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Optimal reconfiguration design and HIL validation of hybrid PV-TEG systems via improved firefly algorithm

ENERGY 2024年 286卷

作者： Yang, Bo Zhang, Zijian Zhang, Jie Cheng, Xianlong Li, Jiale Shu, Jiale Wang, Jiana Wang, Xuetong Li, Yulin Wang, Jingbo Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China China Southern Power Grid Honghe Power Supply Bur Mengzi 661100 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Lancs England

Partial shading conditions (PSCs) caused by clouds, trees, buildings, and other factors can lead to uneven irradiance on the photovoltaic (PV) panel, resulting in reduced power output and decreased service life of PV systems. To deal with this problem, a reconfiguration method for hybrid photovoltaic-thermoelectric generation (PV-TEG) systems based on an improved firefly algorithm (IFA) is proposed in this paper. Firstly, the method uses PV-TEG hybrid system to make full use of the waste heat of traditional PV systems to improve the utilization rate of solar energy. Secondly, IFA is employed to adjust the electrical connection between the modules to reduce the adverse effects of PSC and increase the total output power of the system. To validate the effectiveness of this method, three different scale PV-TEG hybrid systems of 4 x 4, 9 x 9, and 15 x 9 are reconfigured under practical and standard shading conditions, and the results are compared with those obtained using PSO, GA, BWOA, AO, and ACO. The results demonstrate that the IFA-based reconfiguration enhances the output power by 31.19 %, 21.49 %, and 21.84 % for 4 x 4, 9 x 9, and 15 x 9 arrays compared with that without reconfiguration, respectively. Finally, hardware-in-the-loop (HIL) experiments further verify the hardware feasibility of the proposed method.

关键词： Hybrid PV-TEG system Reconfiguration Partial shading condition improved firefly algorithm Hardware-in-loop

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Minimizing Electricity Fuel Cost of Thermal Generating Units by Using improved firefly algorithm

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JOURNAL OF ENGINEERING AND TECHNOLOGICAL SCIENCES 2019年第1期51卷 133-147页

作者： Thang Trung Nguyen Bao Quoc Nguyen Phuong Duy Nguyen Chiem Trong Hien Ton Duc Thang Univ Power Syst Optimizat Res Grp Fac Elect & Elect Engn 19 Nguyen Huu Tho St Ho Chi Minh City 700000 Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn 19 Nguyen Huu Tho St Ho Chi Minh City 700000 Vietnam Saigon Univ Fac Elect Telecommun 273 An Duong Vuong St Ho Chi Minh City 700000 Vietnam Ho Chi Minh City Univ Food Ind Fac Elect & Elect Technol 140 Le Trong Tan St Ho Chi Minh City 700000 Vietnam

This paper presents the application of an improved firefly algorithm (IFA) for minimizing total electricity generation fuel cost while all loads are supplied by thermal generating units. The proposed IFA was developed by combining two proposed improvements of the firefly algorithm (FA), i.e. improvement of the distance between two considered solutions and improvement of the new-solution production technique. The effect of each proposed improvement on the conventional firefly algorithm (FA) and the performance of IFA were investigated in two study cases, i.e. single- and multi-fuel option based thermal generating units. In the first case, three different systems with three, six and twenty units were employed, while a ten-unit system with four different loads was tested in the second case. The comparison results between IFA and existing methods, including three other FA variants, revealed that the two proposed improvements of FA are very efficient and make IFA a very promising meta-heuristic algorithm for minimizing fuel cost of thermal generating units.

关键词： improved firefly algorithm multi-fuel single-fuel thermal generating units total fuel

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Parameters Identification of Ship Heave and Pitch Motion Model Based on improved firefly algorithm

Parameters Identification of Ship Heave and Pitch Motion Mod...

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第43届中国控制会议

作者： Xiufeng Zhang Xiaopeng Gao Yunong Chen Yingjie Yu Navigation College Dalian Maritime University Quanzhou Maritime Safety Administration of P.R.C

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Ships oscillate periodically while navigating at sea. To study the impact of the ocean environment on ship transportation, it is necessary to build accurate mathematical models of ship motion. Based on the theory of rigid body motion,build the mathematical models for ship heave and pitch, and discretize them;By introducing sigmoid function, roulette wheel strategy, and chaos theory, an improved Chaotic firefly algorithm Based on Roulette Wheel Selection Strategy(CRSFA) is proposed, which utilizes representative unimodal and multimodal test functions to optimize the algorithm through different dimensions. Compared with the original algorithm, the improved firefly algorithm based on Roulette Wheel Selection Strategy(CRSFA) proposed effectively avoids algorithm oscillations in this paper, accelerates algorithm convergence speed, and improves the algorithm's global searching ability. Based on the CFD numerical simulation data of the KCS ship model in calm water and 2 to 3 level sea conditions, parameter identification are carried out for the heave and pitch motion of the KCS ship model. The identified parameters are used to simulate and predict the heave and pitch motion of the ship using CRSFA and original FA separately. The simulation results show that fitted with the real time motion trend, verifying the feasibility of the identification scheme proposed in this paper.

