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Accurate parameters extraction of photovoltaic models with multi-strategy gaining-sharing knowledge-based algorithm

INFORMATION SCIENCES 2024年 670卷

作者： Xiong, Guojiang Gu, Zaiyu Mohamed, Ali Wagdy Bouchekara, Houssem R. E. H. Suganthan, Ponnuthurai Nagaratnam Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Peoples R China Guizhou Univ Inst Engn Invest & Design Co Ltd Guiyang 550025 Peoples R China State Grid Sichuan Elect Power Co Meishan Power Supply Co Meishan 620000 Peoples R China Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Univ Sci & Technol Sch Business Zewail City Sci & Technol Giza 12588 Egypt Univ Hafr Al Batin Dept Elect Engn Hafar al Batin 31991 Saudi Arabia Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar

The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In this paper, a multi-strategy gaining-sharing knowledge-based algorithm (MSGSK) is developed to determine these parameters. In our previous work, it has been demonstrated that gaining-sharing knowledge-based algorithm (GSK) is well suited for solving the concerned problem. To enhance its performance, a parameter adjustment strategy is developed to adjust the knowledge rate and knowledge ratio of GSK. Besides, a backtracking differential mutation strategy by combining the mutation scheme of differential evolution and the updating scheme of backtracking search optimization algorithm is developed to enrich the population diversity. Furthermore, a strategy selection mechanism is introduced to integrate the former two strategies to balance exploration and exploitation in different stages of the evolutionary process. The suggested MSGSK algorithm is applied to five PV cases (SDM, DDM, Photowatt-PW201, STM6-40/36, and STP6-120/36). From the experimental data, it can be observed that MSGSK extracts the PV model parameters more precisely than the basic GSK. Furthermore, it exhibits faster convergence speed and higher accuracy compared to other advanced algorithms found in the literature.

关键词： Photovoltaic cell Parameter identification gaining-sharing knowledge-based algorithm Parameter adjustment strategy Backtracking search optimization Differential evolution

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Enhanced gaining-sharing knowledge-based algorithm

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RESULTS IN CONTROL AND OPTIMIZATION 2025年 19卷

作者： Jawad, Mohammed Adnan Roshdy, Heba Sayed Mohamed Mohamed, Ali Wagdy Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Chitkara Univ Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

This article suggests an Enhanced gaining-sharing knowledge-based algorithm (eGSK) to resolve unrestricted optimization minimization problems over a continuous space. This algorithm is based on the idea that people learn and share knowledge throughout their lives. The modification is fundamentally inspired by the principles of Adjust Selection Criteria, Modify Parameters Setup, and Escaping from Local Minimum Solutions, respectively. We conducted comparisons and statistical tests with the gaining-sharing knowledge-based algorithm (GSK) and other algorithms to verify and analyze the performance of the eGSK algorithm. We also performed numerical experiments on 29 test problem sets in 10, 30, 50, and 100 dimensions from the Congress on Evolutionary Computation (CEC) 2017 benchmark. The results were compared with three GSK variant algorithms, seven state-of-the-art algorithms, and GSK alongside components of the eGSK algorithm. According to test results, the eGSK algorithm performs exceptionally well at solving optimization problems with 30, 50, and 100 dimensions and is competitive in 10 dimensions. This means the proposed eGSK algorithm outperforms its competitors and achieves more competitive results, especially with high dimensions.

关键词： Human-related techniques Optimal solutions Meta-heuristics Optimizations gaining-sharing knowledge-based algorithm

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Improved binary gaining-sharing knowledge-based algorithm with mutation for fault section location in distribution networks

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第2期9卷 393-405页

作者： Xiong, Guojiang Yuan, Xufeng Mohamed, Ali Wagdy Chen, Jun Zhang, Jing Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Peoples R China Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Amer Univ Cairo Sch Sci & Engn Dept Math & Actuarial Sci New Cairo 11835 Egypt Oakland Univ Dept Elect & Comp Engn Rochester MI 48309 USA

Fault section location (FSL) plays a critical role in shortening blackout time and restoring power supply for distribution networks. This paper converts the FSL task into a binary optimization problem using the feeder terminal unit (FTU) information. The discrepancy between the reported overcurrent alarms and the expected overcurrent states of the FTUs is adopted as the objective function. It is a typical 0-1 combinatorial optimization problem with many local optima. An improved binary gaining-sharing knowledge-based algorithm (IBGSK) with mutation is proposed to effectively solve this challenging binary optimization problem. Since the original GSK cannot be applied in binary search space directly, and it is easy to get stuck in local optima, IBGSK encodes the individuals as binary vectors instead of real vectors. Moreover, an improved junior gaining and sharing phase and an improved senior gaining and sharing phase are designed to update individuals directly in binary search space. Furthermore, a binary mutation operator is presented and integrated into IBGSK to enhance its global search ability. The proposed algorithm is applied to two test systems, i.e. the IEEE 33-bus distribution network and the USA PG&E 69-bus distribution network. Simulation results indicate that IBGSK outperforms the other 12 advanced algorithms and the original GSK in solution quality, robustness, convergence speed, and statistics. It equilibrates the global search ability and the local search ability effectively. It can diagnose different fault scenarios with 100% and 99% success rates for these two test systems, respectively. Besides, the effect of mutation probability on IBGSK is also investigated, and the result suggests a moderate value. Overall, simulation results demonstrate that IBGSK shows highly promising potential for the FSL problem of distribution networks.

