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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bai, Qiaohua Beijing Information Technology College Beijing100070 China

ISBN: (纸本)9798331509675

Communication optimization algorithms in the complex and changing environments are an important research topic in the information field. optimization of the multi-path transmission control protocol model in 5G network environment is selected as the topic of this study. The original intention of this study is to solve the problem that data packets in traditional networks are only transmitted in a single network protocol. From a holistic perspective, this study designs a dynamic path selection optimization framework based on a greedy strategy and roulette algorithm. At the same time, it combines the improved Fuzzing test case generation technology to optimize test efficiency. At the same time, this study proposed a cross-protocol multi-path transmission mechanism. It also designed a store-and-forward solution for the weak connection and narrow bandwidth environments. Such a design can optimize data transmission performance. In complex 5G scenarios, this study further combined the mLoRa technology to solve problem of conflicting data packets through a frequency estimation algorithm. The experiment is carried out in real time with the assistance of Mininet network simulator and BMv2 programmable switch. The experimental results show that the optimized model is significantly better than the traditional method in terms of bandwidth utilization, transmission delay, data packet loss rate and other indicators which show the higher robustness. © 2025 IEEE.

关键词： 5G mobile communication systems

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A Context-Aware Framework for Sensor Placement Using PSO and Voronoi in Dynamic Scenarios 3

A Context-Aware Framework for Sensor Placement Using PSO and...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Manna, Prithwish Majumder, Sudipta Singh, N. Hemarjit Bose, Rajesh DUIET Dibrugarh University Dept. of CSE Assam Dibrugarh India DUIET Dibrugarh University Dept. of ECE Assam Dibrugarh India JIS University Dept. of CSE West Bengal India

ISBN: (纸本)9798331523893

When we deploy sensors, we face severe problems to achieve resilience and robust coverage. It happens more in stochastic and dynamic environments. In this paper, we propose a hybrid framework which is a combination of Particle Swarm optimization (PSO) and Voronoi Diagram method with context aware adaptation to fix the challenges. The proposed framework balances initial distribution of sensors and iterative optimization to enhance coverage, maintain k-coverage (k=3) and adapt to evolving scenarios even under critical situations like sensor failures. Experimental results highlight the performance of the proposed framework across the key scenarios: comparing coverage with or without context aware optimization quantifying the impact of random node failures, tracking cumulative recovery attempts and evaluating final coverage percentage. The results reveal that this hybrid framework maximizes the coverage and provides resilience to sensor failures. It also reallocates resources dynamically to maintain optimal deployment. This framework is suitable for getting more efficiency and minimal iterations of recovery while enhancing the robustness of the network. It also enlightens the adaptability in sensor performance and becomes very popular in modern applications. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Power control Based on Deep Q Learning in Heterogeneous Networks 3

Power Control Based on Deep Q Learning in Heterogeneous Netw...

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3rd international Joint conference on Information and Communication Engineering (JCICE)

作者： Zhao, Yingqi Jin, Jin Yang, Tingting Zhengzhou Univ Zhengzhou Peoples R China

ISBN: (纸本)9798350388732;9798350388725

Heterogeneous network is a promising solution to meet higher network capacity requirements and faster transmission rates. Under the spectrum-sharing strategy, the interference problem among user devices is a major factor affecting the network's performance. Power control methods employing deep reinforcement learning are extensively utilized for interference management purposes. A previous study formulated a multi-agent power control algorithm utilizing deep reinforcement learning to tackle the sum-rate optimization problem within a fixed base station deployment scenario in heterogeneous networks. Specifically, the aforementioned research exclusively examined a specific scenario wherein the femto base stations are situated within the central coverage area of the macro base station. Inspired by the research, this paper considers the variation of femto base stations' location and explores the impact of base station distribution on the sum-rate using deep Q learning algorithm. Simulation results show that the average sum-rate enhances with the increase of the sum distance between the macro base station and the femto base stations.

关键词： deep Q learning heterogeneous networks power control

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Research on Optimal Design of Wireless Sensor Network with Particle Swarm optimization and Improved Firefly Algorithm 3

Research on Optimal Design of Wireless Sensor Network with P...

