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42nd international conference on High Energy Physics, ICHEP 2024

作者： Liu, Rui Hu, Yu Fu, Shiyuan Qi, Fazhi Nanjing Vocational University of Industry Technology YangShanBei Road 1Qixia District NanJing China Computing Center Institute of High Energy Physics of the Chinese Academy of Sciences YuQuan Road 19 BeiJing China

HEPS is a fourth-generation synchrotron light source, and the experiments conducted at HEPS will transition to high-throughput, multi-modal, ultra-fast frequency, and cross-scale formats. The annual data flux generated by these experiments is anticipated to enter the'Exa-scale' era. Given the substantial volume of high-throughput experimental data, a single computing node struggles to meet the computational demands for data analysis. Consequently, it is essential to develop a high-performance, robust, and user-friendly distributed parallel computing engine to enhance the performance of data processing software. Due to significant variations in data rates across different beamline stations, supporting heterogeneous resources (such as GPUs) in a flexible and fine-grained manner presents a challenge. To optimize computational efficiency and handle extensive datasets, we have developed a distributed parallel computing engine that can leverage scalable, heterogeneous computing resources to deliver HEPS' data analytical services across various scales. Experiment shows that our distributed computing engine significantly enhances the efficiency of HEPS' data processing. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0).

关键词： Data reduction

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Optimal scheduling method of medium-low voltage AC-DC hybrid distribution network based on parallel ADMM algorithm

Optimal scheduling method of medium-low voltage AC-DC hybrid...

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2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Hu, Yuyang Wei, Xin Yang, Xiao Chen, Binbin Gu, Guangfeng Electric Power Dispatching and Control Center Guangxi Power Grid Co. Ltd. Guangxi530023 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

In recent years, with the increasing trend of DC on the distribution side, the optimal dispatching problem of AC-DC hybrid distribution network has attracted much attention. This paper presents a method of partition distributed optimization based on parallel ADMM, the hybrid distribution network is divided into AC and DC subregions based on the connection of voltage source converters. To model this network, the coupling branch method is employed. Initially, a distributed voltage optimization model is created to minimize network losses, voltage deviation, and flexible load utilization. To tackle the non-convex nature of this optimization problem, the alternating direction multiplier distributed method is utilized. Through techniques like second-order cone relaxation, the problem is transformed into a convex optimization model. Subsequently, simulation calculations are conducted on a modified IEEE 33 node example system. The analysis focuses on the impact of AC power and distributed power supply on DC voltage. By comparing optimization results under different control modes, the validity and rationality of the proposed model are verified. The parallel ADMM algorithm plays a crucial role in accelerating the optimization process, thereby enhancing calculation efficiency and facilitating the faster determination of optimal scheduling schemes for medium-low voltage AC-DC hybrid distribution networks. In summary, the future direction of research lies in developing scheduling methods based on parallel ADMM algorithms, aiming to provide more efficient and reliable solutions for the operation and management of hybrid distribution networks. © 2025 SPIE.

关键词： DC distribution systems

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A Dynamic Tree Topology-based Method for Secure Transmission of Ciphertext Data in the Cloud for the Internet of Things for Electric Power 24

A Dynamic Tree Topology-based Method for Secure Transmission...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Li, Hua Wu, Zhengrong Min, Xun Lin, Jiayi Du, Shaohui China Southern Power Grid Guangdong Guangzhou China Guizhou Grid Company Limited Guizhou Guiyang China Co. Ltd. China Southern Power Grid Guangdong Guangzhou China

ISBN: (纸本)9798400709869

Due to the lack of rationality of channel resource allocation in the transmission phase, the effect of transmission has certain limitations, therefore, a study on the secure transmission of ciphertext data in the cloud of power Internet of Things (IoT) based on the dynamic tree topology is proposed. When planning the channel resources in the secure transmission of ciphertext data in the cloud of electric power Internet of Things using the frame slot allocation algorithm in the dynamic tree topology, the parent-child relationship in the tree topology subdivides a single superframe into multiple frames and further divides each frame into three independent time slots, and avoiding the time slot conflict within the vertical distance of two hops from the perspective of improving the overall efficiency of the network;Ciphertext transmission is realized under the distributed consensus mechanism. In the test results, the data transmission completion effect is always stabilized above 0.975, and the energy consumption is always lower than that of the control group. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Trees (mathematics)

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Research on new energy grid-connected human-machine cooperative training system applying parallel control and transformer modeling 3

Research on new energy grid-connected human-machine cooperat...

