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Precision Point Positioning Method with Galileo Broadcast ephemeris for grid Operations in Complex environments 4

Precision Point Positioning Method with Galileo Broadcast Ep...

引用

4th international conference on Smart grid and energy Internet, SGeI 2024

作者： Jiang, Junfei Cai, Hongwei Lai, Shiyu Shenglong, e. Wang, Lei Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou China

ISBN: (纸本)9798350368314

Precise Point Positioning (PPP) is a major technique for obtaining high-precision coordinates for grid operations in deep mountainous areas. However, the effects of extrapolation errors for broadcast orbits and clock offsets need to be carefully considered in PPP with broadcast ephemeris. We take the european Galileo Navigation Satellite System (Galileo) as an example, and the performance of its broadcast ephemeris from day of year (DOY) 161 to 190 in 2022 are analyzed. The evaluation results show that the overall orbital accuracy and the signal-in-space ranging error (SISRe) are up to 0.157 and 0.205m, respectively. For carefully considering the effect of Galileo broadcast ephemeris SISRe, combined errors in the line-of-sight (LOS) direction for each visible satellite orbit and clock are introduced in the traditional Be-PPP function model. The static and kinematic experimental results of the global MGeX stations show that considering LOS combined error can effectively improve the positioning accuracy of Be-PPP. The mean three-dimensional (3D) positioning accuracies of the Be-PPP static/kinematic solution for 14 global MGeX stations with considering SISRe are 16.0 and 35.3 cm, which are improved by 39.4% and 25.7% relative to the traditional Be-PPP solution, respectively. ©2024 ieee.

关键词： Clocks

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Research on Platform Construction of MMC-HVDC Digital Analog Hybrid Simulation Based on ADPSS 5

Research on Platform Construction of MMC-HVDC Digital Analog...

引用

5th international conference on Power engineering, ICPe 2024

作者： Li, Xiaoxiao Hu, Tao Zhu, Yiying Mu, Qing Li, Yalou Peng, Hongying Zhou, Xin Wang, Xiangxu Electric Power Research Institute Grid Simulation Center China Beijing China

ISBN: (纸本)9798331531140

With the development of new energy, it poses a huge challenge to safe and stable operation of the electrical power systems. The transmission technology based on modular multilevel converter (MMC) solves the problems connecting with weak AC systems. When conducting real-time simulation of large-scale ACDC power grids containing MMC-HVDC, the number of nodes is huge, which makes the simulation process extremely slow. The Thevenin equivalent modeling method is used to simulate the electromagnetic transient simulation method of the converter valve. This paper presents a platform based on advanced digital power system simulator (ADPSS). Referring to the parameters of Yu to e ± 420kV back-to-back MMC-HVDC transmission project, the accuracy of the constructed model is verified through comparing result with the actual project. The simulation platform proposed in the article can be used to simulate the large-scale AC/DC interconnection power grid accurately. © 2024 ieee.

关键词： electromagnetic transients

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Pandemic Outbreak Analysis Using Machine Learning Techniques 15

Pandemic Outbreak Analysis Using Machine Learning Techniques

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Panda, Sanjaya Kumar Anwar, Sumaiyya Racharla, Shashank Nit Warangal Department of Cse Telangana Warangal506004 India

ISBN: (纸本)9798350370249

Pandemics are global health emergencies that pose significant threats to people's health, the stability of society, and the economy. The development and quick spread of infectious diseases like Monkeypox (Mpox) and COronaVIrus Disease 2019 (COVID-19) are notable examples of the devastating effects of pandemics. The zoonotic disease Mpox, caused by the MPoX Virus (MPXV), has attracted attention worldwide since it can spread across people and shares similarities with smallpox. international Health Organization (IHO) has categorized Mpox and COVID-19 as global health emergencies, highlighting the seriousness and urgency of the situations and calling for collaborative actions to mitigate them. This paper undertakes a comprehensive analysis of the spread patterns and trends of Mpox and COVID-19 using machine learning (specifically, time series analysis) techniques, i.e., Auto-Regressive Integrated Moving Average (ARIMA), Seasonal Auto-Regressive Integrated Moving Average (SARIMA), ARIMA with Genetic Algorithm (ARIMA-GA), SARIMA with GA (SARIMA-GA), ARIMA with grid Search (ARIMA-GS), SARIMA with grid Search (SARIMA-GS), Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). Here, we use GA and GS to optimize the hyperparameters. The techniques are extensively simulated and evaluated using two datasets, four performance metrics, the forecast plot and the diagnostic plot to show the efficacy of SARIMA-GS, SARIMA-GA and Bi-LSTM as per their applicability. Understanding these techniques' prevalence patterns and trends may contribute to risk assessment and enable effective prevention and timely treatment strategies. © 2024 ieee.