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

作者： Li, Rui Gong, Dong Tan, Yutong Harbin Engineering University Harbin China

ISBN: (纸本)9781665472784

Since wireless sensor network coverage belongs to the category of combinatorial optimization, to improve its coverage in the case of a limited number of nodes, we introduced and improved the PGSO hybrid algorithm. In this paper, the utilization rate of nodes and the adequate coverage of the network are first taken as the optimization goals, and we established the corresponding mathematical model. We carried out the simulation experiments under the condition of a certain number of nodes, and the coverage of the proposed hybrid algorithm is optimal. Compared to other algorithms, the convergence speed and coverage rate are improved. The first part of the article briefly describes the primary use of hybrid algorithms, and the second part models and compares the four algorithms. © 2023 IEEE.

关键词： Combinatorial optimization

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Investigation of a Multi-level Coordinated control Scheme for Vehicle-Garage-Grid System 3

Investigation of a Multi-level Coordinated Control Scheme fo...

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3rd international conference on Sustainable Mobility applications, Renewables and Technology

作者： Zhang, Ruiqi Yang, Hui Wang, Zirui Lin, Heyun Wang, Yifei Chen, Tao Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

ISBN: (纸本)9798331532932;9798331532925

With the growing popularity of electric vehicles (EVs) and advances in vehicle-to-grid (V2G) technology, large-scale EV aggregations (EVAs) have become a critical and integral part of the electricity system. The judicious use of EVAs can improve grid's operational efficiency and economic benefits. However, effectively capturing the different idle energy storage characteristics of EVAs across regions and seamlessly integrating them into power system operations remains a challenge. In this paper, a methodology based on dynamic programming and multi-level optimization is proposed and further investigated to form a multi-level coordinated control system for vehicle, garage, and grid. The proposed method considers the characteristics of user needs and solution objectives within the vehicle-garage-grid system. It aims to reduce user costs, increase the economic benefits of garages, and improve the operational efficiency of the power system. Case studies based on real data show that the proposed multi-level coordinated control system (MCCS) significantly improves both the economic benefits and operational efficiency of the power system.

关键词： Coordinated control dynamic programming electric vehicle(EV) multi-level control vehicle-to-grid(V2G)

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Mitigation of Distribution Transformer Overloading with Rolling-Horizon optimization of Vehicle-to-Grid Resource 3

Mitigation of Distribution Transformer Overloading with Roll...

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3rd conference on Fully Actuated System Theory and applications (FASTA)

作者： Xia, Lin Hong, Xiao Sun, Xiaowei Li, Qingxue Wu, Hao Huo, Yingning Geng, Guangchao State Grid Zhejiang Elect Power Co Ltd Hangzhou Power Supply Co Hangzhou 310020 Peoples R China Zhejiang Univ Polytech Inst Hangzhou 310015 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

Electric vehicles (EVs) play a crucial role in the global transition to low-carbon and clean energy. Large amounts of uncontrolled EVs charging can cause short-term overloading problems with distribution transformers in some areas. EVs as a mobile energy storage component with Vehicle-to-Grid (V2G) capability can provide a solution to alleviate the overloading and aging problems of distribution transformers. This paper comprehensively considers the charging and discharging losses of EVs and the peak load of distribution transformers, with the constraint of EVs' own charging and discharging demands. The rolling-horizon optimization is employed to solve the model, which can adapt to dynamic changes in load conditions and provide rapid solutions. In this paper, the effectiveness of the proposed method is verified and the loads exceeding the capacity of the transformer are successfully shifted to other times and the charging needs of the users participating in the V2G program are met.

关键词： electric vehicles load shift,rolling-horizon optimization Vehicle-to-Grid

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Power system transient stability preventive control optimization method driven by Stacking Ensemble Learning

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ENERGY REPORTS 2023年 9卷 757-765页

作者： Xie, Zhijun Zhang, Dongxia Han, Xiaoqing Hu, Wei Taiyuan Univ Technol Shanxi Key Lab Power Syst Operat & Control Taiyuan 030024 Shanxi Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China

The dynamic characteristics of the power system are becoming more and more complex, and the difficulty of operation control is increasing. Preventive control is the main means of power system transient stability control. This paper proposes a stacking ensemble learning-driven power system transient stability preventive control optimization method. Firstly, a transient stability assessment model based on Stacking Ensemble Deep Belief Nets (SEDBN) network is established in this research. The performance of weak classifiers is improved by SEDBN's multi-layer ensemble structure, and the created transient stability estimator can extract diverse features and has better robustness and generalization abilities. Secondly, the trained transient stability estimator is integrated into the Aptenodytes Forsteri optimization (AFO) algorithm as a "transient stability constraint discriminator". Finally, with the goal of minimizing the cost of preventive control, an optimization algorithm for the preventive control of power system transient stability driven by SEDBN is established. Simulation results on IEEE 39-bus systems show that the proposed method can achieve highly efficient control solutions. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://***/licenses/by/4.0/).

