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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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An efficient optimization State-based coyote optimization algorithm and its applications

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Zhang, Qingke Bu, Xianglong Zhan, Zhi-Hui Li, Junqing Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

coyote optimization algorithm (COA) has demonstrated efficient performance by utilizing the multiple pack (subpopulation) mechanism. However, the fixed number of packs and a relatively singular evolutionary strategy limit its comprehensive optimization performance. Thus, this paper proposes a COA variant, referred to as the optimization State-based coyote optimization algorithm (OSCOA). In the OSCOA algorithm, a Population optimization State Estimation Mechanism is employed for estimating the current population optimization state. Then, the estimation result is used to guide the algorithm in setting the number of packs appropriately as well as selecting appropriate evolutionary strategies to refine search directions, thereby avoiding blind exploration. Additionally, the estimation result assists each pack in selecting suitable parents to generate pups, further improving the global search efficiency of the algorithm. To validate the effectiveness of the proposed algorithm, the OSCOA algorithm is subjected to comprehensive testing and analysis along with seven efficient optimizers on 71 benchmark functions derived from the CEC2014, CEC2017, and CEC2022 benchmark suites. The results of these extensive experiments indicate the competitive performance of OSCOA. Furthermore, to further assess the capability of the OSCOA algorithm in addressing real-world problems, two practical applications is considered: wireless sensor network deployment and image segmentation. The outcomes of these applications further confirm the efficacy and stability of the OSCOA algorithm in tackling real-world scenarios.

关键词： Meta-heuristics algorithms coyote optimization algorithm Population state estimation Multi-thresholding image segmentation Deployment problems of wireless sensor networks

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Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第5期57卷 117-117页

作者： Zhu, Jun-Hua Wang, Jie-Sheng Zhang, Xing-Yue Wang, Yu-Cai Song, Hao-Ming Zheng, Yue Liu, Xun Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

A multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy (MOCOA-ML) was proposed to solve the optimal power flow (OPF) problem. MOCOA-ML adds external archives with grid mechanism on the basis of elite non-dominated sorting. It can guarantee the diversity of the population while obtaining the Pareto solution set. When selecting elite coyotes, there is a greater probability to select the elite in sparse areas, which is conducive to the development of sparse areas. In addition, combined with Meta-Lamarckian learning strategy, based on four crossover operators (horizontal crossover operator, longitudinal crossover operator, elite crossover operator and direct crossover operator), the local search method is adaptively selected for optimization, and its convergence performance is improved. First, the simulation is carried out in 20 test functions, and compared with MODA, MOPSO, MOJAYA, NSGA-II, MOEA/D, MOAOS and MOTEO. The experimental results showed that MOCOA-ML achieved the best inverted generational distance value and the best hypervolume value in 11 and 13 test functions, respectively. Then, MOCOA-ML is used to solve the optimal power flow problem. Taking the fuel cost, power loss and total emissions as objective functions, the tests of two-objective and three-objective bechmark problems are carried out on IEEE 30-bus system and IEEE 57-bus system. The results are compared with MOPSO, MOGWO and MSSA algorithms. The experimental results of OPF demonstrate that MOCOA-ML can find competitive solutions and ranks first in six cases. It also shows that the proposed method has obtained a satisfactory uniform Pareto front.

关键词： Optimal power flow coyote optimization algorithm Multi-objective optimization Non-dominated ranking Grid mechanism Meta-Lamarckian learning strategy

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State of Health Estimation of Lithium-Ion Battery Using Multi-Health Features Based on Savitzky-Golay Filter and Fitness-Distance Balance- and Lévy Roulette-Enhanced coyote optimization algorithm-Optimized Long Short-Term Memory

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PROCESSES 2024年第10期12卷 2284页

作者： Li, Hongbiao Gao, Dengke Shi, Linlong Zheng, Fei Yang, Bo Shanghai KeLiang Informat Technol Co Ltd Shanghai 201103 Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China

