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Unit Commitment Comprehensive Optimal Model Considering the Cost of Wind Power Curtailment and Deep Peak Regulation of Thermal Unit

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IEEE ACCESS 2020年 8卷 71318-71325页

作者： Yang, Bin Cao, Xiangyang Cai, Zhenhua Yang, Tongguang Chen, Dawei Gao, Xiaohai Zhang, Jie Shandong Elect Power Econ Res Inst Jinan 250021 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Shandong Elect Power Co Jinan 250021 Peoples R China

Clean renewable energy is given high priority to generate power in power system dispatching. Unfortunately, the objective to minimize wind power curtailment may results in additional costs, since the inverse peak regulation characteristic of wind power probably result in redundant power qualities curtailed to ensure the balance of the electric supply and demand in real time. In this paper, considering the cost of deep peak regulation of thermal unit and wind power curtailment, unit commitment comprehensive optimal model is proposed to minimize total cost by searching the optimal wind power curtailment. In order to achieve an economic strategy among the wind power curtailment and the deep peak regulation, two indexes including the amount of hourly optimal wind power curtailment and the critical value of comprehensive wind power curtailment are developed. particle swarm optimization algorithm is employed for solving the proposed model. Afterwards, the relationship between the degree of deep peak regulation and the amount of wind power curtailment is researched, the derived conclusions can provide valuable information for system operators.

关键词： Wind power curtailment deep peak regulation unit commitment comprehensive optimal model particle swarm optimization algorithm system operators

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Design and HIL implementation of a new robust fractional sliding mode control of microgrids

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第26期14卷 6690-6702页

作者： Delavari, Hadi Naderian, Sina Hamedan Univ Technol Dept Elect Engn Hamadan 65155 Hamadan Iran

Microgrid inverters in the presence of faults, unavoidable modelling uncertainties, disturbance and harmonic current resulting from nonlinear loads should have small steady state tracking error, small THD and high robustness hence this subject has turned one of the motivations of this investigation. To achieve the stated goals, fractional adaptive sliding mode controller (FASMC) is proposed in this paper. The proposed controller increases the robustness, flexibility and degree of freedom. As far as in practice it is not easy to define the bounds of disturbances and guarantee the system stability, hence in next step, to overcome this challenge the adaptation laws are suggested. Then for the problem of determining the controller parameters, particle swarm optimization (PSO) algorithm is used. The performance of the proposed technique is investigated for an islanded microgrid under different disturbances, also to verify the advantages of the proposed controller, the results are compared with other controllers. Finally, the proposed controller and PID controller are implemented experimentally on Arduino mega 2560 microcontroller.

关键词： adaptive control three-term control particle swarm optimisation invertors control system synthesis robust control microcontrollers uncertain systems variable structure systems distributed power generation nonlinear control systems power generation control harmonic current resulting nonlinear loads steady state tracking error stated goals fractional adaptive sliding mode controller flexibility degree of freedom adaptation laws controller parameters particle swarm optimization algorithm islanded microgrid different disturbances PID controller robust fractional sliding mode control microgrids microgrid inverters unavoidable modelling uncertainties

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Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things

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APPLIED INTELLIGENCE 2020年第6期50卷 1889-1907页

作者： Nguyen Thi My Binh Huynh Thi Thanh Binh Nguyen Van Linh Yu, Shui Hanoi Univ Ind Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam Univ Technol Sydney Sydney NSW Australia

One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary algorithm (HEA) and Gravitation particle swarm optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.

关键词： Internet of things Heterogeneous wireless multimedia sensor network Directional sensing coverage model Minimal exposure path Evolution algorithm particle swarm optimization algorithm

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The calculation of the reliability index in nonlinear problems: A new method

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AIN SHAMS ENGINEERING JOURNAL 2020年第3期11卷 705-716页

作者： Alamdarloo, Pezhman Derakhshan Moghaddam, Mehdi Azhdary Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran

The accurate estimation of the health (reliability) index is important to estimate probability of failure in the reliability assessment of structures. The conventional first-order reliability methods including the Hasofer-Lind, Rackwitz-Fiessler, and Monte Carlo could lead to unstable, fluctuating, and distorted solutions to nonlinear problems, featuring complicated structural performance functions. The present study aimed to propose a new method by combining the particle swarm optimization and differential evolution algorithms in order to calculate the reliability index. The performance of the proposed method was evaluated by 10 examples from different studies, and the convergence results were compared to the results of studies such as Hasofer-Lind, Rackwitz-Fiessler, Monte Carlo and some other methods. To verify the accuracy of the proposed method, to verify the accuracy of the proposed method, a reliability index chart was applied. The comparisons indicated the high accuracy and speed of the proposed method. Accordingly, in higher order nonlinear problems, the proposed method successfully calculated the reliability index while some failed to solve these problems. (C) 2019 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.