关键词： distribution network fault section location gaining-sharing knowledge-based algorithm

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A new method for parameter extraction of solar photovoltaic models using gaining-sharing knowledge based algorithm

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ENERGY REPORTS 2021年 7卷 3286-3301页

作者： Xiong, Guojiang Li, Lei Mohamed, Ali Wagdy Yuan, Xufeng Zhang, Jing Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Peoples R China Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Nile Univ Wireless Intelligent Networks Ctr WINC Sch Engn & Appl Sci Giza Egypt

For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner-intermediate or junior acquisition and sharing stage, and the second phase is the intermediate-expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E-04 +/- 2.18E-17, 9.8280E-04 +/- 8.72E-07, 2.4251E-03 +/- 1.04E-09, 1.7298E-03 +/- 6.25E-18, and 1.6601E-02 +/- 1.44E-16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Solar photovoltaic Parameter extraction gaining-sharing knowledge-based algorithm

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Hybridizing gaining-sharing knowledge and differential evolution for large-scale power system economic dispatch problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第2期10卷 615-631页

作者： Liu, Qinghua Xiong, Guojiang Fu, Xiaofan Mohamed, Ali Wagdy Zhang, Jing Al-Betar, Mohammed Azmi Chen, Hao Chen, Jun Xu, Sheng Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Peoples R China Guizhou Univ Inst Engn Invest & Design Co Ltd Guiyang 550025 Peoples R China Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Amer Univ Cairo Sch Sci & Engn Dept Math & Actuarial Sci New Cairo 11835 Egypt Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Fujian Prov Key Lab Intelligent Identificat & Cont Quanzhou 362216 Peoples R China Oakland Univ Dept Elect & Comp Engn Rochester MI 48309 USA Guizhou Elect Power Grid Dispatching & Control Ctr Guiyang 550002 Peoples R China

Economic dispatch (ED) of thermal power units is significant for optimal generation operation efficiency of power systems. It is a typical nonconvex and nonlinear optimization problem with many local extrema when considering the valve-point effects, especially for large-scale systems. Considering that differential evolution (DE) is efficient in locating global optimal region, while gain-sharing knowledge-based algorithm (GSK) is effective in refining local solutions, this study presents a new hybrid method, namely GSK-DE, to integrate the advantages of both algorithms for solving large-scale ED problems. We design a dual-population evolution framework in which the population is randomly divided into two equal subpopulations in each iteration. One subpopulation performs GSK, while the other executes DE. Then, the updated individuals of these two subpopulations are combined to generate a new population. In such a manner, the exploration and the exploitation are harmonized well to improve the searching efficiency. The proposed GSK-DE is applied to six ED cases, including 15, 38, 40, 110, 120, and 330 units. Simulation results demonstrate that GSK-DE gives full play to the superiorities of GSK and DE effectively. It possesses a quicker global convergence rate to obtain higher quality dispatch schemes with greater robustness. Moreover, the effect of population size is also examined.

关键词： differential evolution economic dispatch gaining-sharing knowledge-based algorithm power system valve-point effect

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A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling

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Computers, Materials & Continua 2021年第10期69卷 849-871页

作者： Said Ali Hassan Khalid Alnowibet Miral H.Khodeir Prachi Agrawal Adel F.Alrasheedi Ali Wagdy Mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route *** model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased *** obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight *** relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport *** mathematical model including stepped discounted fuel prices is *** problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic *** stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real *** similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact ***,metaheuristic approaches are widely used in treating such different optimization *** this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical *** algorithm basically based on the process of gaining and sharing knowledge throughout the human *** generated simulation runs of

关键词： Stochastic flight problem cost of refuelling ferrying strategy tankering discounted prices simulation procedure nonlinear mixed binary model metaheuristic algorithm gaining-sharing knowledge-based algorithm

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A nature-inspired meta-heuristic knowledge-based algorithm for solving multiobjective optimization problems

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JOURNAL OF ENGINEERING MATHEMATICS 2023年第1期143卷 5-5页

作者： Kapoor, Muskan Pathak, Bhupendra Kumar Kumar, Rajiv Jaypee Univ Informat Technol Dept Math Solan 173234 HP India Jaypee Univ Informat Technol Dept Elect & Commun Engn Solan 173234 HP India

The effectiveness of meta-heuristics has recently been well demonstrated. However, there will be a need for reliable algorithms that can handle problems in the real world. The multiobjective nature-inspired meta-heuristic knowledge-based (NMHK) algorithm is an advanced version of the gaining-sharing knowledge optimization (GSK) algorithm, which is available in the literature. NMHK is designed specifically for tackling multiobjective optimization problems (MOPs). knowledge-sharing algorithms are essential for easing the transfer of knowledge and expertise between people and groups. It is possible to significantly improve organizational learning, problem-solving, and decision-making by utilizing the collective knowledge and abilities of individuals. The NMHK algorithm, which is described in this paper, intends to improve the process of obtaining and spreading of knowledge. Moreover, the experimental results highlight the proposed NMHK algorithm's overall speedy performance, particularly when applied to realistic optimization problems.

关键词： gaining-sharing knowledge-based algorithm Meta-heuristics Multiobjective optimization problems (MOPs) Nature-inspired meta-heuristic knowledge-based algorithm (NMHK)

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