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Elite Parallel Cuckoo Search Algorithm for Regional Coverage control Problem in High-Density Wireless Sensor networks 3

Elite Parallel Cuckoo Search Algorithm for Regional Coverage...

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3rd International conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Tian, Min Bai, Jinbin Li, Jiangquan Huang, Mingqi Zhan, Chenyang Ren, Lingjie Shihezi University College of Mechanical and Electrical Engineering Shihezi China

ISBN: (纸本)9781665417327

With the growing application of high-density wireless sensor network (HDWSN), coverage control technology has been a key and paramount problem in different scenarios of HDWSN. A regional coverage optimization algorithm is very significant to monitor the network, reduce the waste of resources and improve the lifetime of HDWSNs. Regional coverage control is a technique providing a method that avoids duplicate coverage in HDWSN. However, the regional coverage control is a nonlinear constrained problem whose complexity increases with a quantity of nodes. In this paper, an elite parallel cuckoo search algorithm (EPCSA), a randomized swarm optimization algorithm for regional coverage control in HDWSNs, motivated by elite selection and parallel theory is proposed. To assess the EPCSA's overall efficiency, the EPCSA flow of regional coverage optimization is designed. The model of the regional coverage control problem and the objective function is given. In the simulation, in order to further verify the effectiveness of EPCSA, particle swarm optimization (PSO) and ant colony optimization (ACO) are compared with EPCSA under the same conditions as EPCSA proposed in this paper. The performance of coverage optimization of three algorithms is compared and analyzed. Results show that the regional coverage rate and the deployment of sensor nodes of the network are optimized effectively by the EPCSA. © 2021 IEEE.

关键词： Ant colony optimization

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2021 3rd International conference on Smart Power and Internet Energy Systems, SPIES 2021

2021 3rd International Conference on Smart Power and Interne...

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3rd International conference on Smart Power and Internet Energy Systems, SPIES 2021

ISBN: (纸本)9780738146331

The proceedings contain 81 papers. The topics discussed include: speed control method of permanent magnet synchronous motor optimized by artificial bee colony algorithm;research on power factor correcting algorithm based on average detection;active power filter in linear systems for power factor correction;web processing control using backstepping and RBF neural networks;trajectory tracking control of manipulator based on particle swarm optimization fuzzy neural network;design and implementation of digital sampling for stability control device;and method for cluster partition of distributed generation considering flexible supply and demand balance and response speed.

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A power supply recovery algorithm for large area power failure in distribution network based on artificial neural network

A power supply recovery algorithm for large area power failu...

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Electronic Technology, Communication and Information (ICETCI), IEEE International conference on

作者： Yan Jin Luo Jinman Ye Siqi Li Yuqing Dongguan Power Supply Bureau of Guangdong Power Grid Corporation Dongguan China

The distribution network directly serves the vast number of users. The distribution network covers every corner of the city and the countryside, and undertakes the function of supplying electricity to small and medium-sized customers. The purpose of this paper is to study the power supply recovery algorithm for large-scale power outages in distribution networks based on artificial neural networks. On the basis of analyzing the characteristics of power distribution network and large-area power outage in distribution network, a method of hot backup and artificial neural network is proposed to solve the problem of power supply recovery from large-area power outage in distribution network method of positioning. Secondly, the distribution network reconstruction model based on CMAC is introduced. In view of the shortcomings of the CMAC algorithm, the spurious technology is introduced to the random mapping method of the associated empty golden section. The CMAC network can quickly and accurately give the switch state when the network loss is the smallest according to different load conditions in the system. It avoids the complicated search optimization in the traditional heuristic method, especially suitable for real-time control.

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A Study on Topology optimization of Aircraft Thermal Management System Based on Intelligent optimization Algorithm

A Study on Topology Optimization of Aircraft Thermal Managem...

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Electrical Engineering and control Science (IC2ECS), International conference on

作者： Zhenghong Li Xingzhuang Liang Zhiyuan Huang Xuan Liu Yang Yu Jiao Liu AVIC Shenyang Aircraft Design & Research Institute Shenyang China School of Aeronautics and Astronaut Dalian University of Technology Dalian China School of Automation Shenyang Aerospace University Shenyang PR China

Traditional design methods, which depend on the experience of designers, make it difficult to meet the requirement of fast design due to the increasingly complex aircraft thermal management system (TMS). Therefore, this paper investigates the architectural design of TMS by the graph theory, and the intelligent optimization algorithm optimizes the flow rate of aircraft TMS. Firstly, the topology architectures of the aircraft TMS are generated by the enumeration method based on the component catalog and corresponding network structure constraints. In the topology architectures, the labeled graphs represent the TMS architectures, and physics-based models located in the graph nodes are used to evaluate the performance of each aircraft's TMS architecture. Secondly, to give the appropriate flow rate, this paper optimizes the flow rate for each aircraft TMS architecture by the intelligent optimization algorithm, and thus, the optimal flow rate and aircraft TMS architecture are obtained. Finally, the fuel circulation sub-system and environmental control sub-system of the aircraft TMS are used to verify the performance of this paper, and the results show that this paper's work can be applied to the sub-system of the aircraft TMS and the optimal flow rate and TMS architecture can be obtained by this paper's strategy.

