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Prediction for power Supply Area Interrupted by Heavy Rainfall in Guangxi power grid 6

Prediction for Power Supply Area Interrupted by Heavy Rainfa...

引用

6th International Conference on Energy, Electrical and power Engineering, CEEPE 2023

作者： Zhang, Wei Li, Shan Feng, Bo Wang, Longjun Luo, Xiaoli Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi Power Grid Equipment Monitoring and Diagnosis Engineering Technology Research Center Guangxi Nanning China Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi Key Laboratory of Intelligent Control and Operation and Maintenance of Power Equipment Guangxi Nanning China School of Electrical Power Engineering South China University of Technology Guangdong Guangzhou China Guangxi Climate Center Guangxi Nanning China

ISBN: (纸本)9798350348279

In view of the problem of flood disaster caused by heavy rainfall, which affected power supply reliability, a dynamic prediction approach for power supply area interrupted by heavy rainfall is proposed. First, under the spatial correlation rules, the power supply area within the surface rainfall boundary is judged by multidimensional data, such as digital geography. Secondly, with the distribution transformer of the power supply area as the geometric center, the area and the scale of power outage users are solved. Thirdly, the supervised learning algorithm is used to predict the disaster area out of service, on the basis of the binary classification of prior data. Finally, the dynamic changes of the predicted results are calculated according to the intensity changes of the rainfall process. Using the confusion matrix and the receiver operation characteristic curve to verify the prediction method. The numerical results show that this approach can dynamically predict the power supply area interrupted by heavy rainfall, which can be used for the production command in the process of 'water rise and power failure, water back and power recovery'. © 2023 IEEE.

关键词： Forecasting

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Application of deep reinforcement learning in power system scheduling strategy simulation

Application of deep reinforcement learning in power system s...

引用

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

作者： Binjiang Hu Libin Huang Yufeng Hu Jie Zhang Haiping Guo Yuanhong Lu Zeqi Hong Jingyue Zhang Hui Luo Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China 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 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 increasing scale and complexity of power system, the current scheduling strategy methods often have the problems of limited adaptability and unsatisfactory results. Therefore, a power system scheduling method using DQN (Deep Q Network) is introduced in this study. This method uses neural network model to learn and optimize scheduling strategy, and has strong adaptability and enhanced global optimization ability. Comparative evaluations with genetic algorithms and particle swarm algorithms on IEEE-118 and SG-126 datasets show that the proposed method has significant advantages in terms of cumulative reward and stable runtime.

关键词： Electrical engineering Runtime Neural networks power system stability Deep reinforcement learning Stability analysis Real-time systems Particle swarm optimization Optimization Genetic algorithms

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Technical Research on Simulation and Predictive Analysis of Wind Farms in power Systems

Technical Research on Simulation and Predictive Analysis of ...

引用

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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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...

引用

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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Proof-of- Reliability Consensus Mechanism of Blockchain for Remote operation of Relay protection

Proof-of- Reliability Consensus Mechanism of Blockchain for ...

引用

China International Electrical and Energy Conference (CIEEC)

作者： Yongjie Zhang Wei Cong Yi Tang Yudun Li Congcong Li Fangfang Shi Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Remote operation of protective setting value can enhance the adaptability of protection to real-time operating conditions. As centralized solutions, existing models face information security risks. The decentralized nature of blockchain is advantageous for addressing reliability issues in remote operations. This paper treats the control center and protection relay system sub-stations as nodes in blockchain, proposing a Proof-of- Reliability (PoRel) consensus mechanism based on node reliability. The paper first defines sub-station node types and classification criteria, achieving optimal sub-station node grouping based on 0–1 integer programming. Next, it provides a method for evaluating sub-station node reliability and outlines the consensus mechanism process. Finally, using the IEEE 39-bus system as an example, a blockchain system with 27 sub-station nodes is built using Python software, and the proposed consensus mechanism is simulated and verified. The results indicate that the proposed consensus mechanism improves efficiency, achieves decentralization, and helps ensure reliable relay setting remote operations.

关键词： Integer programming Protective relaying Information security Software Real-time systems Consensus protocol Software reliability

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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...

引用

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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DC Line Traveling Wave protection Method Based on Improved Hausdorff Distance Comparison

DC Line Traveling Wave Protection Method Based on Improved H...

