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2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Xiaolong Zhang Lin Zheng Heng Tian Li Liu Qun Ma Huafeng Wang Chong Gao State Key Laboratory of Advanced Power Transmission Technology State Grid Smart Grid Research Institute Co. Ltd. Beijing China Beijing Kedong Power Control System Co. Ltd. Beijing China State Grid Electric Power Research Institute Co. Ltd. Nanjing China State Grid Zhoushan power supply company Zhoushan China

power semiconductor devices are the essential core components of modern power electronic equipment. Predicting the remaining useful life (RUL) of power semiconductor devices can help formulate reasonable maintenance strategies for power electronic equipment, thereby improving the availability of energy conversion systems. This article proposes a deep learning method for predicting the RUL of power semiconductor devices, which uses on-state resistance as a critical state parameter. A long short-term memory (LSTM) neural network (NN) model and a convolutional neural network (CNN) model are used to predict the on-state resistance time series data trend. NN models can predict a short section of future data with past data input. The RUL of power semiconductor devices is estimated through curve fitting based on past and predictive data. The proposed method is validated using the power MOSFET accelerated aging dataset from NASA, and the validation results show that the performance of the proposed method is superior to that of existing methods, such as extended Kalman filtering (EKF) and particle filtering (PF).

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A Data-Physics Fusion-Based Approach to power system Equivalent Inertia Assessment

A Data-Physics Fusion-Based Approach to Power System Equival...

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IEEE Student Conference on Electric Machines and systems

作者： Chihan Zhou Changtao Kan Gengkun Li Pengming Zhai Guojie Tian Shuang Liang Shuolin Zhang State Grid Kunming Power Supply Company Kunming China State Grid Jinan Power Supply Company Jinan China State Grid Zhejiang Electric Power Co. Ltd. Lishui Supply Branch Lishui China Wenshan Power Supply Bureau of Yunnan Power Grid Co. Ltd. Grid Metering Center of Yunnan Power Grid Co. Ltd. Wenshan China Shandong Tianyu Changtong Energy Group Co. Ltd. Qingdao China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

In order to solve the error problem of the traditional inertia assessment method, a power system equivalent inertia assessment method incorporating machine learning technology is proposed. First, a physical model of single-machine equivalence is used, combined with a convolutional neural network (CNN) with self-adaptive and learning capabilities as a machine learning model. Second, the strongly correlated feature quantities are selected as inputs by analyzing the model structure and calculating the correlation between the inertia time constant and other feature quantities. In addition, the computational results of traditional evaluation methods are incorporated into the data model inputs, and the model parameters are optimized using the particle swarm optimization (PSO) algorithm. The simulation results prove that the proposed method has certain advantages in improving the accuracy of inertia estimation compared with the traditional method.

关键词： Training Accuracy Computational modeling Estimation Machine learning power system stability Data models Stability analysis Convolutional neural networks Particle swarm optimization

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Construction of Vehicle Driving Conditions Based on Rain flow Counting Theory 6

Construction of Vehicle Driving Conditions Based on Rain flo...

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6th CAA International Conference on Vehicular control and Intelligence, CVCI 2022

作者： Wang, Yunyi Lin, Jiamei Wang, Chao Guo, Jinyan Liu, Sihan Song, Xincheng School of Mechanical and Aerospace Engineering Key Laboratory of Cnc Equipment Reliability Jilin Changchun China Jilin University State Key Laboratory of Automotive Simulation and Control Jilin Changchun China Changchun Power Supply Company Construction Department Jilin Changchun China Engineering Training Center Jilin University Jilin Changchun China School of Mechanical and Aerospace Engineering Jilin Changchun China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

Vehicle driving conditions are used to describe the driving characteristics of a vehicle in a specific traffic environment. which can provide an important reference for vehicle fuel consumption and pollutant control. This paper presents a description of automotive vehicle working conditions based on rain flow counting theory. By using principal component analysis, the main characteristic parameters are obtained to represent the total sample, and the driving speed mapping method based on the rain flow counting theory is used to construct the vehicle driving condition curve that conforms to the urban characteristics. © 2022 IEEE.

关键词： Principal component analysis

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Study on control Sequence Coordination Method between Security and Stability control system and HVDC Pole control system

Study on Control Sequence Coordination Method between Securi...

