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Optimal distribution grid allocation of reactive power with a focus on the particle swarm optimization technique and voltage stability

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Candra, Oriza Alghamdi, Mohammed I. Hammid, Ali Thaeer Alvarez, Jose Ricardo Nunez Staroverova, Olga V. Alawadi, Ahmed Hussien Marhoon, Haydar Abdulameer Shafieezadeh, M. Mehdi Univ Negeri Padang Dept Teknik Elekt Elect Power Engn Res Grp Padang Indonesia Al Baha Univ Dept Comp Sci Al Baha City Saudi Arabia Al Bayan Univ Tech Coll Engn Baghdad Iraq Univ Costa Dept Energy Barranquilla Colombia Plekhanov Russian Univ Econ Dept State & Municipal Finance Stremyanny Lane 36 Moscow 117997 Russia Islamic Univ Najaf Iraq Al Ayen Univ Sci Res Ctr Informat & Commun Technol Res Grp Thi Qar Iraq Univ Kerbala Coll Comp Sci & Informat Technol Karbala Iraq Islamic Azad Univ Dept Chem Engn Shiraz Iran Univ Diyala Coll Engn Dept Elect Engn Baqubah 32001 Diyala Iraq Sumerian Scriptum Synth Publisher Baqubah 32001 Diyala Iraq

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

关键词： Reactive power Voltage stability Ancillary service pso algorithm

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Real-Time Hybrid Test Control Research Based on Improved Electro-Hydraulic Servo Displacement algorithm

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SENSORS 2023年第10期23卷 4765-4765页

作者： Shen, Yaoyu Guo, Ying-Qing Zha, Xiumei Wang, Yina Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China

Real-time hybrid testing (RTH) is a test method for dynamic loading performance evaluation of structures, which is divided into digital simulation and physical testing, but the integration of the two may lead to problems such as time lag, large errors, and slow response time. The electro-hydraulic servo displacement system, as the transmission system of the physical test structure, directly affects the operational performance of RTH. Improving the performance of the electro-hydraulic servo displacement control system has become the key to solving the problem of RTH. In this paper, the FF-pso-PID algorithm is proposed to control the electro-hydraulic servo system in real-time hybrid testing (RTH), which uses the pso algorithm to operate the optimized PID parameters and the feed-forward compensation algorithm to compensate the displacement. First, the mathematical model of the electro-hydraulic displacement servo system in RTH is presented and the actual parameters are determined. Then, the objective evaluation function of the pso algorithm is proposed to optimize the PID parameters in the context of RTH operation, and a displacement feed-forward compensation algorithm is added for theoretical study. To verify the effectiveness of the method, joint simulations were performed in Matlab/Simulink to compare and test FF-pso-PID, pso-PID, and conventional PID (PID) under different input signals. The results show that the proposed FF-pso-PID algorithm effectively improves the accuracy and response speed of the electro-hydraulic servo displacement system and solves the problems of RTH time lag, large error, and slow response.

关键词： real-time hybrid test electro-hydraulic servo FF-pso-PID composite control pso algorithm

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IOT Energy Consumption Based on pso-shortest Path Techniques

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2020年第7期13卷 993-1000页

作者： Wadday, Ahmed G. Al-hchaimi, Ahmed A. J. Ibrahim, Ahmed J. Al Furat Al Awsat Tech Univ Engn Tech Coll Al Najaf Iraq Southern Tech Univ Thi Qar Tech Coll Thi Qar Iraq