关键词： Reliability index Combination of algorithms particle swarm optimization algorithm Differential evolution algorithm

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Research on Virtual Machine Load Balancing Based on Improved particle swarm optimization

Research on Virtual Machine Load Balancing Based on Improved...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Li, Wei Jian, Tiantian Wang, Yanshan Ma, Xiang Xian Univ Posts & Telecommun Sch Automat Xian Peoples R China Beijing Urban Construct Yatai Grp Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728124858

In order to solve a series of data concurrency problems caused by massive terminal data access platform in power management system, the virtualization technology of cloud computing is analyzed, and a better load balancing method is proposed to reasonably place virtual resources on the physical host cluster. Firstly, the K-nearest neighbor classification algorithm is used to classify high-concurrence request tasks into groups. Then the particle swarm optimization algorithm based on discussion mechanism is used to solve the optimal server. compared with the common cloud center load balancing strategy, the improved algorithm can effectively optimize the load balancing in the power management cloud computing system.

关键词： cloud computing load balancing particle swarm optimization algorithm discussion mechanism KNN

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Multi-swarm Single-Objective particle swarm optimization to Extract Multiple-Choice Tests

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VIETNAM JOURNAL OF COMPUTER SCIENCE 2019年第2期6卷 147-161页

作者： Tram Nguyen Toan Bui Bay Vo Nong Lam Univ Fac Informat Technol Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HUTECH Fac Informat Technol Ho Chi Minh City Vietnam

This paper proposes the use of multi-swarm method in particle swarm optimization (PSO) algorithm to generate multiple-choice tests based on assumed objective levels of difficulty. The method extracts an abundance of tests at the same time with the same levels of difficulty and approximates the difficulty-level requirement given by the users. The experimental results show that the proposed method can generate many tests from question banks satisfying predefined levels of difficulty. Additionally, the proposed method is also shown to be effective in terms of many criteria when compared with other methods such as manually extracted tests, random methods and PSO-based methods in terms of execution time, standard deviation, the number of particles per swarm and the number of swarms.

关键词： Test questions bank multiple-choice tests particle swarm optimization algorithm multi-swarm optimization multi-objective optimization

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Analysis of Charging Load Acceptance Capacity of Electric Vehicles in the Residential Distribution Network

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第11期13卷 214页

作者： Hua, Yuan-Peng Wang, Shi-Qian Han, Ding Bai, Hong-Kun Wang, Yuan-Yuan Li, Qiu-Yan State Grid Henan Elect Power Co Econ & Technol Res Inst Zhengzhou 450015 Peoples R China

After large-scale electric vehicles (EVs) are connected to the residential distribution network, community charging has become one of the main bottlenecks at present, especially in old residential areas. Therefore, the current residential distribution network's ability to accept charging load and when and how the distribution network needs to be transformed have become meaningful research points. Based on the characteristics of the EVs' charging load of residential areas on a typical day and the size of the target annual charging load, this paper analyzes the acceptance capacity of the charging load of the distribution network on typical weekdays and weekends. By taking the charging load characteristics, the charging time of EVs, the voltage of each node of the distribution network, the line capacity, the transformation capacity of the distribution station as constraints, and the maximum capacity of the residential distribution network to accept the charging load as the objective function, the charging load capacity of the residential distribution network is analyzed. The particle swarm optimization algorithm is employed to solve the optimized mathematical model. The simulation uses an actual residential distribution network as an analysis example, and the partition optimization results prove the correctness and feasibility of this proposed method.

