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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Tianyu Guo Libin Huang Yanjun Zhao Haiping Guo Yuanhong Lu Jie Zhang Hui Luo Binjiang Hu National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

To address the issue of insufficient accuracy in wind power output forecasting, this paper proposes a Support Vector Machine (VSM) model based on similar day matching and kernel entropy component analysis for wind power prediction. The model effectively extracts similar day features through the similar day approach and optimizes feature representation by combining it with kernel entropy component analysis (KECA), ultimately employing the SVM model for wind farm power forecasting. simulation results demonstrate that using this model for optimizing wind power generation predictions enhances the accuracy of wind power forecasts, surpassing the precision of single-method predictions. The Similar Day-KECA-SVM method leverages historical data from similar days and features extracted through KECA, with SVM performing the final prediction. This approach adeptly handles complex patterns and nonlinear relationships in wind power forecasting, thereby achieving superior predictive performance.

关键词： Support vector machines Analytical models Accuracy Wind speed Wind power generation Predictive models Feature extraction Entropy Forecasting Kernel

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Research on the Application of Big Data in Transient Stability simulation of power systems

Research on the Application of Big Data in Transient Stabili...

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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Jie Zhang Haiping Guo Yanjun Zhao Libin Huang Binjiang Hu Hui Luo Jingyue Zhang State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

With the rapid expansion of smart grids, there is a growing complexity in power system dynamics, which increases the availability of real-time operational data and underscores the need for advanced transient stability simulations. Traditional transient stability analysis methods are constrained by their computational demands and may compromise accuracy. Furthermore, deep learning-based methods often focus on the application of a single algorithm, lacking a systematic consideration of the entire simulation process. To address these challenges, this study proposes a novel approach that leverages big data to enhance transient stability simulations. By integrating deep learning and ensemble learning techniques, the model significantly improves the accuracy and efficiency of predicting transient instabilities. The methodology utilizes a multi-source data acquisition and fusion scheme to ensure data comprehensiveness and a distributed storage architecture to handle large-scale datasets effectively. Advanced visualization technologies are also employed to enhance the interpretability of the simulation results. This comprehensive framework aims to support the safe and stable operation of power systems amid increasing complexity and data availability.

关键词： Accuracy Computational modeling power system stability Big Data Predictive models Stability analysis Data models Real-time systems Transient analysis Thermal stability

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Neural Network technology Applied in Fault Detection and simulation of power systems

Neural Network Technology Applied in Fault Detection and Sim...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Zeqi Hong Libin Huang Yufeng Hu Haiping Guo Yuanhong Lu Binjiang Hu Jingyue Zhang Hui Luo State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

With the escalating demand for power inspection and equipment maintenance, developing effective algorithms for power system fault detection is crucial for ensuring the safety and stability of power systems. This paper leveragesthe Trellis network, enhanced with an attention module, to significantly improve the Trellis network’s capability in recognizing power fault images, thus better addressing fault identification tasks within power systems. Through testing on a self-constructed dataset and comparative experiments with models such as DCNN, RCNN, and DenseNet, the proposed attention mechanismenhanced Trellis network algorithm has demonstrated its effectiveness and superiority in fault detection.

关键词： Fault diagnosis Accuracy Attention mechanisms Fault detection Neural networks power system stability Predictive models Feature extraction Electrical fault detection Maintenance

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Distributed power system simulation and Monitoring Supported by Cloud technology : A grid optimization scheduling method based on MADRL

Distributed Power System Simulation and Monitoring Supported...

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energy, power and Electrical Engineering (EPEE), International Conference on

作者： Hui Luo Jie Zhang Yufeng Hu Jingyue Zhang Haiping Guo Libin Huang China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou China China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&D Centre Guangzhou China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

This study explores the optimization of Distributed Generation systems (DGS) within a Multi-Agent Deep Reinforcement Learning (MADRL) framework. The focus is on transforming the overall optimization goal of the system into a global reward function and designing local rewards adapted to heterogeneous agents. Using the IEEE 33 node system as a case study, the impact of power aggregation and load management from photovoltaic systems, energy storage systems, electric vehicle charging facilities, and HVAC systems is analyzed. The results demonstrate that the proposed edge-cloud collaborative framework optimizes resource allocation and scheduling. The cloud platform is capable of integrating data from diverse sources and managing it in a unified manner, thereby coordinating the operations of multiple heterogeneous agents. The scalability and flexibility of cloud technology allow for the dynamic adjustment of computational resources according to demand, ensuring global optimization and the judicious allocation of resources.

关键词： Photovoltaic systems HVAC Processor scheduling system performance Collaboration Deep reinforcement learning Smart grids Resource management Vehicle dynamics energy storage

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Capacity and power Allocation Strategy of energy Storage Participating in Grid Peak Shaving 5

Capacity and Power Allocation Strategy of Energy Storage Par...