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Li, Miao Wang, Jing Zhuang, Yuan Training Center of Jilin Province State Grid Corporation of China Changchun China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regulators with simulation exercises, intelligent deductions, decision-making references, Q&A services, which provide intelligent reference solutions for precise regulation under new energy access scenarios with high percentage of new energy under a new type of power system. In order to enhance training effectiveness and operational efficiency, it is need to apply intelligent technology and data-driven models instead of experience and human labor. Interactive Q&A application such as Chat GPT has subversively changed the implementation mode of the training process, making the traditional teaching of theory and basic knowledge easier and faster. However, these commercial Q&A systems still have limitations in terms of accuracy, security, as well as professionalism, which makes them inefficient in professional learning area. In this paper, we use the idea of parallel control and the transformer model to construct a human-computer cooperative training system adapted to new energy grid-connected electric power systems, which realize a human-computer cooperative training model that highly integrates the Q&A services with the real trainer, the real trainees, the computer simulation system. By constructing a large model of human-computer system, a training computing experiment platform, as well as a system with a closed loop of training reality, the parallel training will help training program planning, training teaching design, training arrangements, teaching interaction and other key training links, and realize automated and intelligent training design and execution. As a training and management model adapted to the situation of artificial intelligence, parallel training will bring brand new possibilities for the development of the training industry in the intellige

关键词： Decision making

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AJAS: A high performance direct solver for advancing high precision astrometry

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JOURNAL OF COMPUTATIONAL SCIENCE 2025年 87卷

作者： Ryabinin, Konstantin Sarras, Gerasimos Loeffler, Wolfgang Erokhina, Olga Biermann, Michael Heidelberg Univ Ctr Astron Astron Rechen Inst Monchhofstr 12-14 D-69120 Heidelberg Germany

In astrometry, the determination of three-dimensional positions and velocities of stars based on observations from a space telescope suffers from the uncertainty of random and systematic errors. The systematic errors are introduced by imperfections of the telescope's optics and detectors as well as in the pointing accuracy of the satellite. The fine art of astrometry consists of heuristically finding the best possible calibration model that will account for and remove these systematic errors. Since this is a process based on trial and error, appropriate software is needed that is efficient enough to solve the system of astrometric equations and reveal the astrometric parameters of stars for the given calibration model within a reasonable time. This paper is an extended version of the conference paper published and discussed at the international conference on Computational Science 2024. In this work, we propose a novel software architecture and corresponding prototype of a direct solver optimized for running on supercomputers. The main advantages expected from this direct method over an iterative one are the numerical robustness, accuracy of the method, and the explicit calculation of the variance-covariance matrix for the estimation of the accuracy and correlation of the unknown parameters. This solver can handle astrometric systems with billions of equations within several hours. To reach the desired performance, we use state-of-the-art libraries and methods for hybrid parallel and vectorized computing. The calibration model based on Legendre polynomials is tested by generating synthetic observations on grid-shaped constellation with specified distortions. For these small-sized test data, the solver can recover perfectly the correct physical solution under the condition that the correct amount of eigenvalues is zeroed out. During the space mission, the calibration model should be carefully fine-tuned according to the real operating conditions. The developed solver

关键词： Astrometry Instrument calibration High-performance computing Least Squares Method Eigenproblem Pseudo-inverse matrix

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Adaptive Protection Scheme Using Optimal Coordination of Directional Overcurrent Relays in grid-Connected Mode by Applying Micro-Genetic Algorithm 4

Adaptive Protection Scheme Using Optimal Coordination of Dir...

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4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Das, Sukrit Gupta, Shubhrata Department of Electrical Engineering National Institute of Technology Raipur India

ISBN: (纸本)9798331530402

The integration of distributed Generation (DG) in grid-connected systems presents challenges in maintaining reliable protection schemes, particularly in coordinating Directional Overcurrent Relays (DOCR). This paper proposes an Adaptive Protection scheme that optimizes the coordination of DOCRs using a Micro-genetic Algorithm (μGA). The proposed method enhances the resilience and adaptability of protection mechanisms against variable fault conditions and DG contributions. Through simulation studies, the performance of the μGA-based approach is evaluated against traditional coordination methods, demonstrating improved fault detection and minimized relay operating times. Results indicate significant reductions in coordination time intervals, ensuring reliable operation of protection systems in the presence of DG. This research offers a framework for enhancing the operational reliability of power systems while accommodating the increasing prevalence of distributed energy resources. © 2025 IEEE.