Background: The Internet of Thing is a network that enables multiple hardware devices, sensors and other home applications from electronically communicate with each other. New era such technology is increasing importance mainly due to the revolutionary development of information technologies. Methods: However, energy efficiency is still a big challenge facing IoT technology. Thus, it becomes an interesting topic for many researchers to investigate. Current work aims to reduce the energy consumption thereby introducing the shortest path technique and another new practicing for Particle Swarm Optimization algorithm in the Internet of Thing cooperative clusters. Results: The main concept is based on cluster heads cooperation with each other known as Cooperative Clusters to transfer information to the base station. The Primary results reveals a 17% and 16% reduction in energy consumption was achieved over the shortest path technique and Particle Swarm Optimization algorithm respectively. Results also show a remarkable improvement in the system lifetime due to the new applied scheme. Conclusion: The other method is by pso algothrim at beginig it sending Randomly then it will select the path by using the feed back acknowledgment after that it will collect the information by sending it for the Cluster heads by updating the information status automatically. That's why we discovered the pso advantages than the others.

关键词： Cooperative clusters IOT pso energy consumption cooperative clusters pso algorithm

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Multi-Objective Optimal Design of a Cable-Driven Parallel Robot Based on an Adaptive Adjustment Inertia Weight Particle Swarm Optimization algorithm

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JOURNAL OF MECHANICAL DESIGN 2023年第8期145卷 083301-083301页

作者： Zhou, Bin Li, Sipan Zi, Bin Chen, Bing Zhu, Weidong Hefei Univ Technol Sch Mech Engn 193 Tunxi Rd Hefei 230009 Peoples R China Hefei Univ Technol Intelligent Interconnected Syst Lab Anhui Prov 193 Tunxi Rd Hefei 230009 Peoples R China Univ Maryland Baltimore Cty Dept Mech Engn 1000 Hilltop Circle Baltimore MD 21250 USA

Cable-driven parallel robots (CDPRs) have been widely used in engineering fields because of their significant advantages including high load-bearing capacity, large workspace, and low inertia. However, the impact of convergence speed and solution accuracy of optimization approaches on optimal performances can become a key issue when it comes to the optimal design of CDPR applied to large storage space. An adaptive adjustment inertia weight particle swarm optimization (AAIWpso) algorithm is proposed for the multi-objective optimal design of CDPR. The kinematic and static models of CDPR are established based on the principle of virtual work. Subsequently, two performance indices including workspace and dexterity are derived. A multi-objective optimization model is established based on performance indices. The AAIWpso algorithm introduces an adaptive adjustment inertia weight to improve the convergence efficiency and accuracy of traditional particle swarm optimization (pso) algorithm. Numerical examples demonstrate that final convergence values of the objective function by the AAIWpso algorithm can almost be 14 & SIM;20% and 19 & SIM;40% higher than those by the pso algorithm and genetic algorithm (GA) for the optimal design of CDPR with different configurations and masses of end-effectors, respectively.

关键词： cable-driven parallel robots multi-objective optimal design performance index adaptive adjustment inertia weight pso algorithm genetic algorithm

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Embedded feature fusion for multi-label criteria selection via local search strategy and particle swarm optimization

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SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Chen, Suhua Fang, Xu Zhai, Feng Wang, Li Lv, Lin Xuchang Univ Sch Elect Engn Xuchang 461000 Henan Peoples R China Henan Xj Metering Co Ltd Xuchang 461000 Henan Peoples R China China Elect Power Res Inst Beijing 300072 Peoples R China Gaomi Power Supply Co State Grid Shandong Elect Power Co Weifang 261505 Shandong Peoples R China

Multi-label classification is a significant challenge in machine learning, especially as the dimensionality of the problem increases. As the number of dimensions grows, the performance of traditional classification algorithms often degrades substantially. Feature selection is a key technique for reducing dimensionality in multi-label scenarios, operating as a non-parametric process. Despite its importance, feature selection remains a complex issue without straightforward solutions, and various approaches using AI and evolutionary algorithms have been proposed to tackle it. However, these methods typically suffer from reduced efficiency and slower convergence as the dimensionality increases, due to the expanding search space. To address this issue and enhance convergence speed, this article introduces a hybrid AI solution that combines a binary particle swarm optimization algorithm with a local search strategy specifically designed for multi-label feature selection. Within this local search strategy, feature fusion plays a crucial role, where features are merged based on their relevance and correlation with the problem's output. These fused features are divided into two categories: those directly associated with the problem class and those that are similar to the problem class but distinct from other feature fusions. By leveraging this categorization, the particle swarm optimization technique is augmented with a local operator that removes redundant feature fusions and refines each solution. By incorporating this operator, the proposed method achieves superior convergence speed compared to previous algorithms in the field. The performance of the proposed method was evaluated across several datasets against some of the most widely used feature fusion selection algorithms. The experimental results demonstrated the proposed method's accuracy and efficiency, validating its effectiveness in multi-label feature selection.