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2020 IEEE Sustainable power and Energy Conference, iSPEC 2020

作者： Liu, Mingshun Zhu, Yihua Ma, Qinfeng Li, Chengxiang Chen, Rui Zhang, Jingpei Wu, Mingkang Power Dispatching Control Center of Guizhou Power Grid Co. Ltd China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid China

ISBN: (纸本)9781728191645

Security and stability control system (SSC) is an important guarantee for the safe operation of power grid, and for HVDC system modulation function is an important kind of stability control measures. Combined with Tianshengqiao-Guangzhou HVDC safety strategy transformation, RTDS simulation test is conducted. In allusion to the exposed defects about SSC handling with multiple DC power modulation orders and sequence coordination between SSC and pole control system, the handling way for the stability control device of Mawo converter station and control characteristics of DC power modulation for pole control system are analyzed in detail. An optimization method of control sequence coordination for stability control system and pole control system is proposed, and tested on the SSC test platform. Test results show the effectiveness of the proposed method, and meanwhile the significance for the design and implementation of SSC scheme of HVDC project with similar techniques. © 2020 IEEE.

关键词： Electric power transmission networks

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Bi-Level Joint Planning of Transmission Network and Energy Storage system to Enhance Flexibility

Bi-Level Joint Planning of Transmission Network and Energy S...

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power Energy systems and Applications (ICoPESA), International Conference on

作者： Xiangfei Meng Yi Sun Shihao Zou Yongji Cao Hengxu Zhang Rui Liu Xian Wang Yao Wang Economic and Technological Research Institute of State Grid Shandong Electric Power Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China Academy of Intelligent Innovation Shandong University Jinan China

The high proportion of renewable energies make the operation state of the power system more complicated. This paper proposes a bi-level joint planning approach considering the flexibility of the power system, which proposes for the transmission network and energy storage. By building a network flexibility index and adding a flexibility objective function, a bi-level optimization model is established to improve the power system flexibility and reducing investment cost. In addition, the outer model is solved by the snake optimization algorithm. The analysis results show that the joint planning model can effectively improve the flexibility of power system, and has economic advantages compared with the transmission lines expansion.

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Deep Sequence to Sequence Attention Network Approach for Short-Term Electricity Price Forecasting

SSRN

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SSRN 2022年

作者： Chen, Haipeng Wu, Huayue Chen, Jian Kan, Tianyang Miao, Sen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Chengde Power Supply Company State Grid Jibei Electric Power Co. Ltd. Chengde067000 China

With increasing renewable energy penetration and the extension of the power system scale, accurate electricity price prediction has long become a challenging task. The complexity of electricity market information, insufficient model training, and the lack of model generalization ability limit the prediction accuracy of the existing electricity price forecasting methods. This paper proposes a Sequence-to-Sequence Attention algorithm based on the Double Deep Q Network optimization method for short-term electricity price prediction. This algorithm first conducts the maximum information coefficient correlation analysis to select the input dataset for the prediction model of electricity price. Secondly, the Sequence-to-Sequence Attention model is proposed for point forecasting and probabilistic forecasting of short-term electricity prices. Finally, the hyperparameters of the model are optimized by the Double Deep Q Network method, which improves the prediction accuracy and the generalization ability of the prediction model of electricity price. simulations are carried out on Pennsylvania–New Jersey–Maryland market data and the New South Wales electricity market. The results show that the proposed method has superior performance than the existing prediction methods, which is appliable for the short-term electricity price prediction and provides a powerful decision-making basis for market participants and regulators. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Boosting Conversion Efficiency by Bandgap Engineering of Eco-Friendly Antimony Trisulfide Indoor Photovoltaics

SSRN

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SSRN 2024年

作者： Cao, Yu Li, Qiang Zhou, Jing Yu, Xiaoming Yu, Xuan Wang, Sanlong Li, Sen Pang, Jinbo Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Electrical Engineering School of Chemical Engineering Northeast Electric Power University Jilin132012 China School of Marine Engineering Equipment Zhejiang Ocean University Zhejiang Zhoushan316022 China Zhengzhou University of Light Industry China University of Jinan Shandong Jinan250022 China

With the exponential growth of the Internet of Things (IoT), indoor photovoltaics (IPVs) drawing heightened interest due to their adaptability to indoor environments. The Photovoltaic Conversion Efficiency (PCE) of IPV cells is critically dependent on their ability to match the indoor spectrum with the device's response characteristics. In this realm, Antimony Trisulfide (Sb2S3) emerges as a promising candidate for low-light applications. Our study focuses on the design and optimization of Sb2S3 thin-film IPV cells, aiming to refine both the device structure and its photoelectric performance for effective indoor light harvesting. In a strategic shift from conventional CdS materials, we utilized SnO2 as the electron transport layer (ETL) in Sb2S3 IPV cells. This substitution notably enhanced the short-wave response, elevating the spectral response from 45% to 80% at 400 nm. Additionally, we introduced a bandgap-tunable ZnOS buffer layer. This innovation proved instrumental in rectifying the band alignment mismatch between SnO2 and Sb2S3 layers, thereby optimizing interfacial electron transport properties. The resulting optimized Sb2S3 thin-film IPV cell demonstrated exceptional response characteristics across the full visible spectrum (400–750 nm) and showed notable photoelectric performance under both fluorescent lamps (FLs) and light-emitting diodes (LEDs). The device achieved a peak theoretical efficiency of 46.25% under cold white FL lighting, a testament to the optimal match between the device structure and this specific emission power spectrum. This research not only underscores the feasibility of employing antimony (Sb) -based photovoltaic technologies in indoor settings but also offers theoretical guidance for further advancements in this domain. © 2024, The Authors. All rights reserved.

