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12th International Conference on Renewable Power Generation, RPG 2023

作者： Wang, Yishen Wei, Renjie Lin, Siyan Tang, Zhuang State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Smart Grid Research Institute Co. Ltd. Beijing China

In the new types of power systems, characterized by the dominance of renewable energies, uncertain resources such as wind and solar power will play a significant role in power generation. However, the inherent unpredictability and fluctuation of these renewable sources make the operating conditions complex and variable. To address this, intelligent online dispatch applications that integrate optimization and artificial intelligence techniques need to be implemented. In terms of online dispatch, estimating system states and optimizing resources require computing a large number of variables while adhering to operating constraints. Graph computing and graph database technologies have shown promising results in power system online analysis. This paper analyzes the applications of these graph computing techniques to enhance online dispatch and provides a graph computing based-grid online analysis architecture. This architecture offers a range of online situational awareness applications and decision-making support. © The Institution of Engineering & Technology 2023.

关键词： Information management

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A smart grid computational offloading policy generation method for end-edge-cloud environments

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Journal of Reliable Intelligent Environments 2025年第1期11卷 1-13页

作者： Liu, Weiwei Wang, Yue Xu, Chenxi Zheng, Min State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China

With the continuous progress of the Internet of Things (IoT) technology, smart grid has become the key to improve the efficiency of the power system. At present, how to reasonably distribute the massive power computing tasks under the premise of guaranteeing the quality of service is an urgent problem of the current smart grid. Therefore, this paper focuses on the smart grid computation task offloading decision problem based on the collaboration of edge computing and cloud computing. An end-edge-cloud task scheduling model with task execution delay, energy consumption and load balance as optimization objectives is proposed. In addition, the Dyna-Actor Critic (Dyna-AC) algorithm is proposed using Deep Reinforcement Learning (DRL), which integrates the advantages of model-based and model-free learning and optimizes the performance of this algorithm through heuristic sample selection and asynchronous parameter update strategies. Simulation results show that the algorithm is very effective in reducing power business execution delay, terminal energy consumption and improving system load. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Computation offloading

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Graph computing and graph database applications in power system online dispatch

Graph computing and graph database applications in power sys...

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12th International Conference on Renewable Power Generation (RPG 2023)

作者： Y. Wang R. Wei S. Liu Z. Tang State Grid Laboratory of Grid Advanced Computing and Applications Beijing People's Republic of China

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Graph computing applications In Unit Commitment Historical Data Management

Graph Computing Applications In Unit Commitment Historical D...

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2023 IEEE Sustainable Power and Energy Conference, iSPEC 2023

作者： Wang, Yishen Zhou, Fei Zhu, Hong Liu, Siyan Tang, Zhuang Yang, He State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (纸本)9798350322699

The development of new types of power systems poses several key challenges for super-large-scale online analysis and optimization. These challenges include data merging, computation performance, and operation optimization. The uncertainty arising from renewable energy sources and load fluctuations has resulted in highly volatile power flow patterns compared to previous systems. As a result, the operating states of the system are more vulnerable to potential operational risks during critical periods. From the perspective of unit commitment, optimizing system resources involves increasingly complex constraints and variables. Graph computing has shown exceptional performance in online power system analysis, particularly in topology-related analysis. This paper explores the application of graph computing techniques to enhance security-constrained unit commitment historical data management. It offers new insights into the utilization of graph computing to improve unit commitment. © 2023 IEEE.

关键词： data management Graph computing graph database unit commitment

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Deep Learning-Based Algorithmic Trading Based on News and Events Strategies 7th

Deep Learning-Based Algorithmic Trading Based on News and Ev...

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7th International Conference on Soft computing: Theories and applications, SoCTA 2023

作者： Kayathri, V. Prabakaran, P. Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai600 117 India

ISBN: (纸本)9789819720309

Successful stock market investors should consider these trends. This article defines price effect, optimal execution, and placement through the lens of algorithmic trading and strategies based on news and events. By using feedbacks controlled to include consideration of a stop-loss order, algorithmic trading allows traders and investors at large to liquidate or construct big securities positions in a fully automated manner, thus generalising the existing stock trading results. Trading decisions made by the AI agent could affect the asset price. We see this as a marketplace where people are bartering and swapping goods. Creating a new market is the driving force behind our research into generative models. This news sentiment does a good job of describing stocks, and AI systems that read the news and quantify them react to factors that affect prices far more quickly than humans do. All three of the stock market's underpinnings—price, volume, and volatility—respond strongly to news events. Sentiment analysis is a method for gauging the overall tone of a news piece and classifying it as optimistic, pessimistic, or neutral. The simulation is conducted in python to test the effectiveness of the model in trading in an automated manner for stocks. The results show that the proposed sentimental analysis for stock trading achieves higher rate of accuracy and precision than the other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Financial markets

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Research on LLM Method for Knowledge-Assisted Generation of Power Standards

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2024年第6期22卷 168-178页

作者： Fan, Xiaoxuan Zhang, Sai Liang, Xiao Wang, Zhihao Peng, Tao State Grid Laboratory of Grid Advanced Computing and Applications China Electric Power Research Institute Co. Ltd Beijing China State Grid Jiangsu Electric Power Co. Ltd Nanjing China