关键词： Electric power system protection

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Towards Optimizing Cost and Performance for parallel Workloads in Cloud computing 15

Towards Optimizing Cost and Performance for Parallel Workloa...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Maas, William Rossi, Fábio Diniz Luizelli, Marcelo C. Navaux, Philippe O.A. Lorenzon, Arthur F. Institute of Informatics Federal University of Rio Grande do Sul Brazil Federal Institute Farroupilha Campus Alegrete Brazil Federal University of Pampa Campus Alegrete Brazil

ISBN: (纸本)9789897587474

The growing popularity of data-intensive applications in cloud computing necessitates a cost-effective approach to harnessing distributed processing capabilities. However, the wide variety of instance types and configurations available can lead to substantial costs if not selected based on the parallel workload requirements, such as CPU and memory usage and thread scalability. This situation underscores the need for scalable and economical infrastructure that effectively balances parallel workloads’ performance and expenses. To tackle this issue, this paper comprehensively analyzes performance, costs, and trade-offs across 18 parallel workloads utilizing 52 high-performance computing (HPC) optimized instances from three leading cloud providers. Our findings reveal that no single instance type can simultaneously offer the best performance and the lowest costs across all workloads. Instances that excel in performance do not always provide the best cost efficiency, while the most affordable options often struggle to deliver adequate performance. Moreover, we demonstrate that by customizing instance selection to meet the specific needs of each workload, users can achieve up to 81.2% higher performance and reduce costs by 95.5% compared to using a single instance type for every workload. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： parallel processing systems

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Optimizing Transitive Closure Computation for High Performance computing and Security 33

Optimizing Transitive Closure Computation for High Performan...

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33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Masciari, Elio Napolitano, Enea Vincenzo DIETI University of Naples Federico II Naples Italy

ISBN: (纸本)9798331524937

Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (NetworkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security. © 2025 IEEE.

关键词： Graph algorithms

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Research on Abnormal Power Consumption Behavior Detection and Load Forecasting System of Power Big Data Based on FCM and Wavelet Neural Network 14th

Research on Abnormal Power Consumption Behavior Detection an...

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14th international conference on Frontier computing, FC 2024

作者： Pan, Jingni Chai, Yufeng Lv, Jiayu Wang, Xiaotian State Grid Shizuishan Power Supply Co. Ningxia China State Grid Ningxia Power Supply Co. Ningxia China State Grid Wuzhong Power Supply Co. Ningxia China

ISBN: (纸本)9789819627974

With the progress of smart grid, power systems are faced with more and more complex data challenges, including large-scale data, multi-sample types and low resource integration. This research takes power big data as the object, carries out abnormal power consumption behavior detection and load prediction based on Hadoop platform, and successfully realizes the visualization processing of power data. We thoroughly analyzed the needs of power big data visualization and designed the overall Hadoop-based architecture. In terms of key technologies, we adopt the visualization method based on geographic information, and use MapReduce to carry out parallel computation, and effectively identify and display different electrical behaviors. In addition, we proposed a wavelet neural network power load forecasting method based on FCM, and personalized classification of power users through FCM clustering. Combined with the prediction ability of wavelet neural network, accurate power load prediction was successfully realized, and parallel calculation was carried out on the distributed platform to further improve the prediction accuracy. Finally, we built a Hadoop big data experiment platform to verify the experimental results and performance of the proposed method. The experimental results show that our research has the ability to effectively cope with the big data challenges of power system, and provides important support and guidance for the safe and stable operation of power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Abnormal Power Consumption Behavior Detection FCM Hadoop Platform Power Big Data Wavelet Neural Network

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Micro-PMU Data-Driven Anomalous Voltage Event Detection for the Power Distribution System 12th

Micro-PMU Data-Driven Anomalous Voltage Event Detection for...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Parameswaran, Saththiyan Dey, Maitreyee Patel, Preeti Department of Computer Science and Applied Computing London Metropolitan University 166–220 Holloway Rd. LondonN7 8DB United Kingdom

ISBN: (纸本)9789819601424

As distributed Energy Resources become more integrated into the power grid, the complexity and scale of challenges faced by grid operators have increased. The integration of micro-synchrophasors (micro-PMUs) into the grid infrastructure has provided access to high-resolution data, yet analyzing such vast amounts of information presents its own set of obstacles. This study examines thirty days of micro-PMU data from April 2023, employing statistical analysis and unsupervised learning techniques to identify various voltage events over time. Real-world data from grid-connected solar farms in Norfolk, England, are utilized to detect and analyze these voltage events, as well as to explore their distinct patterns. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Power grids

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