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coyote optimization algorithm for the parameter extraction of photovoltaic cells

SOLAR ENERGY 2019年 194卷 656-670页

作者： Chin, Vun Jack Salam, Zainal Univ Southampton Malaysia Sch Elect & Comp Sci Iskandar Puteri 79200 Malaysia Univ Teknol Malaysia Sch Elect Engn Ctr Elect Energy Syst Johor Baharu 81310 Malaysia Univ Teknol Malaysia Inst Future Energy Johor Baharu 81310 Malaysia

In this paper, a new and powerful metaheuristic optimization technique known as the coyote optimization algorithm (COA) is proposed for the parameter extraction of the PV cell/module. It is utilized to identify the parameters of the single diode and two-diode models. Inspired by the social norms adopted by the coyotes to ensure the survivability of their species, the COA possesses several outstanding merits such as low number of control parameters, ease of implementation and diverse mechanisms for balancing exploration and exploitation. For physically meaningful solutions, a set of parametric constraints is introduced to prevent the coyotes from straying outside of the predefined boundaries of the search space. Extensive tests indicate that the proposed optimizer exhibits superior accuracy compared to other state-of-the-art EA-based parameter extraction methods. It achieved root-mean-square error (RSME) as low as 7.7301E-04 A and 7.3265E-04 A, for the single-diode and two-diode models, respectively. Moreover, the algorithm maintains outstanding performance when tested on an assortment of modules of different technologies (i.e. mono-crystalline, poly-crystalline, and thin film) at varying irradiance and temperature. The standard deviations (STDs) of the fitness values over 35 runs are measured to be less than 1 x 10(-5) for both models. This suggests that the results produced by the algorithm are highly consistent. With these outstanding merits, the COA is envisaged to be a competitive option for the parameter extraction problem of PV cell/module.

关键词： Solar photovoltaic Parameter extraction coyote optimization algorithm Evolutionary algorithm Equivalent circuit model

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Developed coyote optimization algorithm and its application to optimal parameters estimation of PEMFC model

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ENERGY REPORTS 2020年 6卷 1106-1117页

作者： Yuan, Zhi Wang, Weiqing Wang, Haiyun Yildizbasi, Abdullah Xinjiang Univ Minist Educ Engn Res Ctr Renewable Energy Power Generat & Gri Urumqi 830047 Xinjiang Peoples R China Ankara Yildirim Beyazit Univ AYBU Dept Ind Engn TR-06010 Ankara Turkey

In this paper, a new approach has been introduced for optimal parameter estimation of a proton exchange membrane fuel cell (PEMFC) model. The main purpose is to minimize the total error between the empirical data and the proposed method by optimal parameter selection of the model. The methodology is based on using a newly introduced developed version of the coyote optimization algorithm (DCOA) for determining the value of the unknown parameters in the model. Two different PEMFC models including 2 kW Nexa FC and 6kW NedSstack PS6 FC are adopted for validation and the results are compared with the empirical data and some well-known methods including conventional COA, Seagull optimization algorithm, and (N + lambda) - ES algorithm to show the proposed method's superiority toward the literature methods. The final results declared a satisfying agreement between the proposed DCOA and the empirical data. The results also declared the excellence of the presented method toward the other compared methods. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Proton exchange membrane fuel coyote optimization algorithm Developed Model parameter estimation Electrical circuit equivalent

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A novel optimal PI^λDND²N² controller using coyote optimization algorithm for an AVR system

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2022年 26卷

作者： Moschos, Ioannis Parisses, Constantinos Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece

In this paper, a novel (PIDNDN2)-D-lambda-N-2 controller is designed for an Automatic Voltage Regulator (AVR) in a power system and is studied in this paper. AVR is responsible for keeping the terminal voltage of a syn-chronous generator at a specified interval. The proposed control design is achieved by deploying for the first-time the coyote optimization algorithm (COA) in an AVR system by optimizing a novel objective function. A comparison is made among the proposed controller and FOPID, PID, PIDA and PIDD2 con-trollers optimized by others metaheuristic algorithms in the literature. The comparison is deducted in terms of transient response and disturbance rejection. From the simulations it is concluded that the pro-posed method outperforms other methods in the literature. Furthermore, the stability of the proposed design is examined via the location of the closed-loop poles and frequency response. Moreover, the robustness of the controller is tested under model uncertainties. In both tests the proposed method dis-plays an outstanding performance. (c) 2021 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Automatic voltage regulator Fractional controller PID controller coyote optimization algorithm Transient response