With the rapid development of large language models, their application in various industries is becoming more and more common, especially in the power sector. Electricity power standard knowledge, as an industry specification for electric power equipment, engineering construction and operation management, can guide product production and engineering practice to improve production efficiency and quality level. In order to help solve the problem of low efficiency of electric power standard knowledge supply and assist the generation of electric power standard knowledge, we proposes an electric standard knowledge auxiliary generation method based on retrieval enhancement generation, which uses the retrieval model and evaluation model to provide large language model with accurate external knowledge, and then uses the generation model to provide a more efficient and accurate solution for the generation of standard knowledge in the electric industry. The experimental results indicate that the approach introduced in this paper can effectively generate accurate electricity-related knowledge and holds significant practical value. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Knowledge generation Knowledge-assisted Large language model Power standards knowledge Retrieval

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Analyzing Brain Damages in Children with Image Processing Using KNN and FCM Algorithms

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SN Computer Science 2024年第1期5卷 62页

作者： Kayathri, B. Prasanna, S. School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

In current scenario, children are addicted towards mobile communication devices such as mobile phone, tablet and various wireless devices. Exploring more radio frequencies with these devices causes brain damage in children aged 5–10 years. This paper uses an original calculation for the examination of the cerebral image. Typically, an MRI scan or CT scan will show the Cerebrum’s living structures. The brain of children is scanned in MRI and analyzed through image processing to know whether the children put their lives in danger or not, parents should be aware of their kids’ true health. The analysis of training data selected from MRI scan pictures yields 96.3% accuracy in determining the outcomes through examination of the children’s brains. Hence, the findings of brain analyses on various children are examined. Thus, the existing methods to analysis on finding brain damage which is not enough to sort out the problem still, so proposed method is needed to find Brain Damage Findings (BDF) which has to work prominently compared with the existing methods of FCM and KNN with unique test and trained data of brain. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Communication devices CT (Computerized Tomography) Damage FCM (Fuzzy C-Means) KNN (K-Nearest Neighbour) MRI (Magnetic Resonance Imaging)

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Enhancing Data Consistency in Distribution Networks Leveraging Graph Contrastive Representation Learning 6

Enhancing Data Consistency in Distribution Networks Leveragi...

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6th International Conference on Power and Energy Technology, ICPET 2024

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

ISBN: (纸本)9798350375855

The rapid integration of distributed renewable energy sources and the diversification of loads have precipitated profound changes in the configuration and operation of power distribution networks in the 21st century, underscoring the necessity for accurate and standardized power grid topology model data. This manuscript introduces a novel graph contrastive learning-based approach for the representation of node features in distribution networks, specifically targeting the alignment of identical nodes across different Common Information Model (CIM) documents. By combining self-supervised learning with graph neural networks, a feature extraction framework that integrates topological structural features and attribute features of the distribution network is proposed in the paper. Besides, the study designs a graph view generation strategy considering distribution network characteristics. The proposed method is validated by an 11-feeder distribution network dataset, and the results show the effectivity of the graph contrastive learning. © 2024 IEEE.

关键词： Contrastive Learning

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Research on Key Technologies of Intelligent Detection of Substation Equipment Based on Cloud Edge Collaboration 24

Research on Key Technologies of Intelligent Detection of Sub...

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2024 International Conference on Computer Vision and Deep Learning, CVDL 2024

作者： Chang, Ke Zheng, Min Wu, Chunpeng State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd Beijing China

ISBN: (纸本)9798400718199

The traditional manual inspection has some problems, such as strong subjectivity, time consuming and poor efficiency. With the remarkable progress of artificial intelligence, advanced communication and other technologies, and the wide application of deep learning models, the intensity of daily work has been reduced, but at the same time, new problems have been created. At present, the memory and computing power of the terminal hardware are limited, and when the model is deployed to the terminal side, the processing efficiency of the model is slow and the accuracy is low due to the limited hardware conditions. Aiming at the limited memory and computing power of the edge hardware, the lightweight network structure and model compression strategy are studied, and a channel structured pruning algorithm is proposed. A lightweight deep learning network model based on structured pruning of VGG-16 model was constructed. Experiments show that the optimized pruning model can obtain the same detection performance as the original model, and can reduce the size of the model by about 55.3% and increase the speed of 33FPS when the average accuracy mAP is lost by 8.1%, which provides effective support for the intelligent detection of substation equipment. © 2024 ACM.

关键词： computing power

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Lightweight Method for Adaptive Power Grid Operation Safety Control Model Based on Knowledge Distillation 24

Lightweight Method for Adaptive Power Grid Operation Safety ...

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2024 International Conference on Computer Vision and Deep Learning, CVDL 2024

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

ISBN: (纸本)9798400718199

Safety control is one of the most important tasks for power grid operation. However, safety control and risk warning models with high accuracy always have multitudinous parameters and need scaled resources for calculation. So as to sort out this problem, we proposed a novel and adaptive model lightweight method based on knowledge distillation. In our method, the intermediate features of the teacher network is utilized to guide the learning of the student network and a regressor is introduced behind the selected middle layer of teacher network to realize dimension match. The prediction confidence of teacher network is mapped to the feature space for the regressor initialization. Besides, the prediction confidence is used to construct the distillation temperature to improve the adaptability of information transfer. Experiments on ImageNet, CIFAR 100 and Grid-specific datasets demonstrate the lightweight model obtained by our method possesses powerful and adaptive feature extraction and representation ability. © 2024 ACM.

关键词： Safety engineering

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