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A Review of Research on Group Regulation and control of Microgrids 3

A Review of Research on Group Regulation and Control of Micr...

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3rd international conference on Smart Grids and Energy Systems, SGES 2024

作者： Lin, Zhijie Zheng, Liu Zhang, Xuze Wang, Guanyao Li, Ran Xu, Yiting Wen, Jiaxuan Liu, Yunan Beijing Zhongdian Feihua Communication Company Limited Fengtai Beijing China

ISBN: (纸本)9798331539634

With the rapid development of distributed new energy, its penetration in the distribution network gradually increases, multiple microgrids in the same distribution network area need intergroup regulation and intergroup control, and the microgrid cluster technology that regulates and controls the microgrids in a grouping mode has begun to be explored in a large number of studies. This paper summarizes the research progress on the concepts of microgrids and microgrid clusters, organize network technology, topological structure, intergroup regulation and control, and operation modes, aiming to provide references for further research and practical applications of microgrid clusters. © 2024 IEEE.

关键词： Network topology

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Data Processing optimization of Power Grid Dispatching control Cloud Based on Edge-Cloud Collaborative Computing 3

Data Processing Optimization of Power Grid Dispatching Contr...

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3rd IEEE international conference on Power, Electronics and Computer applications, ICPECA 2023

作者： Sheng, Lei Wang, Chunying Liu, Yan Zhang, Ningning Wang, Zhaohe State Grid Henan Electric Power Company Information & Telecommunication Company Zhengzhou China

ISBN: (纸本)9781665472784

Cloud computing technology is widely used in power grid systems. In response to the data with strict delay requirements and a large amount of data generated by the operation of the power grid system. New challenges are raised to the delay of business processing. In order to achieve the goal of minimizing the system average latency time of task processing, based on the power grid dispatching cloud architecture, a delay optimization method based on SDN edge-cloud collaborative computing is proposed. The goal is to minimize the system average delay of task processing. Introduce continuously variable edge and cloud and task distribution coefficients. The delay optimization problem is transformed into the task allocation coefficient optimization problem. And finally get the task allocation optimization strategy based on the model calculation. The experimental results show that when the edge node tasks are reasonably loaded into the cloud center for processing, the system delay of the business can be effectively reduced. Compared with the previous methods, the edge-cloud collaborative computing method proposed in this paper reduces the system delay and utilizes the control mechanism of SDN that can coordinate cloud computing and edge computing. The algorithm can be used in the power system cloud platform system based on SDN. © 2023 IEEE.

关键词： Edge computing

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Enactment of Deep Reinforcement Learning Agent for Duty Ratio control of Dual Active Bridge Converter 3

Enactment of Deep Reinforcement Learning Agent for Duty Rati...

引用

3rd IEEE international conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Patra, Stutee Chatterjee, Aditi Department of Electrical Engineering C.V. Raman Global University Bhubaneswar India

ISBN: (纸本)9798331533205

This paper presents a deep reinforcement learning (DRL) agent for duty ratio control of the dual active bridge (DAB) converter using triple phase shift (TPS) modulation. The operation of a dual active bridge under the TPS modulation technique was briefly discussed, stating its advantage over the other phase shift techniques, thus creating the requirement of a model-free control approach for the converter. The reinforcement learning method has evolved as the deep reinforcement learning method that can solve complex problems with rising computational accuracy. This paper focuses on a deep deterministic policy gradient (DDPG) agent founded on actor-critic policy, i.e. implemented as the DRL controller. A detailed discussion about the control strategy using the DRL controller was presented. The model was simulated using MATLAB/Simulink software version 2024b to verify the voltage regulation of the dual active bridge using the DDPG agent. Finally, the conclusion presents the advantages of using a DRL controller for the TPS modulation of the DAB converter. © 2025 IEEE.

关键词： Deep reinforcement learning

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An Aeromagnetic Compensation Algorithm Based on Broad Learning System 3

An Aeromagnetic Compensation Algorithm Based on Broad Learni...

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3rd international conference on Robotics, Artificial Intelligence and Intelligent control (RAIIC)