关键词： Multi-label classification Filter type Local search strategy Feature fusion Collective intelligence pso algorithm

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Adaptive particle swarm optimization based hybrid small-signal modeling of GaN HEMT

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MICROELECTRONICS JOURNAL 2023年 137卷

作者： Cai, Haiyi Zhang, Jincan Liu, Min Yang, Shi Wang, Shaowei Liu, Bo Zhang, Juwei Henan Univ Sci & Technol Coll Informat Engn Luoyang 471023 Peoples R China Novaco Microelect Technol Ltd Nantong 226000 Peoples R China

Based on the GaN HEMT 20-element small-signal model, an improved particle swarm algorithm is proposed in this paper for the optimization of the intrinsic parameters. The conventional Particle Swarm Optimization (pso) algorithm is prone to fall into local optimum solutions when parameter optimization is performed, in turn to affect the accuracy of experimental results. To solve the above problem, dynamic evolutionary estimation, elite learning strategies and adaptive strategies are added to the traditional pso algorithm, in turn to yield Adaptive Particle Swarm Optimization (Apso) algorithm in this paper. The experimental results show that the situation of falling into a local optimum for the traditional pso algorithm is greatly improved by the Apso algorithm, and that the model parameters of the GaN HEMT small-signal model can be extracted more accurately and faster in the frequency range of 0.5-20 GHz using the Apso algorithm.

关键词： GaN HEMT Small-signal model pso algorithm Parameter extraction

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Research on China's tourism marketing model in the data perspective: based on ecological perspective

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Liu, Yi Gao, Jia Yu, Hailong Tangshan Polytech Coll Tangshan 063299 Hebei Peoples R China North China Univ Sci & Technol Qinggong Coll Tangshan 064000 Hebei Peoples R China

With the continuous improvement of technology and residents' living standards. Should actively adapt to the opportunities and challenges brought by the Internet to the tourism marketing model, For the sustainable and rapid development of tourism on the wings of takeoff. In this paper, based on a brief explanation of the basic theory of database marketing, to achieve sustainable development of tourism marketing. Adopted the development of ecotourism boutique routes, Implementing multi-faceted marketing channels and means. Improvement of ecotourism infrastructure and service levels and other development measures. Create a tourism marketing model that responds to the trend of the times. Innovative marketing with big data technology can greatly stimulate the tourist crowd. Two times more efficient with dual cores. Get 56 times the execution efficiency with 64 cores. Get the efficiency of linear growth. The most direct way to increase the share of tourism companies in the consumer market. Maximize the value of marketing economic inputs and outputs. The results show that the improved pso algorithm is more effective in database stability and dispersion. This makes it important for tourism marketing.

关键词： Tourism Marketing Database Marketing pso algorithm Ecotourism Marketing Channels

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Trajectory planning for a 6-axis robotic arm with particle swarm optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期122卷

作者： Ekrem, Ozge Aksoy, Bekir Isparta Univ Appl Sci Dept Mechatron Engn Isparta Turkiye