Accurate and reliable state of health (SOH) estimation is extremely crucial for the safe and stable operation of lithium-ion batteries (LIBs). In this paper, a method based on L & eacute;vy roulette- and fitness-distance balance-enhanced coyote optimization algorithm-optimized long short-term memory (LRFDBCOA-LSTM) is employed for SOH estimation of LIB. Firstly, six health features are extracted from battery charging and discharging data, and Savitzky-Golay is used to filter the feature data to improve correlation between feature and SOH. Then, L & eacute;vy roulette and fitness-distance balance (FDB) strategies are used to improve the coyote optimization algorithm (COA), i.e., LRFDBCOA. Meanwhile, the improved algorithm is used to optimize the internal parameters of long short-term memory (LSTM) neural network. Finally, the effectiveness of the proposed model is comprehensively validated using five evaluation indicators based on battery data obtained under three different testing conditions. The experimental results manifest that after algorithm improvement and network parameter optimization, the performance of the model is significantly improved. In addition, the method has high estimation accuracy, strong generalization, and strong robustness for SOH estimation with a maximum R2 of 0.9896 and minimum R2 of no less than 0.9711.

关键词： lithium-ion battery state of health Savitzky-Golay coyote optimization algorithm LSTM SimuNPS

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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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Design of Total Harmonic Distortion Reduction Using Quantum coyote optimization algorithm for Hybrid Power Generation Systems

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第17期32卷

作者： Varghese, Lijo Jacob Gandhi Raj, R. Ravi Sankar, R. S. Tan, Zhenhai Sri Krishna Coll Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Univ Coll Engn Dept Elect & Elect Engn BIT Campus Trichy Tamil Nadu India Vignans Inst Informat Technol Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India Nanchang Univ Sch Publ Policy & Management Nanchang Peoples R China

The usage of various inverters in various industries has gained considerable attention in the power electronics industry in recent years. The harmonic distortion that is induced by various renewable energy sources, along with the growing use of nonlinear loads and power electronic devices, has a significant impact on the distribution system. This is because of the rise in penetration of various renewable energy sources and the usage of nonlinear loads and power electronic devices. In addition, distributed generation (DG) units with inverters (such as solar (PV) and wind turbines) that are integrated into distribution networks are viewed as significant harmonic producers that have substantial adverse effects on power quality. The power quality in the system suffers because of these generators. This study proposes a method of harmonic mitigation for addressing power quality problems that are present in distribution systems as a solution to the challenges that have been found. These concerns have been brought to light as a result of previous research. The standard two-level and three-level inverters were the basis for the development of the multilevel inverter (MLI), which was meant to address their shortcomings. One of the effective technologies that can maintain constant performance is the hybrid power generating system. A hybrid energy system has several benefits, including high dependability, cheap cost and minimal emissions. When used in a hybrid power production system, the reduction of total harmonic distortion (THD) becomes absolutely necessary (HPGS). In this context, the research provides a novel approach for high-performance quantum computing called quantum coyote optimization algorithm-based THD reduction (QCOA-THDR). The QCOA-THDR approach that has been presented has been tested with several converters, including SEPIC, buck-boost and Cuk converters. Additionally, the proportional as well as the integral gain variables of the proportional integral (PI) con

关键词： Hybrid power generation PI controller DC to DC converter coyote optimization algorithm total harmonic distortion

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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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Implementation of coyote optimization algorithm for solving unit commitment problem in power systems

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ENERGY 2023年第PartA期263卷

作者： Ali, E. S. Abd Elazim, S. M. Balobaid, A. S. Jazan Univ Fac Engn Elect Engn Dept Jizan Saudi Arabia Jazan Univ Fac Comp Sci & Informat Technol Comp Sci Dept Jizan Saudi Arabia

The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time intervals in the study era. The scale of this problem grows speedily with great size of the electric power system and longer planning time. Fixing this large scale problem is a challenging process and computationally expensive. It is the most complicated optimization process in the operation and planning of the power system. Meta heuristics methods are capable to outlast the demerits of traditional deterministic methods in solving UC problem. One of the most recent meta heuristics methods is known as coyote optimization algorithm (COA). It is depended on the adaptation attitude of the coyote by the surroundings and the coyote's experiences exchanging. It has a motivating mechanisms to gain a balance between exploitation and exploration. Also, it is very easy in implementation as it has only two control variables. Moreover, its capability to keep larger diversity helps it to get the optimal cost so it is proposed to handle the UC problem in this paper. The election of the schedule and production size are performed by COA. Achievement of COA is examined for two IEEE systems. Outcomes establish that the elected algorithm is supreme to the recorded literature methods in terms of total cost, CPU, percentage reduction, and statistical analysis.

关键词： Generation scheduling Unit commitment coyote optimization algorithm

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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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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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