作者： Wang, Qilong Zuo, Chao Jin, Fang Song, Junlei Dong, Kaifeng China Univ Geosci Engn Res Ctr Intelligent Technol Geoexplorat Hubei Key Lab Adv Control & Intelligent Automat C Minist EducSch Automat Wuhan Peoples R China Wuhan Second Ship Design & Res Inst Wuhan 430064 Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Aeromagnetic surveys, renowned for their operational flexibility and high efficiency, serve as a crucial technique for measuring the geomagnetic field. However, aeromagnetic surveys are easily affected by magnetic interference from navigation platforms, making the compensation of aeromagnetic interference a crucial step in the measurement process. To address the inadequate consideration of nonlinear magnetic field interference in traditional compensation algorithms, this paper introduces an aeromagnetic compensation approach based on broad learning system. The broad learning system employs an incremental learning mechanism aimed at enhancing the precision of the network alongside the increase in nodes. With each expansion of the network node, computation is streamlined to calculating the pseudo-inverse of the expansion node, eliminating the necessity for retraining the entire network structure. Leveraging the nonlinear fitting characteristics of the broad learning system, this paper improves the accuracy of aeromagnetic interference compensation. Through UAV flight experiments, the broad learning system is compared with methodologies using particle swarm optimization (PSO) and BP neural network. Compared with PSO, training time was reduced by 21.3% and magnetic interference by 33.6%. Compared with BP neural networks, training time was reduced by 34.9% and magnetic interference by 28.6%. This paper provides references and ideas for the selection of aeromagnetic interference compensation algorithms.

关键词： aeromagnetic compensation broad learning system particle swarm optimization incremental learning

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Real-time optimized dispatching control strategy for EV V2G based on dynamic TOU price 3

Real-time optimized dispatching control strategy for EV V2G ...

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3rd international conference on Energy and Electrical Power Systems (ICEEPS)

作者： Wang, Ronggang Mini, Haitao Sun, Weiyi Jiang, Rui Liu, Zhitong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130012 Jilin Peoples R China FAW Co Ltd Gen Inst Changchun 130011 Jilin Peoples R China

ISBN: (纸本)9798350375145;9798350375138

With the increasing popularity of electric vehicles, their disorderly centralized charging behavior will have a serious impact on the security of the power grid. In this paper, we propose a V2G optimal scheduling strategy for electric vehicles based on dynamic electricity price, which aims to guide the orderly charging and discharging behavior of electric vehicle users, and reduce the charging cost and battery aging cost of users. Firstly, a trip chain model based on Naive Bayes was constructed, and the details of each trip were generated by using NHTS data. Secondly, a dynamic electricity price model is established to describe the relationship between electricity price and grid load. With the goal of minimizing the charging cost and battery aging cost, the convex optimization algorithm is used to solve and update the all-day charging and discharging strategy of electric vehicles in real time. Finally, the simulation verifies the reliability of the travel chain data. The optimization strategy proposed in this paper can effectively reduce the charging cost of users, and is conducive to the realization of peak shaving and valley filling of power grids.

关键词： Electric Vehicles V2G Journey Chains Dynamic Time-of-Use Pricing Real-Time optimization Charge and Discharge control

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Wireless communication system optimization and performance improvement based on artificial intelligence 24

Wireless communication system optimization and performance i...

引用

3rd international conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Yang, Bo Zhao, Haohui Guangzhou Coll Technol & Business Guangzhou 510850 Peoples R China

ISBN: (纸本)9798400709777

At present, Artificial intelligence technology is extensively applied across diverse sectors of the social economy and daily life. Firstly, this study introduces the current research status and relevant theoretical basis at home and abroad. Secondly, this study analyzes and summarizes the key problems and optimization goals that need to be solved in intelligent evolutionary game theory. Finally, this study improves the genetic algorithm by constructing models of different levels, and designs a comprehensive iterative cross-validation strategy combined with genetic operators to achieve the optimal control effect of the final performance index. The results show that by optimizing the intelligent algorithm, the bit error rate of the system is reduced from 0.001 to 1e-25, which greatly reduces the possibility of data corruption. Moreover, the packet loss rate decreases with the increase of distance. The result of a packet loss rate of 0.0001 makes the wireless signal more stable in data transmission. It can be seen that the application of intelligent algorithms not only improves the performance of the system, but also improves the reliability of data transmission. This can improve the stability of the communication system in complex environments, and provide users with better, more secure and reliable experience and convenience services.

关键词： Artificial intelligence Wireless communication System optimization Performance improvement

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Smart Servo-Operated Ball Valve with IoT Integration and PID control for Precise Fluid Management 3

Smart Servo-Operated Ball Valve with IoT Integration and PID...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Bhanuse, Vijaykumar Bhosale, Shreyash Nitin Bhilare, Abhishek Mahadev Bauskar, Aneesh Rajesh Vishwakarma Institute of Technology Pune411037 India

ISBN: (纸本)9798331515911

This paper presents the design and implementation of an innovative control valve system that integrates a ball valve run by a servo motor, IoT capabilities, and PID control for precise operation. The system uses a 1/4-inch ball valve mounted in a custom 3D printed housing, driven by an MG995 servo motor. An ESP32 WROOM module microcontroller enables Wi-Fi connectivity and data transmission to the cloud via the Thingspeak platform. Ultrasonic sensors are used for tank level measurement, and the valve operation is controlled using a PID algorithm implemented in C++ on the Arduino IDE. The PID parameters are fine-tuned to achieve best performance. This solution offers a cost-effective and precise method for fluid control in various industrial applications. © 2025 IEEE.