关键词： Antimony compounds

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Coordinated control Strategy of Grid-Connected Photovoltaic Generation system

Coordinated Control Strategy of Grid-Connected Photovoltaic ...

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power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Jian Zhang Hengxu Zhang Yongji Cao Xiaoming Liu Shihao Zou Ruicong Ma Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China Academy of Intelligent Innovation Shandong University Jinan China State Grid Shandong Electric Power Company Economic and Technological Research Institute Jinan China

The development and utilization of renewable energy is imperative, and photovoltaic power generation has broad development prospects. However, photovoltaic power generation systems are limited by many external conditions, which bring more problems to power output and grid connected power quality. Therefore, in order to make better use of photovoltaic power generation, this paper proposes a coordinated control strategy of the grid-connected photovoltaic generation system. The operating principle, mathematical model, and output characteristics of photovoltaic cells are analyzed. Then, the MPPT algorithm and the operating principle of DC/DC converters are studied. Moreover, the structure and control strategy of grid connected inverters are analyzed. A simulation model of a three-phase photovoltaic grid-connected inverter system is established, and a control strategy based on grid voltage orientation is adopted. A case study is conducted to verify the proposed control strategy.

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Improving Surface Performance of Composite Insulators by Multifunctional Nano-Coating

SSRN

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SSRN 2022年

作者： Liu, Shuming Liu, Shuqi Wang, Qian Zuo, Zhou Liang, Xidong Chen, Zhong State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China School of Material Science and Engineering Nanyang Technological University 639798 Singapore

Insulator is an important component of outdoor insulation systems. It works in complex environments and faces a series of problems such as surface contamination, loss of hydrophobicity, accumulation of surface charges, which may eventually lead to flashover. Aiming to solve these problems in a simple and scalable method, we propose a multifunctional nano-coating based on SiO 2 /PDMS/EP. By the combination of multiscale structure and low surface energy modification, this coating shows excellent water-repellency performance, a large contact angle greater than 160° and a small rolling angle near 0° are achieved on coated silicone rubber, which enables the surface with excellent self-cleaning performance. Benefiting from the multiscale roughness, the heat transfer is inhibited by the reduced contact area, which further slows down the icing process on coated silicone rubber. The de-icing force on coated silicone rubber is also smaller than that on pristine silicone rubber. This coating also introduces many shallow traps to the surface, which is beneficial to the fast surface charge dissipation in a short time. Besides, the high water-repelling ability can promote the droplet to roll away under the action of electrical field force, which alleviates the electrical distortion caused by the droplets. As a result, the wet flashover voltage of coated silicone rubber is 60% higher than that of pristine silicone rubber. This multifunctional coating is a facile, scalable, and effective approach to improving the comprehensive surface performance of insulators. © 2022, The Authors. All rights reserved.

关键词： Flashover

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Short-term power Prediction Based on Improved NWP Data Considering Wind Speed Distribution Inner Wind Farm

Short-term Power Prediction Based on Improved NWP Data Consi...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Wei Ma Ying Qiao ZongXiang Lu ZhiFeng Liang JiaMing Li YuChen Hao State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments of Electrical Engineering Tsinghua University Beijing China State Grid Corporation of China Beijing China State Grid Jiangsu Power Grid Corporation Nanjing China

The wind farm has only one NWP data point, which cannot accurately reflect the wind resources inside the wind farm. To solve the problem of a poor match between short-term wind power prediction numerical weather forecast and wind farm measured weather. Put forward a kind of based on clustering and temporal characteristics of the modified liters of scale wind power prediction method, first of all, using EOF orthogonal decomposition method to extract the features of each fan, using hierarchical analysis method of the characteristics of the data matrix clustering, and then, each cluster sample selected representative of a fan, using the error transfer characteristic of the NWP data sequence is modified. Finally, The CNN prediction model is used for power prediction, and the weight is determined according to the cluster sample size. A wind farm in northern Hebei, China, is used to verify the effectiveness of the proposed method.

关键词： Fans Wind speed system integration Wind farms Wind power generation Predictive models Feature extraction

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