关键词： Ship heave and pitch motion parameter identification improved firefly algorithm sigmoid function Roulette Wheel Selection Strategy chaotic theory

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Cooperative jamming resource allocation model based on the improved firefly algorithm 22

Cooperative jamming resource allocation model based on the i...

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering

作者： Zekun Yao Tianhao Liu Chao Wang State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System National University of Defense Technology China

ISBN: (纸本)9781450397148

In this paper, the jamming resource allocation technology of netted radar is studied. A complete jamming resource allocation model including jamming beam, jamming power and other influencing factors is established. Then the objective function of radar detection probability is constructed, and the improved firefly algorithm is used to optimize the jamming resource allocation model and random keys are used in this paper to improve the coding mode of firefly algorithm. At the end of the paper, results show that in the condition of limited jamming resources, the model and algorithm in this paper can achieve a better effectiveness of resource allocation and provide a new idea for future research.

关键词： improved firefly algorithm Netted radar Cooperative jamming Jamming resource allocation

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Study on Optimization of Interior Ballistic Performance of Cased Telescoped Ammunition Based on improved firefly algorithm

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PROPELLANTS EXPLOSIVES PYROTECHNICS 2023年第2期48卷

作者： He, Xin-Jia Li, Chao Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing 210094 Peoples R China

Considering that the evolution operators of common optimization algorithms such as genetic algorithm has a poor convergence speed and solution accuracy in the interior ballistic performance optimization of artillery, an improved firefly algorithm is selected for the interior ballistic performance optimization of cased telescoped ammunition (CTA). It is applied to the interior ballistic mathematical model of CTA to carry out single-objective optimization and multi-objective optimization respectively. The results show that in the single-objective optimization, the muzzle velocity obtained by using the improved firefly algorithm is larger than other commonly used optimization algorithms. Under the constraints of maximum chamber pressure and relative burnout point, the muzzle velocity can be increased by about 30 m/s. In the multi-objective optimization, three sub-objectives of muzzle velocity, ballistic efficiency and muzzle pressure are considered respectively, and the multi-objective firefly algorithm is applied to comprehensively optimize the interior ballistic performance of CTA. Then, the results are compared with the commonly used multi-objective optimization algorithms, such as non-dominated sorting genetic algorithm version II (NSGA-II) and non-dominated sorting genetic algorithm version III (NSGA-III), the solution results are a series of Pareto optimal solutions. The obtained optimization results improved the interior ballistic performance and launch safety, providing reference for the reasonable matching of charge structure parameters and the selection of optimization algorithm upon the interior ballistic performance optimization of CTA to some extent.

关键词： Cased telescoped ammunition improved firefly algorithm Multi-objective optimization problem Interior Ballistics Charge structure parameters

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Autonomous resource allocation of smart workshop for cloud machining orders

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2021年第2期35卷 226-239页

作者： Hui, Jizhuang Lei, Jingyuan Ding, Kai Zhang, Fuqiang Lv, Jingxiang Changan Univ Inst Smart Mfg Syst Middle Sect Naner Huan Rd Xian Xian 710064 Shaanxi Peoples R China Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian 710064 Shaanxi Peoples R China

In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

关键词： Cloud machining orders fuzzy analysis improved firefly algorithm particle swarm optimization resource allocation

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Research on intelligent prediction of hydrogen pipeline leakage fire based on Finite Ridgelet neural network

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第55期47卷 23316-23323页

作者： Zhao, Bin Li, Shasha Gao, Diankui Xu, Lizhi Zhang, Yuanyuan Liaoning Petrochem Univ Sch Mech Engn Fushun 113001 Liaoning Peoples R China

In order to effectively predict fire risk of hydrogen pipeline leakage and improve the safe level of hydrogen pipeline, the finite Ridgelet neural network optimized by improved firefly algorithm is constructed. Related researches on pipeline leakage fire risk and corresponding prediction models are summarized. The finite Ridgelet neural network is constructed by using finite Ridgelet function as excitation function of node in hidden layer, and the structure of finite Ridgelet neural network is established. The improved firefly algorithm based on location update mechanism is designed to optimize the parameters of finite Ridgelet neural network. And then the fire risk level prediction procedure of hydrogen pipeline is designed. Finally, forty training samples and ten testing samples are selected to carry out fire risk level prediction analysis, results show that the proposed finite Ridgelet neural network optimized by improved firefly algorithm has advantages in prediction precision and accuracy, which can effectively predict the safety status of hydrogen pipeline. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Finite ridgelet neural network improved firefly algorithm Hydrogen pipeline Fire risk