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Research on PID Parameter Self-Tuning Speed control System Based on Grasshopper Optimazation Algorithm-Optimized BP Neural network

Research on PID Parameter Self-Tuning Speed Control System B...

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Energy, Power and Electrical Engineering (EPEE), International conference on

作者： Yifan Huang Yonghong Huang Jiangsu University School of Electrical and Information Engineering Zhenjiang China

In response to the limitations associated with conventional PID control strategies, which rely heavily on predetermined design parameters and struggle to provide precise, stable motor speed and current control, particularly in the presence of disturbances, we have developed a novel double closed-loop DC speed regulation system. This system incorporates a self-tuning PID mechanism based on the Grasshopper algorithm optimization neural network. To achieve this, we employed a Back-Propagation (BP) neural network to autonomously fine-tune the parameters of the PID control strategy. Subsequently, during the backpropagation phase of the neural network training, we harnessed the Grasshopper algorithm to optimize these parameters based on a fitness function, leading to the calculation of updated weighting coefficients. Following this iterative process, the output voltage was computed using the incremental PID formula, enabling precise motor control. Remarkably, this approach reduced overshoot by 30%, all without the need for preemptive control parameter adjustments. Moreover, the rise time experienced only a marginal increase of approximately 0.06 seconds. These advancements effectively address the limitations associated with traditional PID control strategies, expanding their utility beyond narrow operational ranges and enhancing performance in complex working conditions. The outcome is a speed regulation system characterized by significantly improved accuracy and robustness, ideally suited to meet the demanding requirements of engineering applications.

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ChatGPT Participates in Traffic control as a Traffic Manager Assistant

ChatGPT Participates in Traffic Control as a Traffic Manager...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International conference on

作者： Yiqing Tang Xingyuan Dai Yisheng Lv School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institution of Automation Chinese Academy of Sciences Beijing China

This paper uses ChatGPT to assist traffic manager in traffic signal control and verifies the possibility of applying ChatGPT in traffic control scenarios. We believe ChatGPT has four capabilities, including: knowledge acquisition, data analysis, decision support, and programming interface support. Proper use of these capabilities can assist traffic manager in their traffic strategy optimization efforts. We constructed a simulation environment and used ChatGPT to assist human traffic manager in performing signal optimization efforts. The process of evaluating the application potential of ChatGPT consists of four steps. First, we conducted a test on traffic related issues to test ChatGPT’s knowledge acquisition ability. Second, the road network structure topology analysis test was conducted to help traffic manager quickly understand the road structure. Third, we performed traffic flow characterization and corresponding optimization suggestions to help traffic manager determine optimization policies. Finally, the policy deployment code modification is done by interacting with ChatGPT through natural language, which can improve the efficiency of traffic policy deployment compared with manual programming. The simulation results show that ChatGPT can help traffic managers to complete traffic policy optimization faster. Also, the traffic control optimization under this process improves the average passing speed of vehicles compared with that before the policy change.

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Q-Learning for Stackelberg Games of Uncertain Nonlinear Systems

Q-Learning for Stackelberg Games of Uncertain Nonlinear Syst...

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Service Robotics (ICoSR), International conference on

作者： Wenbei Tang Xiaohong Cui Jun Sun Jiaxian Deng The College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China Zhejiang Province Key Laboratory of On-line Testing Equipment Calibration Technology Research China Jiliang University Hangzhou China

ISBN: (数字)9798350367119

ISBN: (纸本)9798350367126

The Stackelberg equilibrium approach is based on the traditional dynamic programming technique, which requires an understanding of the system model to solve the optimal control problem. Therefore, we propose a Q-learning algorithm based on actor-critic structure to solve the optimization problem of Stackelberg game in nonlinear systems with unknown system model data. Currently, the Q-learning algorithm is mainly used in the study of linear systems in this direction, and the study of nonlinear system problems is limited. In addition, the stability of the system is ensured by proving the consistency of the system state and the ultimate boundedness of the neural network weight estimation error. Finally, the effectiveness of the algorithm is verified by simulation experiment.

