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Numerical investigation of the critical heat flux in a 5 × 5 rod bundle with multi-grid

Nuclear engineering and Technology 2022年第5期54卷 1914-1928页

作者： Liu, Wei Shang, Zemin Yang, Shihao Yang, Lixin Tian, Zihao Liu, Yu Chen, Xi Peng, Qian State Key Laboratory of Reactor System Design Technology Nuclear Power Institute of China Chengdu 610213 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing 100044 China

To improve the heat transfer efficiency of the reactor fuel assembly, it is necessary to accurately calculate the two-phase flow boiling characteristics and the critical heat flux (CHF) in the fuel assembly. In this paper, a Eulerian two-fluid model combined with the extended wall boiling model was used to numerically simulate the 5 × 5 fuel rod bundle with spacer grids (four sets of mixing vane grids and four sets of simple support grids without mixing vanes). We calculated and analyzed 11 experimental conditions under different pressure, inlet temperature, and mass flux. After comparing the CHF and the location of departure from the nucleate boiling obtained by the numerical simulation with the experimental results, we confirmed the reliability of computational fluid dynamic analysis for the prediction of the CHF of the rod bundle and the boiling characteristics of the two-phase flow. Subsequently, we analyzed the influence of the spacer grid and mixing vanes on the void fraction, liquid temperature, and secondary flow distribution. The research in this article provides theoretical support for the design of fuel assemblies. © 2021 Korean Nuclear Society

关键词： 5 × 5 rod bundle Critical heat flux Eulerian two-fluid model Flow boiling

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Robust Consensus Based Secondary control for Microgrids Considering Communication Delay and Noises: A Kalman Filter Based Approach

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IEEE Transactions on Smart Grid 2025年

作者： Zhao, Qi Zhang, Wen Zhang, Tingting Chen, Jian Ye, Hua Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan250061 China Shandong University Shandong Research Institute of Industrial Technology Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Jinan250061 China

To achieve the frequency and voltage regulation as well as active and reactive power sharing, the consensus-based secondary controls of microgrids rely on communication network to exchange signals between neighbor inverters, which may be affected by the communication delay and noises. Unlike the existing methods that focus solely on communication delay, this paper proposes a Kalman filter-based robust consensus control to address the communication delay and noises comprehensively. Based on the discrete differential equation of the signal transmitted via communication network, the multi-step prediction model of the delayed signal is firstly built by considering the prediction errors. The state space model is then constructed by taking the predicted signals at each step as the state variables while the delayed and noise-involved signal and its differential value are seen as the measurements. A Kalman filter is further designed to comprehensively handle the communication delay, noises and prediction errors to estimate the true signal generated from neighbor inverters. With the estimated signals of all neighbor inverters, the consensus control is performed to coordinate the grid-following and grid-forming inverters within the microgrid. The effectiveness of the proposed method is demonstrated with simulations and experiments performed on the modified IEEE-33 bus-based microgrid. © 2010-2012 IEEE.

关键词： Kalman filters

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A control Strategy for Bidirectional Buck Converter with Combining Continuous and Active Discontinuous Conducting Modes

A Control Strategy for Bidirectional Buck Converter with Com...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Evgeniy Stolyarov Maxim Lashkevich Egor Kulik Yousef Ali Galina Demidova Alecksey Anuchin Department of Electric Drives Moscow Power Engineering Institute Moscow Russia Faculty of Control System and Robotics ITMO University Saint Petersburg Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The hybrid electric powertrains can utilize bidirectional buck converters to charge the battery while driving and discharge it when extra power is required. In order to increase the power density, the wide-bandgap switches should be used. These switches have bidirectional conductivity mode as they are metal-oxide-semiconductor field-effect transistors (MOSFET) and the utilization of the freewheeling diode is undesirable due to high voltage drop across it and much higher losses in conducting mode. This paper proposes the combined control strategy which implements continuous conducting mode at high and active discontinuous conducting mode at low currents. The use of model predictive control allows to minimize the conducting losses by means of reverse conductivity mode of the switches. The paper provides derivation of the combined control strategy. Then, the simulation results show the effectiveness of the proposed method. Finally, the simulation was performed with various errors in the definition of the system parameters.

关键词： power system measurements MOSFET Buck converters Density measurement Mechanical power transmission Conductivity control systems

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Development of a Simple Dielectric Equivalent Circuit Model from Polarization and Depolarization Current Measurements

Development of a Simple Dielectric Equivalent Circuit Model ...