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5th IEEE Conference on energy Internet and energy system Integration, EI2 2021

作者： Qi, Tengyun Xu, Fei Wu, Qiren Li, Yajing Liu, Jianping Li, He Wang, Yiming Li, Cuiping Li, Junhui China Three Gorges Corporation Beijing China Haidian District Beijing China Co. Ltd Beijing101100 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China

ISBN: (纸本)9781665434256

High penetration wind power grid with energy storage system can effectively improve peak load regulation pressure and increase wind power capacity. In this paper, a capacity allocation method of energy storage system under peak load regulation scenario is proposed. The upper model combines the investment cost, operation cost, arbitrage income, environmental income, and wind power grid benefits during the entire life cycle of the energy storage system, with the goal of maximizing the net income during the energy storage life cycle;the lower model optimizes the charging and discharging power of the energy storage system according to each energy storage configuration scheme in the upper modeling, and its objective is to minimize the standard deviation of system net load and waste wind power. Finally, under the conditions of local grid load and wind power data, verified the effectiveness of capacity allocation strategy. © 2021 IEEE

关键词： Economic analysis

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Robust Voltage control Considering Uncertainties of renewable Energies and Loads via Improved Generative Adversarial Network

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Journal of modern power systems and Clean energy 2020年第6期8卷 1104-1114页

作者： Qianyu Zhao Wenlong Liao Shouxiang Wang Jayakrishnan Radhakrishna Pillai the Key Laboratory of Smart Grid of Ministry of Education and Tianjin Key Laboratory of Power System Simulation and Control Tianjin UniversityTianjinChina the Department of Energy Technology Aalborg UniversityAalborgDenmark

The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution *** this paper,a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network(IGAN).Firstly,both real and predicted data are used to train the IGAN consisting of a discriminator and a *** noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario ***,a new improved wolf pack algorithm(IWPA)is presented to solve the formulated robust voltage control model,since the accuracy of the solutions obtained by traditional methods is *** simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads,which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional ***,IWPA has a better performance than traditional methods in terms of convergence speed,accuracy,and stability for robust voltage control.

关键词： Robust voltage control uncertainty generative adversarial network wolf pack algorithm

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Remote Sensing Image Object Detection Based on Improved Sparse R-CNN

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1303-1320页

作者： Zhao, Li-Quan Chen, Chun-Lu Zhong, Tie Cui, Ying Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China School of Electric Power Engineering Northeast Electric Power University Jilin 132012 China Zhuhai Power Supply Bureau Guangdong Electric Power Corporation Zhuhai 519000 China School of Electrical and Information Engineering Beihua University Jilin 132013 China

A more precise sparse region convolutional neural network algorithm is developed to increase the accuracy of remote sensing object recognition. Firstly, a new intersection over union method is proposed to solve the problem that the intersection over union used in sparse region convolutional neural network cannot well measure the offset between the ground-truth box and the proposal box. It introduces multiple geo-metric factors that are overlap area, center point distance, vertex distance between the ground-truth box and the proposal box, and diagonal distance of minimum enclosing of the ground-truth box and the proposal box and into the new intersection over union. The proposed intersection over union can measure the offset between the ground-truth box and the proposal box to optimal the proposal box. Secondly, to improve the detection precision of sparse region convolutional neural network, a dual branches dynamic instance interaction head is designed. The proposed dynamic interaction head consists of a fully connected branch and a convolution branch. The fully connected head is used for both bounding box regression and object classification. The convolution branch is only used for bounding box regression. Compared with original sparse region convolutional neural network, the average mAP and recall are 3.55% and 5.75% higher, and the average detection speed is only 0.85 FPS slower. The proposed method realizes balance between detection precision and detection speed. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Remote sensing

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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on energy Storage system

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Shijiazhuang Gaocheng District Electric Supply Company No. 76 Changsheng South Street Gaocheng District Shijiazhuang052160 China

Large wind farms connected to the grid through MMC-HVDC systems can cause overloading of the non-faulty converter station after a DC fault. This short communication proposes a system fault ride-through method by coordinating and controlling the energy storage system, the non-faulty converter station and the wind farm to jointly consume the unbalanced power during the fault. The model is created in the PSCAD simulation platform, and the correctness and effectiveness of the method are verified by simulation. © 2023, The Authors. All rights reserved.

关键词： energy storage

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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on energy Storage system

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control& Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China State GridShijiazhuang Gaocheng District Electric Supply Company No. 76Changsheng SouthStreet Gaocheng District Shijiazhuang052160 China

关键词： energy storage

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A Novel DC Fault Ride-Through Coordinated control Strategy for DC Grid with Hybrid MMC

SSRN

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SSRN 2022年

作者： Jiang, Shouqi Li, Guoqing Wang, Xiaoxue Xin, Yechun Wang, Weiru Wang, Yanxu Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132000 China

DC grid based on hybrid modular multilevel converter (MMC) is an effective means to achieve large-scale renewable energy integration, multi-energy complementarity and friendly grid-connection. Aiming at the problem that the traditional DC fault ride-through (FRT) scheme will cause the interruption of power transmission in the whole network, a novel DC FRT coordinated control strategy is proposed in this paper, which can quickly interrupt the fault current and avoid the MMC exiting operation. Based on the analysis of fault current composition and key influencing factors, for fault line MMC (FLMMC), a negative voltage control strategy adaptive to the change of fault current is proposed, which can improve the current attenuation speed and ensure reliable fault isolation. For non-fault line MMC (NFLMMC), considering the overvoltage and overcurrent risks of FLMMC, the coordinated current-limiting control method and parameter selection principle of NFLMMC is proposed, where the current-limiting performance can be adaptive to the voltage change of DC bus, so as to realize the rapid recovery of DC power grid while restraining fault current. Finally, a four-terminal DC grid real-time digital simulation model is built to verify the validity of the proposed FRT coordinated control strategies. © 2022, The Authors. All rights reserved.

关键词： Voltage control

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