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Multiobjective coyote optimization algorithm for techno-economic simultaneous placement of DGs and FCLs in distribution networks

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第11期31卷

作者： Abou El-Ela, Adel A. El-Sehiemy, Ragab A. Shaheen, Abdullah M. Elllien, Aya R. Minoufiya Univ Fac Engn Shibin Al Kawm Egypt Kafrelsheikh Univ Fac Engn Kafr Al Sheikh Egypt Suez Univ Fac Engn Suez Egypt

In this paper, a multi-objective coyote optimization algorithm (MOCOA) is proposed for techno-economic benefits of simultaneous placement of fault current limiters (FCLs) and distributed generation (DGs) units in distribution grids. The proposed MOCOA is employed for mitigating the loss of power in distribution lines, minimizing the fault current levels, minimizing the economic costs of FCL size, enhancing the total voltage stability index, and reducing the total voltage deviation. The MOCOA is enhanced by emerging the concept of pareto-optimal dominance to store the distinguished solutions in each generation. Finally, TOPSIS, a technique is implemented to find the compromise solution to fulfill the operator requirements. Applications are carried out in three distribution systems. The simulation results show the great effectiveness of the proposed MOCOA in acquiring several alternatives with appropriate technical improvements in power losses, voltage stability, fault level reduction, and economical FCL size.

关键词： coyote optimization algorithm distributed generation fault current limiters pareto solution TOPSIS

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A Feature Selection Approach Based on Improved Binary coyote optimization algorithm

A Feature Selection Approach Based on Improved Binary Coyote...

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2021中国智能自动化大会(CIAC 2021)

作者： Zhicheng Zhang Jianqin Yin Beijing University of Posts and Telecommunications

Feature selection is one of the essential approaches for machine learning and data mining to eliminate the irrelevant and redundant features from the datasets. As the wrapper-based feature selection can be considered as a binary optimization problem, an improved binary coyote optimization algorithm(IBCOA)is proposed and applied for solving the feature selection problems in this *** the proposed algorithm, two Sigmoidal transfer functions are utilized respectively for updating the social condition of each coyote and dealing with the birth of new coyotes. An alpha based mutation strategy is applied for updating and binarizing the coyotes in a pack. The proposed approach is evaluated using twelve well-known datasets from the UCI machine learning repository. The experimental results confirm that the proposed approach outperforms the other two feature selection approaches based on the conventional COA and a V-shaped binary COA.

关键词： Feature selection Classification coyote optimization algorithm Binary optimization

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Simulation-Based coyote optimization algorithm to Determine Gains of PI Controller for Enhancing the Performance of Solar PV Water-Pumping System

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ENERGIES 2020年第17期13卷 4473页

作者： Arfaoui, Jouda Rezk, Hegazy Al-Dhaifallah, Mujahed Ibrahim, Mohamed N. Abdelkader, Mami Univ Tunis ELMANAR Natl Sch Engn BP 37 Tunis 1002 Tunisia Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj 11991 Saudi Arabia Minia Univ Fac Engn Elect Engn Deprtment Al Minya 61519 Egypt King Fahd Univ Petr & Minerals Syst Engn Dept Dhahran 31261 Saudi Arabia Univ Ghent Dept Electromech Syst & Met Engn B-9000 Ghent Belgium Univ Ghent FlandersMake Corelab EEDT MP B-3001 Leuven Belgium Kafrelshiekh Univ Elect Engn Dept Kafr Al Sheikh 33511 Egypt Univ Tunis El Manar Fac Sci Dept Phys BP 37 Tunis 1002 Tunisia

