot be considered adequately with the existing reliability evaluation methods, which have not yet reached the requirements of online evaluation. This paper proposes a fusion algorithm for online reliability evaluation of microgrid inverter IGBT, which combines condition monitoring and reliability evaluation. Firstly, based on the microgrid inverter topology and IGBT characteristics, an electrothermal coupling model is established to obtain junction temperature data. Secondly, the segmented long short-term memory (LSTM) algorithm is studied, which can accurately predict the aging process of the IGBT and judge the aging state via the limited monitoring data. Then, the parameters of the electrothermal coupling model are corrected according to the aging process. Besides, the fusion algorithm is applied to the practical case. Finally, the data comparison verifies the feasibility of the fusion algorithm, whose cumulative damage degree and estimated life error are 5.10% and 5.83%, respectively.

scholarly journals Exploring the Electro-Thermal Parameters of Reliable Power Modules: Insulated Gate Bipolar Transistor Junction and Case Temperature

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2371
Author(s):  
Bo-Ying Liu ◽  
Gao-Sheng Wang ◽  
Ming-Lang Tseng ◽  
Kuo-Jui Wu ◽  
Zhi-Gang Li
Keyword(s):  
Bipolar Transistor ◽  
Thermal Parameters ◽  
Junction Temperature ◽  
Stable Operation ◽  
Switching Loss ◽  
Thermal Impedance ◽  
Power Module ◽  
Reliable Operation ◽  
Insulated Gate Bipolar Transistor ◽  
Transient Thermal

In the exploration of new energy sources and the search for a path to sustainable development the reliable operation of wind turbines is of great importance to the stability of power systems. To ensure the stable and reliable operation of the Insulated Gate Bipolar Transistor (IGBT) power module, in this work the influence of changes with aging of different electro-thermal parameters on the junction temperature and the case temperature was studied. Firstly, power thermal cycling tests were performed on the IGBT power module, and the I-V characteristic curve, switching loss and transient thermal impedance are recorded every 1000 power cycles, and then the electrical parameters (saturation voltage drop and switching loss) and the thermal parameters (junction-to-case thermal resistance) of the IGBT are obtained under different aging states. The obtained electro-thermal parameters are substituted into the established electro-thermal coupling model to obtain the junction temperature and the case temperature under different aging states. The degrees of influence of these electro-thermal parameters on the junction temperature and case temperature under different aging states are analyzed by the single variable method. The results show that the changes of the electro-thermal parameters under different aging states affects the junction temperature and the case temperature as follows: (1) Compared with other parameters, the transient thermal impedance has the greatest influence on the junction temperature, which is 60.1%. (2) Compared with other parameters, the switching loss has the greatest influence on the case temperature, which is 79.8%. The result provides a novel method for the junction temperature calculation model and lays a foundation for evaluating the aging state by using the case temperature, which has important theoretical and practical significance for the stable operation of power electronic systems.

scholarly journals Reliability evaluation of buck converter based on thermal analysis

2019 ◽  
pp. 217-227
Keyword(s):  
Thermal Analysis ◽  
Duty Cycle ◽  
Reliability Evaluation ◽  
Buck Converter ◽  
Junction Temperature ◽  
Switching Frequency ◽  
Time To Failure ◽  
Reliability Design ◽  
Insulated Gate Bipolar Transistor ◽  
Power Electronic Circuits

The design, which is based on the concept of reliability, is impressive. In power electronic circuits, the reliability design has been shown to be useful over time. Moreover, power loss in switches and diodes plays a permanent role in reliability assessment. This paper presents a reliability evaluation for a buck converter based on thermal analysis of an insulated-gate bipolar transistor (IGBT) and a diode. The provided thermal analysis is used to determine the switch and diode junction temperature. In this study, the effects of switching frequency and duty cycle are considered as criteria for reliability. A limit of 150°C has been set for over-temperature issues. The simulation of a 12 kW buck converter (duty cycle = 42% and switching frequency = 10 kHz) illustrates that the switch and diode junction temperature are 117.29°C and 122.27°C, respectively. The results show that mean time to failure for the buck converter is 32,973 hours.

scholarly journals An Aging-Degree Evaluation Method for IGBT Bond Wire with Online Multivariate Monitoring

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3962 ◽  
Author(s):  
Zilang Hu ◽  
Xinglai Ge ◽  
Dong Xie ◽  
Yichi Zhang ◽  
Bo Yao ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Junction Temperature ◽  
Normal Operation ◽  
Monitoring Method ◽  
Bonding Wire ◽  
Insulated Gate Bipolar Transistor ◽  
Igbt Module ◽  
Bond Wire ◽  
Artificial Neural Network Ann ◽  
Multivariate Monitoring