关键词： Linear systems Q-learning Heuristic algorithms Optimal control Games Data models Dynamic programming Nonlinear systems optimization Thermal stability

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An Optimized Scheduling Strategy for Electric Vehicle Charging and Discharging Considering Traffic network Information

An Optimized Scheduling Strategy for Electric Vehicle Chargi...

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Power and Electrical Technology conference (APET), Asia

作者： Huaming Chen Yinggao Chen Lang Ling Tiejun Ma Zheng Wang Tao Jin College of Electrical Engineering and Automation Fuzhou University Fuzhou China Fujian Huashun Water Conservancy and Hydropower Engineering Co. Ltd Fuzhou China Fujian Fuqing Nuclear Power Co. Ltd Fuqing China Fujian Xiexing Construction Co. Ltd Fuzhou China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The installed capacity of renewable energy in the distribution network poses challenges to the stability of the networks due to their intermittency and volatility. To address this issue, an optimized scheduling strategy for electric vehicle charging and discharging considering traffic network information is proposed. Firstly, the interaction factors and methods of the electric transportation coupling network are analyzed, and a control framework for electric vehicle charging and discharging is proposed. Secondly, an optimization model for electric vehicle charging and discharging travel paths is proposed based on road resistance information and vehicle origin-destination matrices. Finally, considering the curtailment cost of renewable energy, an electric vehicle charging and discharging scheduling model based on the system's optimal cost is established. Simulations on the improved IEEE 33-node system demonstrate that the proposed strategy effectively solves the low-voltage problem at the terminal nodes of the distribution network while considering the system cost, achieving efficient integration of renewable energy.

关键词： Resistance Renewable energy sources Costs Roads optimization models Electricity Transportation Distribution networks Electric vehicle charging optimization

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TLBO Based Optimized Planning Framework of Distribution network considering Electric Vehicle Charging Station and Solar Photovoltaic System

TLBO Based Optimized Planning Framework of Distribution Netw...

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Smart Technologies for Power, Energy and control (STPEC), IEEE International conference on

作者： Sasmita Tripathy Sharmistha Nandi Sreeparna Roy Ghatak Parimal Acharjee Pampa Sinha School of Electrical Engineering KIIT University BBSR Odisha India School of Electrical Engineering NIT Durgapur Durgapur West Bengal India

Electric vehicles (EVs) are significantly increasing the burden on the electricity sector, resulting in power loss, a poor voltage profile, and voltage stability difficulties. To address this, a solid planning framework for efficient deployment of EV charging stations and solar energy resources in the distribution network is being established in the present work. A practical probabilistic model of EV is developed using Probability Distribution Function (PDF) considering uncertainty parameters *** distance, trip end time, types of battery and battery capacity extracted from real National Household Travel Survey (NHTS) datasheet. The Teaching Learning Based optimization (TLBO) algorithm is used to solve the problem of optimum joint deployment of EV charging station and solar energy, minimizing power loss reduction index (PLRI) and voltage deviation index(VDI) while considering system constraints. Further the voltage stability of the system is also analyzed with the incorporation of devices in the network. In this paper, the optimization problem is solved using MATLAB (version R2018a) software, and the IEEE 69 bus system is employed as a test system. The simulation outcomes show that optimal deployment of EV charging stations with photovoltaic (PV) units improves voltage profile, the penetration level of EV charging station and also voltage stability index (VSI) of the system, but reduces total real loss. The technique is compared with three different algorithms, including Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Differential Evolution (DE), in terms of convergence characteristics.

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A Frequency control Method for Distributed Energy Storage Cluster control

A Frequency Control Method for Distributed Energy Storage Cl...

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Frontiers of Power and Energy (ACFPE), Asian conference on

作者： Ying Li Hongfu Liang Bo Gu Ke Li Jiexin Zhang Marketing Department State Grid Chongqing Shinan Electric Power Supply Branch Chongqing China

ISBN: (数字)9798350367676

ISBN: (纸本)9798350378467

A frequency control method for distributed energy storage cluster control is proposed to address the issue of poor frequency regulation performance in the power system due to the widespread integration of new energy sources. Firstly, build a system frequency control architecture for distributed energy storage clusters, and unify the regulation of energy storage clusters through aggregation control centers. Then, design an enhanced learning algorithm controller and use the BP neural network optimized by the improved particle swarm optimization algorithm to find the optimal energy storage power increment for frequency control. Finally, use the control output of the controller to achieve system frequency control. Based on the IEEE 39 node system, experimental analysis was conducted on the proposed method, and the results showed that the system frequency did not experience overshoot, and the energy storage output power did not exceed the limit of 75MW, significantly improving the frequency control capability of the new power system.

关键词： Renewable energy sources Frequency modulation Neural networks Clustering algorithms control systems Regulation Particle swarm optimization Frequency control Power generation Energy storage

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