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Research on Automatic Adjustment Technology of Power grid Operation Mode based on Genetic Algorithm

Research on Automatic Adjustment Technology of Power Grid Op...

引用

2022 international conference on Edge computing and applications, ICECAA 2022

作者： Li, Wei Zhou, Junyu Hua, Jie Foshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong528000 China

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Research on automatic adjustment technology of the power grid operation mode based on genetic algorithm is the focus of this study. In the case of grid-connected operation designed by us, when the distribution network fails or the power quality cannot meet the requirements, the voltage and frequency of the common coupling point are detected. Island operation mode, the control mode of energy storage system is changed from P/Q control to U/F control mode. The management of power grid operation mode involves many factors, such as complex data, different external environments, decoupling and paralleling of power grids, technical analysis of the safe operation of power grids, and power flow calculation and analysis of the operation mode need to be strengthened. Then, the Genetic Algorithm model is used to further construct the efficient model. The experiment shows the general performance is robust. © 2022 IEEE.

关键词： Genetic algorithms

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Application of Intelligent Control Based on Soft computing in the Safety Control of Switch Cabinet Maintenance 5

Application of Intelligent Control Based on Soft Computing i...

引用

5th international conference on Advances in Electrical Engineering and Computer applications, AEECA 2024

作者： Guo, Ninghui Xu, Xiao Wu, Hao Feng, Chi Wang, Qifeng Li, Yang State Grid Shijiazhuang Electric Power Supply Company Shijiazhuang China

ISBN: (纸本)9798350355253

To intelligently upgrade the safety management of switch cabinet maintenance, this paper proposes a safety control system based on cloud data management. The system integrates the BP neural network algorithm with RFID technology to enhance intelligent control. Initially, data such as operation procedures, risk types, and the maintenance environment are input into the neural network. The iterative calculation method is used to assess whether the maintenance personnel's actions and the surrounding environment meet safety requirements. Additionally, RFID technology is employed to identify and authenticate maintenance staff, ensuring comprehensive safety management and control during switch cabinet maintenance. Finally, the system's effectiveness is validated using MyEclipse 8.6 software to simulate maintenance safety control scenarios. The results are compared with traditional methods, such as the gray correlation analysis algorithm and the target detection algorithm, focusing on risk identification rate, safety detection accuracy, and response time. The findings demonstrate that the intelligent control system utilizing the BP neural network algorithm offers significant advantages in ensuring safety during switchgear maintenance. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Air Pollution Forecasting using Integrated Weather Stations and Convolutional Neural Network (CNN) Algorithm 7

Air Pollution Forecasting using Integrated Weather Stations ...

引用

7th international conference on Data Science and Its applications, ICoDSA 2024

作者： Marpaung, Yesanna Pininta Lamria Nuha, Hilal H. Oktaria, Dita Sailellah, Hassan School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350365351

The main challenge in forecasting pollution levels is the need to consider local weather conditions. Integrating weather data with pollution data is crucial for improving the accuracy of air pollution level forecasts. This research integrates weather data and pollution data to forecast air pollution levels using Convolutional Neural Network (CNN), a deep learning method that can train systems on large datasets by integrating feature extraction and classification processes. CNN is particularly effective in processing data with a grid structure, such as two-dimensional images, and can also handle high-dimensional data, such as video. This study applies an effective CNN method to forecast air pollution levels using data from an 10T-based integrated weather station. Out of 10 experiments conducted using the Convolutional Neural Network (CNN) algorithm, 9 successfully predicted air pollution levels with high accuracy. With RMSE of 1.2639, MAE of 1.2637, and MAPE of 0.63583%, the CNN model performed best in this investigation, exhibiting extremely low prediction errors. Additionally, an R-Square value of 0.98819 indicates that the model can explain almost all the variability in the data. © 2024 IEEE.

关键词： Convolutional neural networks

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A Dual Attention-Based Task Offloading Approach in computing Power Networks for Object Detection 19th

A Dual Attention-Based Task Offloading Approach in Computin...

引用

19th EAI international conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Huang, Kang Qiu, Chao Zhu, Hong Gao, Lisha Zhang, Qizhe Peng, Guozheng Xiang, Nan Wang, Xiaofei College of Intelligence and Computing Tianjin University Tianjin China China Electric Power Research Institute Beijing China State Grid Nanjing Power Supply Company Nanjing China

ISBN: (纸本)9783031651229

Intelligent object detection has enabled automatic defect detection at the edges, facilitating smart power transmission inspection and target recognition. However, edge devices often suffer from limited computational resources, resulting in slow model recognition and high energy consumption, making it challenging to meet daily inspection requirements. computing Power Networks (CPNs) provide a secure and efficient distributed computing solution, enabling the effective offloading of object detection tasks to resource pools within CPNs. Despite numerous studies focusing on optimizing task offloading, some issues persist, including coarse granularity of computing tasks, dispersed computing resources, and weak decision adaptability in complex environments. To address these challenges, we propose a dual attention-based deep reinforcement learning (Dat-DRL) approach, utilizing a custom sequence-to-sequence (seq2seq) neural network to learn effective task offloading strategies. Monitoring tasks at the edge of the power transmission line are modeled as a directed hypergraph, and the dual attention mechanism captures inter-task dependencies and diverse service requirements more effectively. To evaluate the proposed algorithm, we construct a simulation environment to model task offloading scenarios, and extensive experiments demonstrate the efficacy of Dat-DRL in reducing latency and energy consumption across different environments. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Computation offloading

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Distribution grid Topology Estimation: A New Approach-based on Bayesian Network Models 8

Distribution Grid Topology Estimation: A New Approach-based ...

