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检索条件"机构=Grid Laboratory of Grid Advanced Computing and Applications"

共 144 条记录，以下是11-20 订阅

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Online Fine-tuning Method for Power grid Artificial Intelligence Model Based on Cloud-Edge Collaboration 3

Online Fine-tuning Method for Power Grid Artificial Intellig...

引用

3rd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Du, Zexu Zhang, Guoliang Zhang, Yi Wei, Wei Chu, Zongbo State Grid Smart Grid Research Institute Co.ltd State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350360813

As artificial intelligence methods play an increasingly important role in the digital transformation of power grids, when applied in power grid scenarios, these methods face problems such as the inability of models to adaptively deploy data distribution changes, slow response speed, inability to ensure data security on the side, limited computing power on the side, and uneven distribution of computing resources. This method proposes a novel fine-tuning framework based on cloud-edge collaboration to address these issues. This method utilizes reinforcement learning to segment the model, discarding unreliable samples collected on-site to fine-tune the model's fine-tuning parameters. Finally, validation was conducted on the application scenario of detecting violations in the power grid, and the experimental results show the effectiveness of our method. © 2023 IEEE.

关键词： Reinforcement learning

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A Method for Defects Inspection of Power Transmission Lines Based on DETA and Integrated Improvement Algorithm 11

A Method for Defects Inspection of Power Transmission Lines ...

引用

11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Wang, Jin Wang, Bo Wu, Peng Zhang, Guoliang Chen, Jiangqi Zhang, Xi State Grid Smart Grid Research Institute Co.Ltd State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350333664

Collecting images through drone and helicopter aerial photography is one of the main methods for transmission line inspection. Currently, the popular intelligent transmission defects recognition is mainly based on deep neural network object detection algorithms. Due to the outstanding performance in various public datasets and rapid development of DETR algorithms, there is a promising application prospect in the field of transmission inspection. This article proposes a transmission line inspection model based on DETA, which is optimized by integrated improvement algorithm, which inlcudes SNIP sampling algorithm, multi-scale inference strategy, and prior knowledge based box-selection algorithm. Experiments have shown that the method proposed in this article has the advantages of accurate defect localization, high detection accuracy, and strong scalability. © 2023 IEEE.

关键词： Deep Learning defect recognition DETA DETR object detection transmission inspection

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AXIM: A Programmable Simulation Environment for FPGA Kernels with AXI Interfaces 7

AXIM: A Programmable Simulation Environment for FPGA Kernels...

引用

7th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Lin, Long Shi, Cuncun Yang, He State Grid Smart Grid Research Institute Co. Ltd State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350334197

In the recent decade, the development of FPGA kernels raises its level of abstraction from RTL to HLS C or OpenCL. And so are the simulation and debugging software suites. However, it makes some existing high-quality RTL simulators incompatible to use. Therefore, we propose AXIM, a programmable simulation environment that integrates existing RTL simulators for FPGA kernels with interfaces at a high-level abstraction via AXI buses. AXIM consists three modules, including AXIM hardware driver, AXIM runtime library, and AXIM debugger. Experimental results show that AXIM runs faster than the one integrated with Xilinx Vitis. Besides, another experiment demonstrates that visualization analysis of DRAM's activities generated by AXIM can help developers optimize the design of kernels. As a side product, we also identify an inconsistent behavior in hardware emulation in Xilinx Vitis 2019.2 about data transfer using AXI interfaces. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Analysis and Optimization of Sparse LU factorization of Power Matrix Based on GLU 12

Analysis and Optimization of Sparse LU factorization of Powe...

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12th International Conference on Renewable Power Generation, RPG 2023

作者： Shi, Cuncun Lin, Long Yang, He Wang, Shucai State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd Beijing China

With the continuous improvement of power grid stability and reliability requirements, improving the efficiency and accuracy of power system simulation has become an important research topic. The sparse LU factorization is one of the key modules of the sparse direct solver, which usually plays a dominant role in the solving time of the sparse linear algebra equation of power system. In order to improve the efficiency of sparse LU factorization, we propose: 1) an adaptive mode partition strategy based on GLU, and select the mode to be used according to the characteristics of the matrix to be decomposed, which improves the computational efficiency of LU factorization;2) a dynamic Nstream value taking method for the stream pattern, which determines the number of streams to be used based on the dimensions of the sub-matrix, 3) a pipeline model for the continuous layer with the size of 1 in GLU, which improves the parallelism of sparse LU factorization and helps to improve the computing efficiency. The results show that the proposed improvement can achieve better results, and the speed of LU factorization is increased by more than 30% on rajatl2 and circuit_2. © The Institution of Engineering & Technology 2023.