关键词： residential distribution network electric vehicle acceptance capacity charging load particle swarm optimization algorithm

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Fault Detection of UHV Converter Valve Based on Optimized Cost-Sensitive Extreme Random Forest

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ENERGIES 2022年第21期15卷 8059页

作者： Xiong, Fuqiang Cao, Chenhuan Tang, Mingzhu Wang, Zhihong Tang, Jun Yi, Jiabiao State Grid Hunan Extra High Voltage Substn Co Changsha 410004 Peoples R China State Grid Hunan Elect Power Co Ltd Substn Intelligent Operat & Inspect Lab Changsha 410004 Peoples R China Changsha Univ Sci & Technol Sch Energy & Power Engn Changsha 410114 Peoples R China

Aiming at the problem of unbalanced data categories of UHV converter valve fault data, a method for UHV converter valve fault detection based on optimization cost-sensitive extreme random forest is proposed. The misclassification cost gain is integrated into the extreme random forest decision tree as a splitting index, and the inertia weight and learning factor are improved to construct an improved particle swarm optimization algorithm. First, feature extraction and data cleaning are carried out to solve the problems of local data loss, large computational load, and low real-time performance of the model. Then, the classifier training based on the optimization cost-sensitive extreme random forest is used to construct a fault detection model, and the improved particle swarm optimization algorithm is used to output the optimal model parameters, achieving fast response of the model and high classification accuracy, good robustness, and generalization under unbalanced data. Finally, in order to verify its effectiveness, this model is compared with the existing optimization algorithms. The running speed is faster and the fault detection performance is higher, which can meet the actual needs.

关键词： converter valve cost-sensitive extreme random forest fault detection particle swarm optimization algorithm

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particle swarm optimization and Cuckoo Search Paralleled algorithm 3

Particle Swarm Optimization and Cuckoo Search Paralleled Alg...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Yang Xiaodong Cai Zefan Shunde Polytech Elect & Informat Engn Deptartment Shunde Peoples R China

ISBN: (纸本)9781509063529

particle swarm optimization algorithm and cuckoo search algorithm both are bionic swarm optimization algorithms, which are simple and convenient. They have been applied to many fields. However, the algorithms have obvious disadvantages. When they are applied to complex optimization problems, they cannot obtain the optimal solutions, so some measures must be adopted in order to improve their global search ability. In this paper, particle swarm optimization algorithm and cuckoo search algorithm evolve in parallel. At the end of each generation, the better solution of the two algorithms is selected as the global optimal solution. The simulation results show that the paralleled algorithm absorbs the advantages of the two algorithms, improves the global search ability and the average convergence speed, and enhances the robustness of the algorithm. The new algorithm is able to solve complex optimization problems more efficiently.

关键词： cuckoo search algorithm particle swarm optimization algorithm paralleled algorithm

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Evolutionary algorithms For k-Anonymity In Social Networks Based On Clustering Approach

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COMPUTER JOURNAL 2020年第7期63卷 1039-1062页

作者： Yazdanjue, Navid Fathian, Mohammad Amiri, Babak Iran Univ Sci & Technol Sch Ind Engn Tehran *** Iran

The usage of social networks shows a growing trend in recent years. Due to a large number of online social networking users, there is a lot of data within these networks. Recently, advances in technology have made it possible to extract useful information about individuals and the interactions among them. In parallel, several methods and techniques were proposed to preserve the users' privacy through the anonymization of social network graphs. In this regard, the utilization of the k-anonymity method, where k is the required threshold of structural anonymity, is among the most useful techniques. In this technique, the nodes are clustered together to form the super-nodes of size at least k. Our main idea in this paper is, initially, to optimize the clustering process in the k-anonymity method by means of the particle swarm optimization (PSO) algorithm in order to minimize the normalized structural information loss (NSIL), which is equal to maximizing 1-NSIL. Although the proposed PSO-based method shows a higher convergence rate than the previously introduced genetic algorithm (GA) method, it did not provide a lower NSIL value. Therefore, in order to achieve the NSIL value provided by GA optimization while preserving the high convergence rate obtained from the PSO algorithm, we present hybrid solutions based on the GA and PSO algorithms. Eventually, in order to achieve indistinguishable nodes, the edge generalization process is employed based on their relationships. The simulation results demonstrate the efficiency of the proposed model to balance the maximized 1-NSIL and the algorithm's convergence rate.

关键词： social network k-anonymity particle swarm optimization algorithm genetic algorithm structural information loss

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