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Risk assessment of refinery unit maintenance based on fuzzy second generation curvelet neural network

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ALEXANDRIA ENGINEERING JOURNAL 2020年第3期59卷 1823-1831页

作者： Zhao, Bin Chen, Hao Gao, Diankui Xu, Lizhi Liaoning Shihua Univ Sch Mech Engn Fushun 113001 Liaoning Peoples R China

To ensure safety of maintenance of refinery unit and improve reliability of refinery unit, the risk assessment model of refinery unit maintenance is established based on fuzzy second generation curvelet neural network optimized by improved firefly algorithm. Firstly, the fuzzy second generation curvelet neural network is constructed based on second generation curvelet transform, artificial neural network, and fuzzy theory. Secondly, the training algorithm of fuzzy second curvelet neural network is designed based on improved firefly algorithm, the traditional firefly algorithm is improved based on chaotic mapping. And the corresponding algorithm procedure is designed. Finally thirty-five gasoline hydrodesulphurization units of petrochemical companies are used as research objects to carry out risk assessment of maintenance based on the proposed method and other two existing methods, results show that the proposed fuzzy second curvelet neural network has better computing precision and efficiency, which can correctly and efficiently evaluate the risk level of maintenance of refinery unit. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Second generation curvelet neural network improved firefly algorithm Maintenance Refinery unit

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An Energy Optimal Dispatching Model of an Integrated Energy System Based on Uncertain Bilevel Programming

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ENERGIES 2020年第2期13卷 477页

作者： Song, Xueying Lin, Hongyu De, Gejirifu Li, Hanfang Fu, Xiaoxu Tan, Zhongfu North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China

An integrated energy system (IES) involving a large number of decision-makers causes problems of bad coordination between energy sub-networks and the IES and it is not able to fully consider the multi-energy complementarity among multiple decision-makers. In this context, firstly, this paper constructs an energy optimal dispatching model of an IES based on uncertain bilevel programming. The upper model takes the transformation matrix of energy hubs as the upper decision-maker, taking the minimum operation cost of the IES in the form of confidence as the objective function;the lower model takes each optimal operation plan of the electric power sub-network, the thermal energy sub-network, and the gas energy sub-network as the lower decision-makers, aiming at the operation economy of each sub-network and considering their operation as necessary constraints. Secondly, a firefly algorithm with chaotic search and an improved light intensity coefficient is designed to improve the proposed model. An empirical analysis was conducted on a pilot area of an integrated energy system in Hebei Province. The results show the following: (1) The typical daily operating cost of the integrated energy system in winter is lower than that in summer;(2) under the same load level, the typical winter and summer running costs of the integrated energy system are lower than that of the traditional microgrid;(3) compared with the particle swarm optimization algorithm, the improved firefly algorithm proposed in the paper has obvious advantages both in terms of running cost and solution time;and (4) when the confidence of the objective function and the constraints increases, the operating cost of various schemes also increase. The above results validate the effectiveness of the energy optimal dispatching model of the IES and the economy of the system operation under the multiple decision-maker hierarchy.

关键词： uncertain bilevel programming integrated energy system IES operating cost improved firefly algorithm

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Prediction of Aircraft Optimal Slip Rate Based on IFABP Neural Network

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2019年第3期22卷 459-468页

作者： Wu, Huawei Zhang, Yuanjinn Hubei Univ Arts & Sci Hubei Key Lab Power Syst Design & Test Elect Vehi Xiangyang 441053 Peoples R China Hubei Univ Arts & Sci Sch Automot & Traff Engn Xiangyang 441053 Peoples R China

In view of the nonlinear property of aircraft braking process and the complexity of on-line estimation, a prediction method of optimal slip rate based on improved firefly BP neural network (IFABP) is proposed. By improving the cross factor of the firefly algorithm, the operation speed of the firefly algorithm is enhanced. The global optimization ability of firefly algorithm is used to optimize the weights and thresholds of BP neural network, and the prediction ability of BP neural network is improved. The improved BP neural network prediction model of firefly is constructed based on the slip rate-coefficient data under different working conditions, and the optimal slip rate identification system is constructed. The simulation results in the aircraft brake system verify the feasibility and effectiveness of the proposed optimal slip rate identification method.

关键词： Prediction of Aircraft Optimal Slip Rate BP Neural Network improved firefly algorithm

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