The aging fracture of bonding wire is one of the main reasons for failure of insulated gate bipolar transistor (IGBT). This paper proposes an online monitoring method for IGBT bonding wire aging that does not interfere with the normal operation of the IGBT module. A quantitative analysis of aging degree was first performed, and the results of multivariate and univariate monitoring were compared. Based on the relationship between the monitoring parameters and the aging of the IGBT bonding wire, gradual damage of the IGBT bond wire was implemented to simulate aging failure and obtain the aging data. Moreover, the change of junction temperature was considered to regulate monitoring parameters. Then, the aging degree was evaluated by an artificial neural network (ANN) algorithm. The experimental results showed the effectiveness of the proposed method.

scholarly journals Multimodal Decision-level Group Sentiment Prediction of Students in Classrooms

2019 ◽  
Vol 8 (12) ◽  
pp. 4902-4909
Keyword(s):  
Neural Network ◽  
Classroom Environment ◽  
Short Term Memory ◽  
Human Life ◽  
Support Vector ◽  
Fusion Algorithm ◽  
Decision Level ◽  
Audio Features ◽  
Twitter Account ◽  
Group Emotions

Sentiment analysis can be used to study an individual or a group’s emotions and attitudes towards other people and entities like products, services, or social events. With the advancements in the field of deep learning, the enormity of available information on internet, chiefly on social media, combined with powerful computing machines, it’s just a matter of time before artificial intelligence (AI) systems make their presence in every aspect of human life, making our lives more introspective. In this paper, we propose to implement a multimodal sentiment prediction system that can analyze the emotions predicted from different modal sources such as video, audio and text and integrate them to recognize the group emotions of the students in a classroom. Our experimental setup involves a digital video camera with microphones to capture the live video and audio feeds of the students during a lecture. The students are advised to provide their digital feedback on the lecture as ‘tweets’ on their twitter account addressed to the lecturer’s official twitter account. The audio and video frames are separated from the live streaming video using tools such as lame and ffmpeg. A twitter API was used to access and extract messages from twitter platform. The audio and video features are extracted using Mel-Frequency Cepstral Co-efficients (MFCC) and Haar Cascades classifier respectively. The extracted features are then passed to the Convolutional Neural Network (CNN) model trained on the FER2013 facial images database to generate the feature vector for classification of video-based emotions. A Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), trained on speech emotion corpus database was used to train on the audio features. A lexicon-based approach with senti-word dictionary and learning based approach with custom dataset trained by Support Vector Machines (SVM) was used in the twitter-texts based approach. A decision-level fusion algorithm was applied on these three different modal schemes to integrate the classification results and deduce the overall group emotions of the students. The use-case of this proposed system will be in student emotion recognition, employee performance feedback, monitoring or surveillance-based systems. The implemented system framework was tested in a classroom environment during a live lecture and the predicted emotions demonstrated the classification accuracy of our approach.

scholarly journals Reliability Evaluation for Cyber-Physical Smart Substation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yangrong Chen ◽  
Jun’e Li ◽  
Ang Xu ◽  
Kai Yuan ◽  
Kaipei Liu
Keyword(s):  
Smart Grid ◽  
Hybrid Method ◽  
Sampling Method ◽  
Circuit Breaker ◽  
Latin Hypercube Sampling ◽  
Reliability Evaluation ◽  
Stable Operation ◽  
Reliability Indices ◽  
Physical Interactions ◽  
Smart Substation

Smart substation is the key part of smart grid. The reliability of smart substation is extremely important to the safe and stable operation of the smart grid. Smart substation is a cyber-physical system (CPS). Hence, this paper conducts reliability evaluation study for smart substation from the perspective of CPS. Firstly, the basic reliability indices of cyber and physical elements of smart substation are presented. The reliability index of one cyber element takes into account the reliability factors of data leakage, tampering, loss and delay, etc., on the cyber side. Then, the cyber-physical interactions of smart substation are analyzed. It is concluded that the effect of the cyber side and cyber-physical interactions on the reliability of smart substation is reflected in the effect of measurement and control messages on circuit breaker operation. And, the new reliability indices considering cyber-physical interactions are proposed. Furthermore, the MALI-hybrid method, which combines the Monte Carlo method, analytical method, Latin hypercube sampling method, and important sampling method, is presented for evaluating the reliability of smart substation. Finally, the rationality of the proposed reliability indices, the efficiency, and correctness of MALI-hybrid method are verified by case studies.

scholarly journals Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization

2019 ◽  
Vol 9 (2) ◽  
pp. 292 ◽  
Author(s):  
Jiahui Zhang ◽  
Zhiyu Xu ◽  
Weisheng Xu ◽  
Feiyu Zhu ◽  
Xiaoyu Lyu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Power Plant ◽  
Short Term Memory ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Practical Case ◽  
Virtual Power ◽  
Swarm Optimization ◽  
Virtual Power Plant

This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP in terms of economic cost (EC) and power quality (PQ). Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, “improvisational” and “foresighted”, are compared by testing on the Institute of Electrical and Electronic Engineers (IEEE) 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over a 5-h receding horizon, takes 10 Pareto dominances in 24 h.

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