关键词： Computational efficiency

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Research on communication mechanism optimization based on distributed graph computing environment 4

Research on communication mechanism optimization based on di...

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4th International Conference on Big Data Economy and Information Management, BDEIM 2023

作者： Lin, Jianchao Cao, Chunna Ren, Chunhui State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd Beijing China

ISBN: (纸本)9798400716669

In recent years, with the rapid development of the Internet of Things, the Internet, and social networks, the storage of data in the network is growing at an explosive rate and is becoming more and more closely related to the real natural world. Driven by large-scale data mining and machine learning applications, distributed graph computing models that use graph data structures to describe data and relationships between data have been increasingly widely used. Therefore, this paper studies the optimization of communication mechanisms based on a distributed graph computing environment. Firstly, a BSP model based on a pure message-passing communication mechanism of a distributed graph computing system is established. Secondly, the optimization model is evaluated from two aspects: data communication and convergence condition judgment. Finally, large-scale data sets are used to test and evaluate performance optimization. The results show that this method can greatly improve the efficiency of graph parallel computing. © 2023 ACM.

关键词： BSP model Distributed Graph Computation Distributed system Graph processing Parallel computing

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A CBL Estimation Method for Industrial Customers Based on K-Means and LSTM 11

A CBL Estimation Method for Industrial Customers Based on K-...

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11th China International Conference on Electricity Distribution, CICED 2024

作者： Zhang, Wensi Lei, Shuya Liu, Mohan Wei, Renjie Wang, Yishen State Grid Laboratory of Grid Advanced Computing and Applications China Electric Power Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350368345

In pursuit of attaining the objective of 'carbon peaking and carbon neutrality', the structure of energy in China has undergone a great transformation. With the rapid development of renewable energy generation, the power grid has been confronted with new challenges. The reliability and flexibility of the grid have been affected. Demand response (DR), as an effective management method, has become a hotspot. Customer baseline load (CBL) serves as the standard for calculating economic compensation in DR. Hence, accurate estimation of CBL is a crucial aspect in ensuring the effective implementation of DR strategies. This paper proposes a CBL estimation method for industrial customers based on the K-means and long short-term memory (LSTM). First, the K-means method is used to cluster the daily load curves of various industries to yield the typical patterns of electricity consumption behaviors. Second, the LSTM neural network is established to estimate CBL. It is trained with historical load data of different industries and clustering results. Finally, the effectiveness of the novel method is validated via a real data set. © 2024 IEEE.

关键词： Sales

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Information Semantic Mining Method for Risk Warning Scenarios of Electric Power Field Operations 3

Information Semantic Mining Method for Risk Warning Scenario...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Chu, Zongbo Wei, Wei Zhang, Yi Du, Zexu State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd Beijing102209 China

ISBN: (纸本)9798400716775

Risk management and control of on-site power operations is an important link in the power production process. Traditional methods that rely on manual execution of safety rules lack proactive prevention and control capabilities, and insufficient efficiency leads to an increasing frequency of safety issues in power production. Therefore, it is urgent to adopt intelligent methods to improve the level of safety risk management and control. In response to this issue, this paper proposes a multimodal causal knowledge graph based auxiliary decision-making method for safety risk management in power field operations. Based on the characteristics and requirements of safety risk management for on-site power operations, first obtain multimodal data related to safety risk management for on-site power operations, including video, photo, and text information collected by end devices, as well as historical archive data in various system databases. Then, based on historical risk management rules, the causal relationships of the core elements of on-site work safety issues are sorted out, and the data involved in the multiple types of antecedent influencing factors of historical faults and safety accidents are taken as the causes, and the faults and safety issues are taken as the results, forming a complete causal knowledge sample and constructing a causal effect estimation learning model. This model can output the safety risks of on-site power operations, forming a causal knowledge graph to provide auxiliary support for early warning and pre control decision-making of safety risks in on-site power operations. © 2023 ACM.