Robotic arms, which are favored for usage in both large-and small-scale industrial regions, run into issues with numerous limits between the starting and ending locations in the working space when attempting to complete a particular task. With the solution to one of these problems, trajectory planning, the robotic arm manipulator can move from the starting point to the target point vibration-free, without hitting obstacles, and by choosing the shortest way. In this study, a robotic arm with 6 degrees of freedom, which is in the Mechatronics Engineering laboratory of Isparta University of Applied Sciences and whose prototype was realized by Acrome company, was used. In the study, the trajectory planning of the robotic arm was carried out using the MATLAB program and particle swarm optimization (pso). Trajectory planning is developed using the pso algorithm to determine the position of the robot at each point as it moves from its starting point to its target. Thus, time optimization was achieved by choosing the shortest path between the two points. Trajectory planning in joint space is aimed to ensure that the position, speed, and acceleration between the starting and ending points are continuous by using the fifth-order polynomial. The instant values of the joint variables used to determine the points followed by the manipulator were obtained by forward kinematics through the MATLAB program. Using forward kinematics, the position information of the manipulator was obtained by providing a transition from joint space to Cartesian space.

关键词： Robotic arm Robot optimization pso algorithm Matlab Trajectory planning Cartesian space Joint space

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CONSTRUCTION METHOD OF INFORMATION SECURITY DETECTION BASED ON CLUSTERING algorithm

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第6期25卷 5295-5301页

作者： Chen, Shaobo Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723000 Peoples R China

A method for K-prototype clustering, which can process mixed data types, is proposed first. An algorithm for information security evaluation of hybrid clusters based on K-prototypes was constructed. This method makes full use of the excellent global optimization performance of pso and effectively solves the defect that the K-protection function quickly falls into local optimization. Simulation results show that the proposed method can effectively prevent local extreme values and improve overall performance.

关键词： Clustering Information security Safety evaluation pso algorithm K -prototypes

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Enhancement of a Grid-Connected DFIG Wind Turbine System Using Fractional Order PI Controllers

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RENEWABLE ENERGY FOCUS 2023年 47卷

作者： Veerendra, A. S. Maheswari, M. Vasavi Uma Peddakapu, K. Sekhar, Ch. Punya Manipal Inst Technol Manipal Acad Higher Educ Dept Elect & Elect Engn Manipal 576104 India Acharya Nagarjuna Univ Elect & Elect Engn Guntur India Univ Malaysia Pahang Al Sultan Abdullah Ctr Adv Ind Technol Kuantan 26300 Pahang Malaysia

The wind velocities are unpredictable, and generating power with persistent speed is difficult which comprises mechanical stress at the rotor that leads to poor efficiency. So, variable wind power speed generation has ample significance with the Doubly Fed Induction Generator (DFIG). For a grid-connected DFIG, the governing of power is done conventionally by employing vector-controlled PI regulators. Yet, the performance of PI regulators depends on system parameters, which motivates the adoption of Fractional Order PI (FOPI) regulators. Presently FOPI regulators are seizing a lot of applications with the assistance of additional tuning parameters. However, tuning FOPI regulators by conventional methods is challenging. So, initially, the Particle Swarm Optimization (pso) method was used for tuning FOPI regulators, but the disadvantages are low convergence rate, falling into local elucidation, entailing more variables to attune, and requiring a large number of iterations. Thus, this motivates introducing the Ant Lion Optimization (ALO) algorithm in this article due to its better performance and eligibility to overcome the limitations of conventional methods. Furthermore, in the validation of the proposed algorithm, the performance parameters like DC coupling capacitor voltage, rotor Speed, active power, and reactive power with PI, FOPI, pso-FOPI, and ALO-FOPI regulators are compared and analysed using the bode plot are presented. The time response analysis of all the controllers with PI, FOPI, pso-FOPI, and ALO-FOPI controllers were also observed with a voltage dip. Finally, the fractional order controllers with frequency response and time response analysis show superior performance in distributed generation with advanced heuristic controllers when compared to conventional controllers. The complete work has been simulated via MATLAB/ Simulation platform.

关键词： Doubly Fed Induction Generator PI regulators FOPI regulators pso algorithm ALO algorithm Bode Plot

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