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Extraction of Single Diode Model Parameters of Solar Cells and PV Modules by Combining an intelligent optimization Algorithm with Simplified Explicit Equation Based on Lambert W Function

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ENERGIES 2023年第14期16卷 5425-5425页

作者： Li, Jianing Qin, Cheng Yang, Chen Ai, Bin Zhou, Yecheng Sun Yat Sen Univ Sch Mat Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Photovolta Technol Guangzhou 510006 Peoples R China

In this paper, the explicit equation of the single diode model (SDM) expressed by the Lambert W function was reduced to its simplified form through variable replacement;then the simplified explicit equation was combined with an intelligent optimization algorithm to estimate the SDM parameters of solar cells and PV modules. To evaluate the parameter extraction performance of the new method, eight typical intelligent optimization algorithms were combined with the implicit, explicit, and simplified explicit equation to extract the SDM parameters of a solar cell and three types of PV modules. The results show that the new method not only improves the accuracy of parameter extraction but also enhances the robustness and convergence speed. Most importantly, the new method can nearly improve the parameter extraction accuracy of a poor-performing algorithm in traditional methods to the level of other well-performing algorithms without enhancing the algorithm itself. In a word, this study offers a new choice for a more accurate and reliable extraction of SDM parameters from both solar cells and PV modules.

关键词： single diode model parameter estimation solar cells photovoltaic modules Lambert W function intelligent optimization algorithms

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Chaos enhanced intelligent optimization-based novel deception detection system

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CHAOS SOLITONS & FRACTALS 2023年 166卷

作者： Bingol, Harun Alatas, Bilal Malatya Turgut Ozal Univ Dept Software Engn TR-44200 Malatya Turkey Firat Univ Dept Software Engn TR-23119 Elazig Turkey

Today, with the developing technology, access methods to information have also changed. Internet blogs and news sites, social media, etc. replace the traditional information access tools such as TV, radio, newspaper, and magazines. Cheaper and faster access than traditional methods and easy access from anywhere where the internet is available are the main factors in changing the access method to information. Besides, information spreads rapidly on the internet without proving the accuracy. There may be many reasons for the distribution of misinformation in this way, such as commercial, political, and economic. Information with fake content and deception purposes negatively affects both the person and the society. It is extremely important to detect deceptive information in textual data and researchers continue to propose new models in order to obtain efficient results in terms of many metrics. This paper proposes a new approach for deception detection problems. The deception detection problem was considered an optimization problem for the first time. Optics Inspired Opti-mization (OIO), Grey Wolf optimization (GWO), and Chaos Based Optics Inspired optimization (CBOIOs) are adapted and modeled for the first time in the deception detection problem. Deception detection models proposed in this article include data preprocessing, adapting optimization methods, and testing stages. An experimental evaluation and comparison of the proposed model and seven supervised machine learning algorithms are per-formed within two different data sets depending on four evaluation metrics (Accuracy, Recall, Precision, and F -Measure). Results show that CBOIOs are more effective than OIO, GWO, and other machine learning algorithms for this problem.

关键词： Deception detection intelligent optimization algorithms Chaos Machine learning

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Analysis of Credit Risk Assessment Model for Financial Data Based on intelligent optimization Algorithm

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Procedia Computer Science 2023年 228卷 421-428页

作者： Xiaolan Qu Yunnan University of Finance and Economics Kunming 650221 Yunnan China

With the rapid development of China's social economy, the financial industry accounts for an increasing proportion of the national economy, and the financial industry is one of the key factors of a country's modernization level and comprehensive national strength. Therefore, how to effectively evaluate financial institutions is particularly important. intelligent optimization algorithms can provide investors with more accurate, timely and comprehensive information, which can help them make accurate investment decisions, and this can also help improve the financial institutions' ability to control and manage risks in the financial market. Based on the analysis of intelligent optimization algorithm, this paper constructs a set of effective assessment model for financial data credit risk, which can reduce the default rate of financial institutions through the assessment of financial data credit risk, thus improving the efficiency of capital use and thus promoting the healthy development of China's financial industry.