关键词： Auto-Regressive Integrated Moving Average Coronavirus Genetic Algorithm grid Search Long Short-Term Memory Monkeypox Seasonal Auto-Regressive Integrated Moving Average

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SG-CIM Model Verification and Validation Framework in Business Middle Platform 8

SG-CIM Model Verification and Validation Framework in Busine...

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8th ieee international conference on Smart Cloud, SmartCloud 2023

作者： Lv, Jun Chen, Lei Lv, Guangxian Liu, Riliang Xu, Chongyou Lu, Yiming Du, Jian State Grid Corporation of China Co. Ltd Beijing China State Grid Zhejiang Electric Power Co. Ltd Zhejiang Hangzhou China State Grid Shanghai Energy Interconnection Research Institute Co. Ltd. Shanghai China Ningbo Supply Power Co. State Grid Zhejiang Electric Power Co. LTD. Zhejiang Ningbo China

ISBN: (纸本)9798350313505

With the continuous development of State grid middle platforms and expansion of power grid applications, SG-CIM model verification has received increasing attention. It plays a crucial role in ensuring the data accuracy and consistency for information applications tailored to the needs of different professional departments in the power industry. This paper provides an overview of the SG-CIM middle platform, emphasizing its crucial role in supporting data sharing and analysis applications between different professional and operational units. We outline key steps involved in the verification and validation process for SG-CIM models, including input checks, model validation, sensitivity analysis, and model evaluation modules. Furthermore, the paper also highlights how this framework fosters collaboration between different parts of an organization by ensuring that data used in these applications is accurate and consistent across different business processes. ©2023 ieee.

关键词： Sensitivity analysis

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Artificial Intelligence Based Reduced Switch Multilevel Inverter for grid Connected PV Applications

Artificial Intelligence Based Reduced Switch Multilevel Inve...

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2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Thangam, Thomas Rathika, N. Sivasubramanian, M. Kumar, R. Tharwin Manohar, V. Joshi Kavyalakshmi, A. International Maritime College of Oman Department of Process Engineering National University of Science and Technology Sohar Oman Mahendra Engineering College for Women Department of Electrical & Electronics Engineering Namakkal Tiruchengode India Sri Venkateswara College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Thiruvallur India Puducherry Technological University Department of Electrical and Electronics Engineering Puducherry India Presidency University Department of Electrical & Electronics Engineering Bengaluru India Government College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350333244

Due to the rise in computer power, tools, and data collection, artificial intelligence (AI) is becoming more and more prevalent in diverse photovoltaic (PV) system applications. The basics of grid-connected multilevel inverters for PV systems is provided, together with information on the drivers, characteristics, evaluation criteria, topologies, modulation schemes, and selection criteria for various applications. The results of a thorough reliability investigation of basic 15L multilevel inverter (MLI) are examined in this study. The inverter is the most crucial component of a grid-connected PV system. This study provides a survey of the system topologies and grid-connected PV inverters utilized for PV systems linked to the grid. © 2023 ieee.

关键词： Maximum power point trackers

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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9th ieee international conference on Data science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (ReSs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of ReS generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where ReS is abundant or electricity prices are low, based on ReS generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 ieee.

关键词： Low emission

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Quantum computing Approaches to Optimal Power Flow in Complex grid Networks

Quantum Computing Approaches to Optimal Power Flow in Comple...

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2023 international conference on Power energy, environment and Intelligent Control, PeeIC 2023

作者： Boddu, Bhasker Krishna, M Hari Manjunatha Kumar, Nagendra Singh, Navdeep Lafta, Alaa M. Mlr Institute of Technology Department of Information Technology Telangana Hyderabad India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gl Bajaj Institute of Technology & Management Dept. of Eee U.P. Greater Noida India Lovely Professional University Phagwara India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