引用

8th IEEE international conference on Big Data computing Service and applications, BigDataService 2022

作者： Mabrouk, Ahmed Rajagopal, Ram CSAI Lab CRIGEN ENGIE Group Stains France Stanford University Civil and Environmental Engineering StanfordCA94305 United States

ISBN: (数字)9781665458900

ISBN: (纸本)9781665458900

The important penetration of distributed energy resources into the power grid comes with a raft of challenges for urban distribution management. Knowing the grid topology in real-time is essential to ensure the reliability of operational power flow control. However, in practice, the topology information is often unavailable. Worst, this latter can change relatively more frequently with limited line monitoring devices. To address these drawbacks, we provide a new approach relying on Bayesian network models to detect connections amongst grid buses. We prove that the grid topology estimation can be formulated as a Bayesian network structure learning. The robustness of our approach, as well as its benefits compared to other estimation algorithms, are highlighted in numerical experiments. © 2022 IEEE.

关键词： Bayesian networks

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Comparative Analysis and Prediction of Machine Learning Algorithms for MRI-Based Alzheimer's Detection Using Multi-modal Data

Comparative Analysis and Prediction of Machine Learning Algo...

引用

2024 IEEE international conference on computing, applications and Systems, COMPAS 2024

作者： Hossen Abir, Md. Ismiel Salam, Taspia International Standard University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

In this research, various machine learning techniques were used to assess Alzheimer's disease detection based on eleven significant criteria. The methodology included comprehensive data preprocessing, analysis, and visualization. Initial steps involved removing duplicates, handling missing values using a simple imputer with a mode strategy, and ensuring all features were appropriately scaled and encoded. Data visualization techniques, such as histograms and density plots, were used to understand the dataset's distribution and identify outliers. We developed predictive models using supervised learning algorithms, including Random Forest, XGBoost, Gradient Boosting, CatBoost, SVM, KNN, Logistic Regression, Decision Tree, and Voting Classifier. Hyperparameter tuning and grid search cross-validation were applied to enhance model performance. The Random Forest, XGBoost, Gradient Boosting, and CatBoost classifiers achieved the highest accuracy of 96.30 percent, followed closely by the Voting Classifier with an accuracy of 96.29 percent. KNN and Decision Tree models also showed strong performance with accuracy of 95.06 percent and 93.83 percent, respectively. SVM and Logistic Regression had lower accuracy of 92.59 percent and 90.12 percent. Visual analysis indicated that individuals aged 70-80 were more affected by Alzheimer's disease. The study concluded that machine learning algorithms, particularly ensemble methods, are highly effective for classifying Alzheimer's disease, providing a robust approach to understanding and predicting the disease. The findings highlight the potential of machine learning in advancing health and disease diagnostics. © 2024 IEEE.

关键词： Logistic regression

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Encrypted Cost Based Load Forecasting With Attack Regression Capacity for CPS Model Based Anomaly Detection in Smart grid Security

Encrypted Cost Based Load Forecasting With Attack Regression...

引用

2023 IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Mishra, Saket Mishra, Rahul Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab India

ISBN: (纸本)9798350398465

Smart grid is distributed through grid computing programs that focus on the efficient operation of power systems in applications to be used in conjunction with. Involvement in SGS techniques involves threats such as power outages, energy theft, customer privacy behavior, threats to operators' security, etc., therefore, requires serious criticism. Designed to prevent the collapse of potential threats or security issues in existing systems, smart grid systems Analytical security assessment is based on framework-matter. Based on the power overflow phase, the properties of the communication network, and the integration coincidence equation, the Markov decision process (MDP) is used to measure smart grid security. The safety code provided for example appears to be a novel solution to the simultaneous power emergency review and can be defended by taking corrective action to resolve the hole-size sound grid product critical level. The goal of the proposed task is to expand the wide area surveillance system (W AMS) to provide security hole size counterfeit science fiction, and we hope to be able to build a security indicator to suit the W AMS at the top of the case. W AMP AC makes full use of measurement design and models in the cyber-physics system, as well as descriptive case studies based on the detection and mitigation of anomalies in attack and implementation of flexibility algorithms. © 2023 IEEE.

关键词： Markov processes

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Suppression of Conducted EMI and Enhancement of Power Quality Using Digital Communication Modulation Technique in SMPS applications

Suppression of Conducted EMI and Enhancement of Power Qualit...