关键词： Risk management

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Topology Identification of Distribution Networks Using Multi-Object Binary Classification Graph Transformer 7

Topology Identification of Distribution Networks Using Multi...

引用

7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Liu, Siyan Shi, Danni State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350345094

With the continuous increase of renewable energy, the distribution network's structure tends to be more complex. The real-time topology is difficult to obtain timely and the topol-ogy categories increases exponentially, which poses challenges to further analysis and the network's computation. Traditional distribution network topology identification methods rely on high computational complexity and require a large number of fault samples. To address these problems, this paper proposed a Multi-Object(MO) binary classification graph transformer method for topology identification. Through mapping the switch status and voltage measurement and branch conductance of the distribution network, a MO classification mechanism is introduced, performing binary classification for each edge with undetermined connection status, and physically map the output of the identification model to the switches status in the distribution network. The method utilized a Graph Transformer-based convolutional neural network which used to extract the nodes and the edges features. Combing the proposed architecture with an online hard example mining mechanism and maximum spanning tree filter strategy, the model collapse caused by imbalanced data is dealt with and the accurate identification is realized. The proposed method is validated by the IEEE 33-bus distribution network system, and the results show the high accuracy for practical topology identification. © 2023 IEEE.

关键词： Trees (mathematics)

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An Active Learning Model Production Method for Electric Power Inspection Image Multi-target Detection 7

An Active Learning Model Production Method for Electric Powe...

引用

7th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Chen, Jiangqi Wang, Bo Zhang, Xi Gao, Kunlun Wu, Peng State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Smart Grid Research Institute Co. Ltd Beijing China

ISBN: (纸本)9798350334197

With the development of intelligent electric power inspection technology, electric power companies use UAVs, cameras, robots and other equipment for power transmission line inspections, and take a large number of inspection images. In recent years, the image multi-target detection model has been maturely applied in the intelligent inspection of electric power. The current image intelligent detection model mainly relies on a large number of image sample labels. Power companies have a small number of labeled image samples, and a large number of unlabeled. But manual data annotation is very expensive, and cost lots of time and money. Moreover, there will be certain errors in manual annotation, so it is necessary to repeat the annotation and verification to reduce the error. But this will also increase the cost. This article proposes an active learning model production method for electric power inspection image multi-target detection. We select samples for annotation according to some strategies, and select labeled samples for verification. This method can reduce the cost of sample annotation, and improve the model. productivity. Compared with the traditional random labeling strategy, our method can achieve the same accuracy with only 20% of the annotation cost. With 50% of the annotation cost, the mAP index will increase by 12%. © 2023 IEEE.

关键词： Inspection

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Carbon Emission Flow Analysis of New Power System Considering the Uncertainty of New Energy Sources 8

Carbon Emission Flow Analysis of New Power System Considerin...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Tang, Zhuang Chunhui, Ren Danni, Shi Jianchao, Lin Xinghua, Shi State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China State Grid Zhejiang Eletric Power Co Ltd Zhejiang310007 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The core feature of the new power system is that new energy replaces traditional thermal power as the main power source of the power system, and the basic development positioning is clean, low-carbon, safe and efficient. To analyze the low-carbon contribution of photovoltaic power generation to the power system, a method for analyzing the impact of uncertainty in photovoltaic injection power on the carbon emission flow of the power system is proposed. Firstly, by modeling the output of photovoltaic units and the power of system nodes, a functional expression between light intensity and the total carbon flow rate of system nodes is established. Secondly, the randomness of photovoltaic output is transferred to the balanced unit. Based on the carbon flow rate correlation matrix between the balanced unit and the system node branch, the impact factor of photovoltaic injection power on the node and branch is calculated, and the uncertain characteristics of carbon emissions in the power system under photovoltaic injection power are obtained. Finally, the correctness of the proposed method is verified through case analysis results, and the low-carbon contribution of photovoltaic access to the power system is quantitatively analyzed. © 2024 SPIE.

关键词： Flow rate

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