In this study, a simulation-based coyote optimization algorithm (COA) to identify the gains of PI to ameliorate the water-pumping system performance fed from the photovoltaic system is presented. The aim is to develop a stand-alone water-pumping system powered by solar energy, i.e., without the need of electric power from the utility grid. The voltage of the DC bus was adopted as a good candidate to guarantee the extraction of the maximum power under partial shading conditions. In such a system, two proportional-integral (PI) controllers, at least, are necessary. The adjustment of (Proportional-Integral) controllers are always carried out by classical and tiresome trials and errors techniques which becomes a hard task and time-consuming. In order to overcome this problem, an optimization problem was reformulated and modeled under functional time-domain constraints, aiming at tuning these decision variables. For achieving the desired operational characteristics of the PV water-pumping system for both rotor speed and DC-link voltage, simultaneously, the proposed COA algorithm is adopted. It is carried out through resolving a multiobjective optimization problem employing the weighted-sum technique. Inspired on theCanis latransspecies, the COA algorithm is successfully investigated to resolve such a problem by taking into account some constraints in terms of time-domain performance as well as producing the maximum power from the photovoltaic generation system. To assess the efficiency of the suggested COA method, the classical Ziegler-Nichols and trial-error tuning methods for the DC-link voltage and rotor speed dynamics, were compared. The main outcomes ensured the effectiveness and superiority of the COA algorithm. Compared to the other reported techniques, it is superior in terms of convergence rapidity and solution qualities.

关键词： simulation-based optimization coyote optimization algorithm water pumping energy efficiency

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coyote optimization algorithm: A new metaheuristic for global optimization problems

Coyote Optimization Algorithm: A new metaheuristic for globa...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pierezan, Juliano Coelho, Leandro dos Santos Fed Univ Parana UFPR Dept Elect Engn Lactec Inst LACTEC Dept Mech Curitiba Parana Brazil Fed Univ Parana UFPR Dept Elect Engn Curitiba Parana Brazil Pontifical Catholic Univ Parana PUCPR Polytech Sch Curitiba Parana Brazil

ISBN: (纸本)9781509060177

The behavior of natural phenomena has become one of the most popular sources for researchers to design optimization algorithms for scientific, computing and engineering fields. As a result, a lot of nature-inspired algorithms have been proposed in the last decades. Due to the numerous issues of the global optimization process, new algorithms are always welcome in this research field. This paper introduces the coyote optimization algorithm (COA), which is a population based metaheuristic for optimization inspired on the canis latrans species. It contributes with a new algorithmic structure and mechanisms for balancing exploration and exploitation. A set of boundary constrained real parameter optimization benchmarks is tested and a comparative study with other nature-inspired metaheuristics is provided to investigate the performance of the COA. Numerical results and non-parametric statistical significance tests indicate that the COA is capable of locating promising solutions and it outperforms other metaheuristics on most tested functions.

关键词： Global optimization Bio-inspired metaheuristics coyote optimization algorithm

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Economic Dispatch Integrated Wind Power Using coyote optimization algorithm 7

Economic Dispatch Integrated Wind Power Using Coyote Optimiz...

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7th International Istanbul Smart Grids and Cities Congress and Fair (ICSG)

作者： Guvenc, Ugur Kaymaz, Enes Duzce Univ Elect & Elect Engn Duzce Turkey

ISBN: (纸本)9781728113159

Fossil fuels used in power system cause air pollution and global warming because of releasing greenhouse gases. Nowadays, renewable energy especially wind power has more widespread in power generation due to ecological concerns and increasing fuel prices. Therefore, it is presented the Economic Dispatch integrated wind power approach in this paper. However, wind power is stochastic because wind speed is uncertain in nature. Therefore Weibull Probability Density Function ( PDF) and Incomplete Gamma Function (IGF) are used to estimating and modelling wind power. To solve the problem effectively, coyote optimization algorithm (COA) is implemented to the problem and it was tested on various power system consisting thermal generator and wind power generator. Simulation results generated by COA are compared with other heuristic algorithm such as GA and PSO. It can be clearly seen that COA produces better results than GA and PSO.