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IEEE/IAS Industrial and Commercial power system Asia (I&CPS Asia)

作者： Peerawut Yutthagowith Phethai Nimsanong Yoshihiro Baba School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Power System Control Metropolitan Electricity Authority Power System Analysis Section Bangkok Thailand Graduate School of Science and Engineering Doshisha University Kyoto Japan

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Assessing insulation performance is vital for sustainable energy systems, which aim to minimize visual impact while ensuring reliability and resilience against weather-related disruptions. With increasing reliance on renewable energy sources, the importance of insulation performance becomes even more pronounced. Regular evaluations of insulation performance are critical for maintaining the safety, reliability, and resilience of electricity networks. By promptly identifying risks such as insulation degradation, utilities can improve maintenance practices, prioritize repairs, and enhance the integration of renewables, thereby boosting overall infrastructure performance. This paper introduces a straightforward and precise technique for determining the equivalent circuit parameters of insulation systems in high-voltage equipment. The proposed method facilitates the accurate determination of the dielectric loss factor from the equivalent circuit. To validate this approach, test cases were utilized to demonstrate its effectiveness. The results obtained through this method were then compared with those obtained using commercial software. The comparison reveals that the proposed approach results in a lower number of depolarization current branches compared to the commercial software. Also, it provides almost the same relative root mean square error and the lower root mean square relative error of the depolarization current. These findings underscore the appeal and effectiveness of the proposed method for determining the equivalent circuit of the dielectric model.

关键词： Insulation Renewable energy sources Current measurement Fitting Dielectric losses Software Integrated circuit modeling Root mean square Equivalent circuits Resilience

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Influence of contact layer on the sulphur corrosion of copper conductors in power transformers

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High Voltage 2022年第1期7卷 176-184页

作者： Lijun Yang Dong Ding Yuan Yuan Sihang Gao Yuncai Lu State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina College of Material Science and Engineering Chongqing UniversityChongqingChina Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqingChina State Grid Jiangsu Electric Power Co. Ltd.Electric Power Research InstituteNanjingChina

Owing to the corrosive sulphides in the mineral insulating oil such as dibenzyl disul-phide(DBDS),copper windings in oil‐immersed transformers may suffer sulphur *** study investigates the influence of different contact conditions on the corrosion of copper conductors in ***‐immersed accelerated corrosion experiment was conducted on copper conductors with various contact materials and different layers of insulating *** surface micromorphology,deposition compo-nent and the potentiodynamic polarization characteristics of copper conductors were *** results show that copper with different contact materials suffer sulphur corrosion;conversely,bare copper rarely *** was also verified that the existence of a particular gap between copper and contact layer is a significant factor in sulphur corrosion by the formation of a concentration cell on the copper *** insu-lating paper wrapped around copper conductors does not increase the corrosion ten-dency of copper;however,it contributes to the formation of a region concentrated with DBDS,Cu^(2+),H^(+)by adsorption and impediment,which plays a key role in facilitating the sulphur corrosion of copper ***,sulphur corrosion can also occur on other metal parts in transformers when there is a particular gap on surfaces other than copper.

关键词： corrosion copper sulphur

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The impact of current-loop control parameters on the electromagnetic transient voltage performance of voltage-source converter

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Frontiers in Energy Research 2024年 12卷

作者： Ding, Xiaoyu Chen, Hongwei Zhou, Xiaobo Shi, Qianhong Wang, Guanzhong China Energy Engineering Group Zhejiang Electric Power Design Institute Co. Ltd. Hangzhou China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Shandong University Jinan China

Since the large-scale integration of renewable energy sources into the AC grid has led to a relative decline in the voltage support capacity of the grid and the deterioration of the voltage dynamic at the grid connection point, especially under fast-scale conditions, the voltage disturbance has become more obvious. To improve the dynamic characteristics of the electromagnetic transient voltage at the grid connection point, this paper uses a practical dynamic damping method to analyze the impact of the converter current inner loop, feedforward voltage, and other links on the dynamic performance of the electromagnetic transient voltage. First, the current inner loop dynamic in the converter’s synchronous coordinate is converted into an equivalent transfer function in the stationary coordinate, and the transfer function between the transient voltage disturbance at the grid connection point and the inner loop current output is established. On this basis, the Bode diagram and the vector diagram of the transfer function in the weak damping frequency band are used to analyze the dynamic damping of the current inner loop parameters and voltage feedforward filter parameters on the voltage disturbance at the grid connection point. The results indicate that moderately increasing the current inner loop bandwidth or reducing the feedforward filter bandwidth can help enhance the electromagnetic transient voltage stability of the grid connection point, but increasing the current inner loop bandwidth will worsen the low-frequency damping characteristics and reduce the feedforward filter bandwidth will still help increase low-frequency damping. Copyright © 2024 Ding, Chen, Zhou, Shi and Wang.

关键词： Electromagnetic transients

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Machine Learning Strategies for Wind Turbine Fault Diagnosis: A Comparative Study

Machine Learning Strategies for Wind Turbine Fault Diagnosis...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Galina Demidova Alecksey Anuchin Denis Semenov Konstantin Savichev Electric Drive Department Moscow Power Engineering Institute Moscow Russia Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Engineering Center JSC "Power Machines" Saint-Petersburg Russia

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This study investigates the application of various machine learning (ML) models for diagnosing faults in wind turbines, with a particular focus on preprocessing SCADA data to ensure accurate model training. Several algorithms, including Light Gradient Boosting Machine (LGBM), Decision Tree (DT), Random Forest (RF), Long Short-Term Memory (LSTM) networks, and Support Vector Machine (SVM), were evaluated. The findings indicate that the LGBM Classifier, specifically Model 2, demonstrated the best overall performance, consistently achieving high F1-scores across different fault types. Models diagnosing faults in the wind turbine generator, feeding faults, and air-cooling faults achieved a training accuracy of 90.5% and an overall model accuracy of 70%. However, challenges were observed in diagnosing mains failure fault and excitation fault due to their lower frequency in the dataset, which impacted model accuracy. The research emphasizes the development of a robust multi-class classification approach. These results contribute valuable insights into optimizing fault diagnosis systems for wind turbines, balancing accuracy with practical implementation in real-world scenarios.