关键词： Three term control systems

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optimization Algorithm for Centralized control System of conference Equipment Based on Artificial Intelligence 3

Optimization Algorithm for Centralized Control System of Con...

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3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Yuan, Chao Liu, Hengjie Jiang, Xiuzhen Zheng, Lianzhen Zhao, Ziqi State Grid Laiwu Power Supply Company Jinan Shandong271100 China

ISBN: (纸本)9798350376173

With the rapid growth of technology, the era of network information has led to an increasing demand for communication among people, and the means of communication have also undergone earth shaking changes. To meet the various forms of international and domestic conference exchange needs, major enterprises around the world have introduced various advanced multimedia intelligent conference equipment and created various forms of conference halls. However, with the continuous growth of mainstream multimedia conferences, equipment maintenance issues, human-computer interaction issues, and resource utilization issues have gradually become prominent, becoming key factors that constrain conference efficiency and experience. In order to effectively address these challenges, this article proposes an optimization algorithm for a centralized control system of conference equipment based on artificial intelligence (AI). This algorithm utilizes advanced technologies such as deep learning (DL) and the Internet of Things (loT) to achieve automatic recognition, configuration, status monitoring, and intelligent scheduling of conference equipment. By collecting and analyzing device and user data in real-time, algorithms can automatically optimize device settings, improve human-computer interaction efficiency, and allocate device resources reasonably to ensure the smooth progress of meetings. The experimental results show that the algorithm can significantly improve the management efficiency and user experience of conference equipment. © 2024 IEEE.

关键词： Efficiency

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Blockchain Integrated Cloud Architecture for Surveillance Systems 3

Blockchain Integrated Cloud Architecture for Surveillance Sy...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Kumar, Vinay Raghuvanshi, Varun Singh Sharma, Shashwat Yadav, Yash Goutam, Arvind Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798331519582

The speedy growth of data-pushed programs, especially in surveillance and IoT systems, has underscored the need for steady, green, and privateness-retaining records control answers. While cloud computing gives scalability, it poses sizeable safety risks, making it liable to records breaches and unauthorized access. Blockchain technology, with its decentralized and immutable ledger, offers promising answers to these challenges, yet it struggles with scalability and computational demands while implemented to high-frequency, actual-time applications. This research paper proposes an technique to address those limitations through an integration of blockchain generation with cloudprimarily based surveillance systems. Through a hybrid version of selective storage and optimized consensus mechanisms, the proposed solution addresses the demanding situations of latency, scalability, and privacy maintenance at the same time while enhancing real-time access control. © 2025 IEEE.

关键词： Real time control

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An Intelligent Hybrid Algorithm for Smooth AUV Path Generation 3

An Intelligent Hybrid Algorithm for Smooth AUV Path Generati...

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3rd IEEE international conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Singh, Paramjeet Talukdar, Bhaskar Jyoti Dash, Rudra Narayan Das, Bikramaditya BPUT Dept. of Computer Science and Engineering Odisha Rourkela India VSSUT Dept. of Electronics and Telecommunication Engineering Odisha Burla India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India BPUT Dept. of Electronics and Communication Engineering Odisha Rourkela India

ISBN: (纸本)9798331533205

The development of effective multi-objective optimization strategies for Autonomous Underwater Vehicles (AUVs) operating in uncharted territories constitutes a considerable challenge. This endeavor necessitates the simultaneous minimization of travel time, reduction of path length, enhancement of navigational safety, and smoothing of trajectories. In light of these challenges, this research presents an innovative real-time path planning framework that seamlessly integrates Particle Swarm optimization (PSO) with Simulated Annealing (SA), thereby enhancing AUV navigation within static environments. The PSO algorithm is utilized to generate optimal temporary waypoints across the scenarios examined, by balancing the crucial optimization parameters. Subsequently, an optimal path is formulated, informed by both the initial and terminal points. The efficacy of the proposed methodology is assessed through simulations conducted across three distinct maps. Each obstacle map demonstrates varying densities of obstacles. The outcomes of these simulations emphasize the robustness and adaptability of the proposed strategy, highlighting its ability to formulate optimal paths within complex underwater environments, irrespective of the arrangement of obstacles. The AUVs display remarkable agility in maneuvering through intricate underwater landscapes, adeptly avoiding debris and obstacles while preserving operational efficiency. This enhanced path planning technique not only elevates the responsiveness of the AUVs during critical missions - such as search and rescue operations and structural assessments - but also significantly bolsters their overall navigational safety. Through this research, we introduce a formidable tool for enhancing AUV capabilities in practical applications, ultimately facilitating more efficient underwater exploration and intervention. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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