关键词： economic dispatch coyote optimization algorithm wind power renewable energy

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Cultural coyote optimization algorithm applied to a heavy duty gas turbine operation

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ENERGY CONVERSION AND MANAGEMENT 2019年 199卷 111932-000页

作者： Pierezan, Juliano Maidl, Gabriel Yamao, Eduardo Massashi Coelho, Leandro dos Santos Mariani, Viviana Cocco LACTEC Mech Syst Div Prefeito Lothario Meissner Ave 1 BR-80210170 Curitiba PR Brazil Univ Fed Parana Dept Elect Engn Cel Francisco H dos Santos Ave 100 Curitiba PR Brazil Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Imaculada Conceicao BR-1155 Curitiba PR Brazil Pontifical Catholic Univ Parana PUCPR Dept Mech Engn Imaculada Conceicao BR-1155 Curitiba PR Brazil

In the past decades, the quantity of researches regarding industrial gas turbines (GT) has increased exponentially in terms of number of publications and diversity of applications. The GTs offer high power output along with a high combined cycle efficiency and high fuel flexibility. As consequence, the energy efficiency, the pressure oscillations, the pollutant emissions and the fault diagnosis have become some of the recent concerns related to this type of equipment. In order to solve these GTs related problems and many other real-world engineering and industry 4.0 issues, a set of new technological approaches have been tested, such as the combination of Artificial Neural Networks (ANN) and metaheuristics for global optimization. In this paper, the recently proposed metaheuristic denoted coyote optimization algorithm (COA) is applied to the operation optimization of a heavy duty gas turbine placed in Brazil and used in power generation. The global goal is to find the best valves setup to reduce the fuel consumption while coping with environmental and physical constraints from its operation. In order to treat it as an optimization problem, an integrated simulation model is implemented from original data driven models and others previously proposed in literature. Moreover, a new version of the COA that links some concepts from Cultural algorithms (CA) is proposed, which is validated under a set of benchmarks functions from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2017 and tested to the GT problem. The results show that the proposed Cultural coyote optimization algorithm (CCOA) outperforms its counterpart for benchmark functions. Further, non-parametric statistical significance tests prove that the CCOA's performance is competitive when compared to other state-of-the-art metaheuristics after a set of experiments for five case studies. In addition, the convergence analysis shows that the cultural mechanism

关键词： Gas turbines Power generation Constrained global optimization coyote optimization algorithm Cultural algorithm

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A novel hybrid coyote-Particle Swarm optimization algorithm for three-dimensional constrained trajectory planning of Unmanned Aerial Vehicle

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Gupta, Himanshu Verma, Om Prakash Dr BR Ambedkar Natl Inst Technol Dept Instrumentat & Control Engn Jalandhar India

Unmanned Aerial Vehicles (UAVs) have been considered the future of transportation systems. However, mostly the reported optimizers struggle to estimate the flyable trajectories within acceptable accuracy and time bound under various constraints, particularly in a complex 3D environment. Therefore, this work proposes a novel hybrid optimizer (HCPSOA) by combining the Particle Swarm optimization (PSO) and coyote optimization algorithm (COA). Further, the chaotic logistic map and dynamic weight adjustments have been incorporated to enhance the exploration-exploitation capabilities. After validating the promising performance of HCPSOA against popular metaheuristics (COA, PSO, Improved COA, Glowworm Swarm optimization, and Hybrid Fireworks PSO) for several benchmark functions, the proposed HCPSOA has been employed to estimate the flyable path by formulating a novel cost function and smoothened by cubic B-spline curve. The simulated results reveal that the HCPSOA provide a non -colliding path with up to 10.00% lesser average cost for the considered real world scenario (map 3), which confirms its supremacy for the estimation of the safe and flyable relative to other compared algorithms.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： coyote optimization algorithm Hybrid optimization Particle Swarm optimization Path planning Unmanned Aerial Vehicle

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