Power flow optimization is paramount in ensuring reliable and efficient operation of electrical grid networks, especially as grids become more intricate with the integration of renewable energy sources. Classical computing methods have long dominated this optimization space but are increasingly challenged by the scalability and computational complexity of contemporary grid systems. This paper introduces a pioneering methodology leveraging the inherent capabilities of quantum computing to tackle optimal power flow (OPF) problems in multifaceted grid networks. Quantum algorithms, characterized by their superposition and entanglement properties, are harnessed to explore the vast solution space more efficiently than their classical counterparts. A quantum annealing approach, combined with the quantum phase estimation algorithm, is employed to ascertain global optima with reduced computational resources and time. Results obtained from simulated quantum environments, benchmarked against classical algorithms, demonstrate a significant reduction in computational time and enhanced accuracy in determining optimal solutions. This work elucidates the potential of quantum computing as a transformative tool in advancing the realms of grid optimization, setting a paradigm for future research in quantum-enabled energy systems. © 2023 ieee.

关键词： Complex grid Networks Optimal Power Flow Quantum Annealing Quantum computing Quantum Phase estimation. Top of Form

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A Reference Architecture Based on Collaborative computing Paradigms Adopted for Dynamic IoT Systems 7

A Reference Architecture Based on Collaborative Computing Pa...

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7th ieee Pune Section international conference, PuneCon 2024

作者： Joshi, Prashant G. Deshpande, Bharat M. Bits Pilani K K Birla Goa Campus Department of Computer Science & Information Systems Goa India

ISBN: (纸本)9798331527822

Current IoT architecture standards offer a foundational reference model for building diverse applications. However, they lack detailed provisions for emergent aspects such as various paradigms of computing, data processing, and dynamism of the system. Addressing these limitations, the IoT architecture based on Collaborative computing Paradigms (CCP) presents unique characteristics, including, (a) Interconnection and interplay (b) Dynamic distribution of data processing (c) Fluidity of computing across paradigms (d) Storage and data management across participating paradigms (e) Scalability and extendability of the architecture. Reference Architectures (RA) are foundational reference models, and provide a blueprint for creating applications in a broad set of applications and applications domains. A CCP-IOT-RA is an architecture which overcomes the challenges of dynamic IoT environments, and is a reference architecture that satisfies the requirements of a RA. An architecture based on CCP-IOT-RA was designed, and developed for dynamic IoT environments. The developed architecture was evaluated against the requirements of RA and dynamic IoT environments using real-world applications. With this, it is validated that the designed CCP-IOT-RA indeed is a reference architecture that can be applied across various application domains. © 2024 ieee.

关键词： Storage management

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YOLOv8 for Pedestrian Detection: A Comparative Study for Pedestrian Detection 3

YOLOv8 for Pedestrian Detection: A Comparative Study for Ped...

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3rd international conference on electrical, electronics, Information and Communication Technologies, ICeeICT 2024

作者： Sivaraman, G. Sophiya, e. Diviya, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

This research analyzes the usefulness of YOLOv8 for robust pedestrian recognition in demanding real world situations. A custom-trained YOLOv8 model is evaluated against SSD models, displaying better accuracy in detecting and localizing human classes within images characterized by occlusions and variable scales. We analyze the model's performance across confidence thresholds, demonstrating a positive balance between precision and recall. While noting computational considerations, our findings demonstrate YOLOv8's potential for advancing pedestrian detection systems, opening the way for enhanced safety and efficiency in applications such as autonomous driving, surveillance, and smart city efforts. © 2024 ieee.

关键词： Deep learning

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Pre-trained Language Model for Chinese Frame Semantic Parsing based on electric Power Dataset 4

Pre-trained Language Model for Chinese Frame Semantic Parsin...

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4th international conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Wang, Xiaohui Liang, Yundan Gui, Chenwei Wang, Boya Wang, Gaozhou State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Corporation of China Beijing China University of International Relations Lab of Intelligent Social Computing Beijing China State Grid Shandong Electric Power Company Information and Telecommunications Company Shandong China

ISBN: (纸本)9798350373233

In the operational landscape of the electric power industry, a wealth of events, operations, and fault reports generates substantial textual data. The application of frame semantic parsing to the power grid system presents challenges, especially given the distinctive characteristics of Chinese text, including entity ambiguity, word segmentation issues, and a misalignment with knowledge in the power systems field. In response to these challenges, we introduce an innovative pipeline specifically designed for Chinese semantic analysis based on an electric power dataset, showcasing significant enhancements over the baseline. This research strives to drive technological advancements, fostering a more sophisticated understanding of textual data within the intricate domain of the Chinese electric power industry, with a particular focus on natural language processing. © 2023 ieee.

关键词： Pipelines

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