关键词： Fault diagnosis Training Support vector machines Accuracy Atmospheric modeling Boosting Data models Wind turbines Classification algorithms Long short term memory

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Fault Detection in Electric Drives Based on LSTM Autoencoder Model Machine Learning Approach

Fault Detection in Electric Drives Based on LSTM Autoencoder...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Galina Demidova Daniil Privalov Denis Semenov Dmitry Lukichev Zhitao Liu Alecksey Anuchin Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Institute of Cyber-Systems & Control Zhejiang University Hangzhou China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. This paper provides a brief overview of the methods based on machine learning used for fault detection in electric drives. Additional this research presents a machine learning approach that utilizes the Long Short-Term Memory Autoencoder model for fault detection deal with bearing faults. A flowchart illustrating the implementation steps of the Long Short-Term Memory Autoencoder algorithm is included. Additionally, a case study on bearing faults in an electric drive system is presented to demonstrate the advantage of the proposed approach. By training the Long Short-Term Memory Autoencoder on operational data, accurate prediction of bearing faults is achieved, enabling timely maintenance interventions. It is shown that in the 7th epoch of training, the model achieved a notably high accuracy more than $\mathbf{9 9 \%}$. The results highlight the potential of Long Short-Term Memory Autoencoder models as a valuable tool for fault detection in electric drives, thereby improving system reliability and performance.

关键词： Training Vibrations Fault diagnosis Machine learning algorithms Fault detection Machine learning Prediction algorithms

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Design of Narrowband and Wideband UHF Antennas for Partial Discharge Detection on High Voltage Equipment

Design of Narrowband and Wideband UHF Antennas for Partial D...

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International Conference on engineering, Applied Sciences, and Technology (ICEAST)

作者： Peerawut Yutthagowith Thanatorn Mai-Eiam Phethai Nimsanong School of Engineering King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand Power System Control Department Metropolitan Electricity Authority Bangkok Thailand

ISBN: (数字)9798350385946

ISBN: (纸本)9798350385953

This paper presents designs for ultra-high-frequency (UHF) electromagnetic sensors based on printed circuit board (PCB) antennas for partial discharge (PD) detection in gas-insulated substations (GIS) applications. The sensors were designed to meet the requirement of narrowband/wideband UHF measuring systems for the UHF PD detection in GIS. The PD signals in the physical characteristic of the high-frequency electromagnetic (EM) wave were detected using the designed UHF antennas, which include the inset-fed rectangular microstrip patch (IFRMP) antennas, known for their narrowband characteristics that offer high interference signal rejection to improve signal-to-noise ratio and the two arm Archimedean spiral (TAAS) antennas, recognized for their broadband frequency spectrum capability. Because UHF PD signals are typically detected using various field sensor designs, both IFRMP and TAAS antennas were designed for three different models under calculation and simulation using MATLAB to apply in different operating frequency bands within the UHF range of PD detection (300 MHz-3 GHz). The designed UHF antennas have very characteristics, i.e. portable, lightweight, and attractive for practical applications with a low fabrication cost. Additionally, they are more convenient to install on the insulating flange compared to some types of external antenna sensors. In terms of antenna performance, they exhibit parameters like return loss (parameter S11) less than -10 dB and voltage standing wave ratio (VSWR) less than 2 within a close range of the designed operating frequency. Using the designed antennas, the phase-resolved PD (PRPD) patterns, the statistical magnitudes and waveforms of detected UHF signal have been obtained from laboratory testing for the sensitivity verification to help users in the effective application of the UHF PD detection.

关键词： Antenna measurements Substations Microstrip antennas UHF measurements UHF antennas Sensor phenomena and characterization Loss measurement

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Deep learning based fault diagnosis for chemical process with statistical feature fusion

Deep learning based fault diagnosis for chemical process wit...

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第42届中国控制会议

作者： Lei Zhang Hao Xia Bao Chao School of Control Science and Control Engineering Dalian University of Technology Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China

The existing fault diagnosis methods based on deep neural networks mostly rely on process data for fault classification and diagnosis,which makes it difficult to distinguish minor faults with similar data ***,this paper proposes a deep learning-based fault diagnosis model that combines statistical indicators and neural *** analyzing and calculating the relevant information entropy to screen the statistical signal features of time domain and frequency domain,we can extract more hidden information from the raw data,and use the convolutional autoencoder to compress the statistical indicators for feature extraction,and use CNN network to extract features from raw ***,the two sets of features are fused and input into the LSTM network to obtain temporal information for fault *** conducted on the Tennessee Eastman(TE)process show that the proposed model outperforms typical classifiers and improves the diagnostic accuracy of minor faults.

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