关键词： Stacking Ensemble Learning Aptenodytes Forsteri optimization algorithm Transient stability Transient stability preventive control

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Enhancing Electric Vehicle Charging Stations with Renewable Energy Integration through Advanced control Algorithms 3

Enhancing Electric Vehicle Charging Stations with Renewable ...

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3rd IEEE international conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Singh, Ankita Bhongade, Sandeep Shri G. S. Institute of Science and Technology Department of Electronics and Electrical Engineering Indore India Ips Academy Institute of Engineering and Science Indore India Shri G. S. Institute of Science and Technology Department of Electrical Engineering Indore India

ISBN: (纸本)9798350387193

This paper presents a novel microgrid model for EV charging stations, primarily powered by renewable energy sources such as solar photovoltaics (PV) and wind. The proposed model employs an advanced control algorithm to independently manage solar PV, wind turbines, battery storage, and the EV charging process, intricately coordinated with the AC grids. A key innovation of our model is the optimization of on-grid charging through the strategic selection of component ratings for PV and wind converters. This optimization is achieved using a Fuzzy Proportional-Integral-Derivative (PID) controller enhanced by Grey Wolf optimization, ensuring maximal energy extraction from renewable sources. Furthermore, the model innovatively incorporates Q Learning algorithms to repurpose the braking torque energy of EVs back into the battery charging circuit. Empirical results demonstrate the superiority of the proposed model over existing methods. Notably, it exhibits a 15.5 % improvement in energy efficiency and an 8.3 % reduction in overall operational costs. © 2024 IEEE.

关键词： Charging stations

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Reinforcement learning-based three-dimensional cooperative guidance law 3

Reinforcement learning-based three-dimensional cooperative g...

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3rd conference on Fully Actuated System Theory and applications (FASTA)

作者： Gu, Zhen Zhang, Zeqi Kang, Honglong Li, Jianfeng Yue, Keyuan Song, Shenmin Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China Sci & Technol Complex Syst Control & Intelligent Beijing 100074 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

An innovative three-dimensional cooperative guidance law for striking stationary target, based on proximal policy optimization (PPO), is introduced. This novel guidance law directly correlates engagement state information with the navigation ratio of proportional navigation guidance (PNG). Initially, the cooperative homing guidance problem is transformed into a Markov decision process, and the reward function incorporates considerations for the zero-effort-miss (ZEM) and the consensus error of time-to-go. Subsequently, the cooperative homing guidance problem is transposed into the framework of reinforcement learning (RL). Ultimately, the effectiveness of the cooperative homing guidance solution is validated through numerical simulations, encompassing agent model training and Monte Carlo cases.

关键词： Reinforcement learning Cooperative guidance law Three dimensional

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Energy-Efficient Routing optimization for IoT-Based Multilevel Heterogeneous Wireless Sensor Networks using Firefly Algorithm and Hybrid Clustering 3

Energy-Efficient Routing Optimization for IoT-Based Multilev...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shilpa, Challa Venkata Ashokkumar, N. Srinivas, Cherukuri Reddy, Guttireddy Krishna Chaitanya Gowd, Chitrala Rajasekhar Rao, Y. Mallikarjuna Department of Ece AndhraPradesh Tirupati517102 India Santhiram Engineering College Department of Ece Andrapresh Nandyal India

ISBN: (纸本)9798331509675

Based on the Internet of Things (IoT) the heterogeneous wireless sensor networks (HWSN) have gained popularity and are crucial for developing a variety of human-centered applications. The researchers have proposed a number of ways for energy usage minimization and achieve energy-efficient network operations but most of these attempts concentrate on using the clustering technique, although it is very effective but most of them require the recurrent cluster creation which draws a lot of energy from nodes throughout the clustering process. In this work, an Energy-Efficient Hybrid Clustering Technique (EEHCT) has been presented for IoT-based HWSN and Firefly optimization (FFO) to optimize the routing. The nodes energy usage is ideal when the route to Base Station (BS) is optimal increasing the number of packets transmitted and alive nodes in the network and performed 90.27% than the existing methods in terms of network lifetime. © 2025 IEEE.

关键词： Energy utilization

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