引用

2023 IEEE international conference on Power Electronics, Smart grid, and Renewable Energy, PESGRE 2023

作者： Alavanthan, Rajalakshmi Anbukumar, Kavitha Rajalakshmi Engineering College Department of Electrical and Electronis Engineering Chennai India Anna University College of Engineering Guindy Department of Electrical and Electronis Engineering Chennai India

ISBN: (纸本)9798350310573

In SMPS applications DC-DC converters operate at high frequencies. It leads to a rise in EMI emission levels within the AC power source, consequently elevating the harmonic content in the current supply and deteriorating the quality of the grid power supply. The Frequency Hopping Spread-Spectrum (FHSS) modulation technique is implemented in SMPS to reduce Electro-Magnetic Interference (EMI) emission. The proposed method attenuates the peak EMI in the source side, by using a repeated switching of the carrier frequency. The modulated carrier signal hops between two switching frequencies within a specified band in a pseudo-random sequence, increasing the bandwidth of the carrier and thus reducing the EMI emission. The switching pulse for the DC-DC converter is obtained using Field Programmable Gate Arrays (FPGA). To prove the supremacy of the proposed FHSS modulation technique, the necessary power quality parameters namely source current THD and FFT spectrum, grid power factor and the peak value of the conducted EMI obtained are compared with the popularly used Random Pulse Width Modulation (RPWM). It is observed that by using the proposed FHSS modulation technique conducted EMI emission is reduced by 17 dBm in comparison with the RPWM technique. © 2023 IEEE.

关键词： Electromagnetic Interference Field Programmable Gate Arrays Frequency-Hopping Spread-Spectrum Modulation Random-Pulse Width Modulation Switched Mode Power Supplies (SMPS)

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A digital transformation: Advancing power standards with knowledge graphs and intelligent applications 4

A digital transformation: Advancing power standards with kno...

引用

4th international conference on Digital Signal and Computer Communications, DSCC 2024

作者： Gao, Xiaoxin Yang, Qiang Han, Jiajia Liu, Jiale Beijing China-Power Information Technology Co. Ltd Beijing China State Grid Zhejiang Electric Power Co. Ltd. Electric Power Science Research Institute Hangzhou China North China Electric Power University Beijing China

ISBN: (纸本)9781510681538

This paper addresses the digitization of power standards in the power industry through the development of knowledge graphs and the application of intelligent technologies. We explore the transformation of standard management, leveraging technologies like cloud computing, big data, and AI, and discuss the strategies of international standardization bodies like ISO and IEC towards machine-readable standards. The core of our research is the construction and application of a power standard knowledge graph, aimed at enhancing the management of standardized knowledge. This involves stages from data collection, incorporating OCR technology, to data management with an emphasis on data quality and security. A significant contribution of this work is the development of the PLM-based Knowledge Graph Completion Model, particularly focusing on the KG-SBERT model, which integrates semantic and structured features from pre-trained language models and knowledge graphs. Our experiments demonstrate the superiority of KG-SBERT over traditional KGE models in knowledge graph completion tasks, highlighting the effectiveness of prompt information in improving model performance. surpassing traditional knowledge graph embedding techniques in link prediction accuracy, attaining an MRR of 47.67, and Hits@5 and Hits@10 scores of 25.15, 81.54, and 93.91, respectively. This study underlines the critical role of digital transformation in the power industry, demonstrating how advanced technologies can significantly enhance the management, security, and accessibility of power standards, with broad implications for the industry's future development. © 2024 SPIE.

关键词： Standardization

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Push-Pull based grid tied Micro inverter for Photovoltaic applications

Push-Pull based Grid tied Micro inverter for Photovoltaic Ap...

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2023 IEEE international conference on Power Electronics, Smart grid, and Renewable Energy, PESGRE 2023

作者： Paul, Preenu Jose, Babita R Shahana, T.K. Abraham, Chikku Mathew, Jimson Sandeep, J. Cochin University of Science and Technology Division of Electronics School of Engineering Cochin India Muthoot Institute of Technology and Science Department of Electrical and Electronics Engineering Ernakulam India Indian Institute of Technology Department of Computer Science and Engineering Patna India Sfo Technologies R & D Solutions Power Electronics Cochin India

ISBN: (纸本)9798350310573

This paper presents the harnessing of solar energy using a two-stage grid-tied micro inverter with an isolated high gain DC-DC converter as first stage and a single-phase grid tied inverter as second stage. High gain converter is implemented using a quasi-switched boost network, switched capacitor topology and push-pull circuit. High frequency transformer in first stage improves gain and provides isolation between solar Photovoltaic (PV) circuit and grid connection which add safety margins. Micro inverter topology, control logic for Maximum Power Point Tracking (MPPT) and H - bridge inverter is presented. The operation of a 250 V micro inverter is verified using MATLAB Simulink tool. © 2023 IEEE.

关键词： grid tied inverter Isolated DC-DC converters MPPT One cycle control Switched boost topology Switched capacitor

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