关键词： intelligent optimization algorithms Financial Data Credit Risk Evaluation Model Analysis

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Optimal Energy Storage Configuration Method For Wave Energy Power Generation System 3

Optimal Energy Storage Configuration Method For Wave Energy ...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Wang, Lixin Li, Ye Yang, Shuai Chen, Yating He, Wenbin Ma, Xiaoting Inner Mongolia Power Grp Hohhot Peoples R China Inner Mongolia Elect Power Econ & Technol Res Ins Hohhot Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The rapid and random changes in wave characteristics make it difficult to meet the requirements for secure and stable operation of the power grid, resulting in low wave energy utilization. The inherent advantage of energy storage can address the grid connection and improve wave energy consumption, hence proposing an energy storage optimization configuration method for wave energy generation system. The goal is to minimize the construction and operational costs of the wave energy storage system by taking into account constraints such as the state of charge of the energy storage, energy storage power, and the grid operating conditions. An energy storage configuration optimization model is established. The Whale optimization Algorithm and Genetic Algorithm are applied to solve the inner and outer layer models, optimizing the system's daily operation strategy, energy storage access points, and capacity to increase wave energy utilization while minimizing system costs. Simulation results show that in the IEEE 33-node system, the proposed method can address the energy storage layout, and capacity configuration issues in wave energy power generation system(WEPGS), verifying the effectiveness and feasibility of the proposed method.

关键词： Energy storage system Wave energy Optimal configuration intelligent optimization algorithms

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Research on Control Methods and Path Planning for AUVs

Research on Control Methods and Path Planning for AUVs

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2024 International Conference on Guidance, Navigation and Control

作者： Gao, Chunxiao Tan, Fuxiao Shanghai Maritime Univ Shanghai 201306 Peoples R China

ISBN: (纸本)9789819622542;9789819622528;9789819622511

Autonomous Underwater Vehicles (AUVs) are playing an increasingly prominent role in the field of marine scientific research. Control methods and path planning algorithms, as the core technologies of AUVs, have a direct impact on their application prospects and are of great significance and worth studying. The purpose of this paper is to discuss and outline the development of control methods and path planning algorithms for AUVs in the last five years. Firstly, this paper describes the fundamentals, characteristics, as well as advantages and disadvantages of several typical control methods and several types of representative path-planning algorithms, surveys related work, and presents the latest research progress in this field. Secondly, this paper proposes some promising future research directions that are worth studying in the field of AUVs and provides guidance for its application and development.

关键词： Autonomous Underwater Vehicles (AUVs) Path planning Control method intelligent optimization algorithms

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Particle swarm optimization algorithm for dynamic synchronization of smart grid

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第2期44卷 3940-3959页

作者： Zulueta, Asier Azurmendi, Iker Rey, Nerea Zulueta, Ekaitz Fernandez-Gamiz, Unai Univ Basque Country Nucl Engn & Fluid Mech Dept Vitoria Spain Univ Basque Country Syst Engn & Automat Control Dept Vitoria Spain

In recent decades, traditional energy generation and consumption have undergone a transformation to cleaner energy sources. The transition to a sustainable energy system based on efficiency and renewable energies will thus require the replacement of a complex and heavily implemented energy system with one based on concepts such as microgrids. A microgrid is a controlled small-scale power system and the elements that make up it are: distributed generation systems;energy storage systems;load management techniques;monitoring systems, etc. Consequently, since ac devices are used, synchronization criteria must be satisfied to switch operation between them. The synchronization criteria consist of making the values of the phase-angle difference, slip frequency, and voltage difference as small as possible. This is very important to ensure the correct operation of the grid. In the other hand, the use of Particle Swarm optimization (PSO) algorithm has been increasing, because of their simplicity and efficiency in engineering optimization problems. Therefore, this paper proposes an active synchronizing control scheme that allows to synchronize a generator set to the grid and a particle swarm optimization algorithm, which makes the synchronizing control as efficient as possible. With the implemented control and with the help of the synchronization algorithm, improvements in the reduction of settling time (from 9 seconds to 3 seconds) are obtained in comparison to other controls in the literature.