引用

China International Electrical and Energy Conference (CIEEC)

作者： Chengyan Diao Wei Cong Xiang Lin Xiaofei Shao Yudun Li Congcong Li Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

To improve the reliability of conventional protection using fault travelling wave, this paper proposes a line traveling wave protection method based on improved Hausdorff distance comparison. The traveling waves at the two sides of the line have different propagation laws and change characteristics during external fault and internal fault. First, the traveling waveform is processed by time matching and segmentation calculation. Second, splitting the long waveform into short waveforms and averaging the waveforms at both sides of the block processing, thus calculating the average Hausdorff distance. Last, using PSCAD building a bipolar DC transmission system for simulation verification, the results show that the improved method has high reliability.

关键词： Fault diagnosis power transmission lines Simulation Voltage Capacitance Reliability engineering Reflection

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Designing and Implementing Communication-efficient Model of Distributed System for Real-time Electromagnetic Transient Simulation

Designing and Implementing Communication-efficient Model of ...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Qi Guo Binjiang Hu Zeqi Hong Yanjun Zhao Shuyong Li Liang Tu State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid China Southern Power Grid Co. Ltd

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

This paper introduces CM-RETS, a 4-Level address Communication Model designed specifically for real-time simulation of electromagnetic transients in power systems. CM-RETS incorporates a unique 4-Level address scheme and leverages Remote Direct Memory Access (RDMA) technology to enable efficient and reliable data exchanges between computing nodes in a distributed system. By effectively distributing computational load, CM-RETS facilitates faster and more realistic simulations, ensuring precise and timely data exchanges within a 5μs time step to capture transients in real time. Experimental results validate the effectiveness and efficiency of CM-RETS in achieving accurate and real-time simulations of electromagnetic transients. With its robust and cost-effective solution, CM-RETS provides researchers and engineers with the means to perform detailed analysis and studies on power systems under various transient conditions.

关键词： Computational modeling Memory management Real-time systems Data models power system reliability Reliability Security

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High-performance Full Electromagnetic Transient Simulation System of Fujian power grid and its Application 5

High-performance Full Electromagnetic Transient Simulation S...

引用

5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Li, Zhicheng Tang, Zhijun Zhang, Dongyuan Chao, Wujie Peng, Xiaoyan Leng, Yongjie Electric Power Research Institute State Grid Fujian Electric Power Co. Ltd Fujian Key Laboratory of Smart Grid Protection and Operation Control Fujian China Electric Power Research Institute State Grid Fujian Electric Power Co. Ltd Fujian Key Laboratory of Smart Grid Protection and Operation Control Fuzhou China Integrated Electronic Systems Lab Co. Ltd. Jinan China

ISBN: (纸本)9781665434256

This paper proposes a high-performance full electromagnetic transient simulation system of Fujian power grid. The system is developed based on the General Transient Simulator (GTS), which is a self-developed electromagnetic transient simulation tool. The key technologies of the simulation system, such as the high-performance automatic parallel computing technology and the large time step electromagnetic transient simulation technology, are introduced. The full electromagnetic transient simulation of the AC power system above lOkV in Fujian power grid based on the dispatching control cloud platform is realized. Study cases are carried out and the validity and accuracy of the simulation are verified by comparing the actual fault recording data and simulation waveforms. © 2021 IEEE

关键词： Transients

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Prediction of Distributed Photovoltaic Accommodation Capability Based on Ridge Regression 9th

Prediction of Distributed Photovoltaic Accommodation Capabil...

引用

9th Purple Mountain Form on Smart grid protection and control, PMF 2024

作者： Ji, Xingquan Zhang, Bowen Zhang, Yumin Sun, Pengkai Zhang, Xiaofeng Liu, Wei dong Shandong University of Science and Technology Qingdao266590 China Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan250061 China Information and Communication Company State Grid Shandong Electric Power Company Jinan250013 China Qingdao Yihe Electric Group Co. Ltd. Qingdao266510 China

ISBN: (纸本)9789819616701

A prediction method of distributed photovoltaic accommodation capability is proposed based on ridge regression. First, the factors influencing the distributed photovoltaic accommodation capacity are analyzed, and the concept of the contribution degree of distributed photovoltaic accommodation is introduced. The gray relation analysis method is used to evaluate the correlation between different factors and distributed photovoltaic accommodation, and a model of factors affecting distributed photovoltaic accommodation is constructed. Then, a prediction model of distributed photovoltaic accommodation capability is established based on ridge regression. The factors with high photovoltaic accommodation contribution degrees are incorporated in the prediction model. The mapping relationship between the driving factors of distributed photovoltaic accommodation and the prediction model is derived, then the development strategies for future photovoltaic accommodation capacity are formulated. Finally, simulation is performed on the SPSSPRO platform, and the results show that the proposed method can predict the distributed photovoltaic accommodation capability accurately and provide strategic suggestions for improving future photovoltaic accommodation capacity. © state grid Electric power 2025.

关键词： Photovoltaics

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