关键词： Smart grid microgrid synchronization genset Particle swarm optimization (PSO) genetic algorithms intelligent optimization algorithms

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Evolution of Controllers Under a Generalized Structure Encoding/Decoding Scheme With Application to Magnetic Levitation System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第9期69卷 9655-9666页

作者： Xin, Bin Wang, Yipeng Xue, Wenchao Cai, Tao Fan, Zhun Zhan, Jiaoyang Chen, Jie Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Shantou Univ Minist Educ Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China

Evolutionary search has been widely implemented for the adjustment of controllers' parameters. Nevertheless, the structure of controllers, which has a more important role in control systems, has been seldom studied. To this end, an evolutionary design method of controllers is proposed to optimize both structures and parameters simultaneously in this article. A controller is made up of a combination of some basic controller components and relevant parameters. The design of controllers can be transformed into an optimization problem involving the structure (represented by discrete vectors) and parameters (represented by real numbers). A generalized structure encoding/decoding scheme is developed. Guided by the performance indicators, intelligent algorithms for both combinatorial and numerical optimization are employed to iteratively and cooperatively evolve the controller structure and parameters, respectively. In order to effectively reduce some redundant or infeasible solutions, a set of generation rules for the controller structure are put forward, which also ensures the feasibility of the structure. Furthermore, this method is applied to a magnetic levitation ball system with nonlinear dynamics and external disturbance. Both simulation and experiment results demonstrate the effectiveness and practicability of the proposed method.

关键词： optimization Design methodology Control systems Tuning Magnetic levitation Signal processing Periodic structures Automatic design of controllers disturbance-rejection intelligent optimization algorithms magnetic levitation system (MLS) mixed-variable optimization

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A Study of Multi-Constraint Planning Problem for Time Series Data with Correlation 4

A Study of Multi-Constraint Planning Problem for Time Series...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Yu, Fangda Yu, Xiaotong Northeast Forestry University Mechanical and Electrical Engineering Heilongjiang Harbin China

ISBN: (纸本)9798350352931

This paper studies the multi-constraint optimization problem under the background of time series data correlation, which is particularly important in the fields of finance, energy and meteorology. Aiming at the multiple constraints that need to be considered at the same time in practical applications, such as time, resources and risks, this paper proposes an optimization algorithm that combines time series correlation analysis. Through theoretical analysis and model design, the algorithm has been verified to be effective in multiple application scenarios, especially in improving financial forecasting and decision-making efficiency. Then, we use the financial market time series data into the algorithm of this paper for optimization experiments. The experimental results show that our new algorithm can not only improve the accuracy of similar financial market prediction models, but also significantly improve the decision-making efficiency. Finally, this paper looks forward to the improvement of the method in complex constraints and the direction of more efficient algorithm, which provides ideas for further research. © 2024 IEEE.

关键词： dynamic dependency analysis intelligent optimization algorithms multi-constraint optimization multi-objective optimization time series correlation

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Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy

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ENERGY 2022年 258卷

作者： Gao, Yang Ma, Shaoxiu Wang, Tao Miao, Changhong Yang, Fan Henan Univ Key Res Inst Yellow River Civilizat & Sustainable Kaifeng 475001 Peoples R China Henan Univ Collaborat Innovat Ctr Yellow River Civilizat Hena Kaifeng 475001 Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Desert & Desertificat Lanzhou 730000 Peoples R China

Distributed joint complementary wind generation is a powerful way to reduce the variability and improve the penetration of wind power. But distributed wind farm siting is a complex problem of nonlinear and global combinatorial optimization. Various correlation coefficients were used to measure the complementarity of renewable energy, which were not intuitive and accurate in former studies, and traditional siting methods are based on the abundance of wind energy, siting results cannot fully utilize the complementary potential of distributed wind energy. In this study, intelligent optimization algorithm is adopted to select the location of distributed wind farm in China mainland from the perspective of utilizing the complementarity of wind energy. The result showed that the existing wind power bases have obvious complementary superiority, combined power generation can improve availability from about 57.61% to 66.93%, reduce the long/short term variability confidence by about 40%. Siting result indicated that central and eastern Inner Mongolia, Xinjiang and Hexi Corridor are still key regions for the future wind farms construction. This study provides more direct evidence of the complementarity of existing wind power bases, and the proposed siting methodology can be applied in different regions worldwide.

关键词： Onshore wind energy Siting Spatial and temporal variability intelligent optimization algorithms

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Cost-Minimized Partial Computation Offloading in Cloud-Assisted Mobile Edge Computing Systems

Cost-Minimized Partial Computation Offloading in Cloud-Assis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers. © 2023 IEEE.

关键词： cloud computing computation offloading intelligent optimization algorithms Lévy flights Mobile edge computing

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