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Optimal Integration of Renewable Energy-Based Distributed Generation Units in Radial Distribution System

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2025年第1期2025卷

作者： Kien, Le Chi Hien, Truong Van Tram, Hoang Do Ngoc Pham, Thai Dinh Ho Chi Minh City Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Educ Dept Phys 280 Duong Vuong StWard 4Dist 5 Ho Chi Minh City Vietnam

Determining the optimal integration of renewable energy-based distributed generation units (DGUs) in electric distribution systems brings many positive technical and economic impacts and contributes significantly for improving the performance of the distribution system. The suitable installation of DGUs is always a challenging problem because the output behaviors of DGUs, specifically photovoltaic units (PVUs) and wind turbine units (WTUs) are strongly affected by stochastic natural conditions. In this study, the main purpose is to address optimal nonlinear constrained problems with three targets in the multiobjective function (MOF) for minimizing (1) total power loss, (2) voltage deviation, and (3) the cost of purchasing energy from the main grid considering the uncertainties of solar irradiance and wind speed in the actual region in Binh Thuan Province, Vietnam. This paper solves three problems related to optimal connection of DGUs in the distribution system considering constant power generation and consumption with unity power factor (PF) (Problem 1), constant power generation and consumption with optimal PF (Problem 2), and time-varying power generation and consumption with optimal PF (Problem 3). Besides, this research also proposes a novel meta-heuristic optimization algorithm called revised coyote optimization algorithm (RCOA) for addressing the optimization problems of the simultaneous integration of DGUs in IEEE 69-bus radial distribution system. The obtained results in above three problems are compared with the original and published methods to demonstrate the superior economic and technical benefits of the proposed method.

关键词： coyote optimization algorithm renewable energies total power loss uncertainties voltage profile

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UBIQUITOUS LEARNING-DRIVEN NATURAL LANGUAGE PROCESSING FOR TWITTER CLIMATE CHANGE SENTIMENT ANALYSIS USING BAYESIAN MACHINE LEARNING FOR FOSTER SUSTAINABLE DEVELOPMENT IN RURAL AREAS

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2025年第2期33卷

作者： Eltahir, Majdy M. Bin Subait, Wala Alahmari, Saad Alharbi, Abeer A. K. Salama, Ahmed S. Alneil, Amani A. King Khalid Univ Appl Coll Mahayil Dept Informat Syst Abha Saudi Arabia Princess Nourah bint Abdulrahman Univ Arab Language Teaching Inst Dept Language Preparat POB 84428 Riyadh 11671 Saudi Arabia Northern Border Univ Appl Coll Dept Comp Sci Ar Ar Saudi Arabia Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Dept Informat Syst Riyadh 11432 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt Prince Sattam bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj Saudi Arabia

The size of data shared has promptly surged with the usage of social networks, and this has become a crucial research area for environmental concerns. Sentiment analysis (SA) defines the behavior and people's sensitivity to environmental problems. Ubiquitous learning and natural language processing (NLP) intersect to address climate change SA by analyzing vast textual data from scientific reports, social media, and news to gauge public attitudes and perceptions. This provides meaningful insight for organizations and policymakers working on climate change mitigation systems in rural areas and allows local communities to engage in environmental discourse actively, fostering a bottom-up algorithm to address climate problems. This study develops a new twitter climate change sentiment analysis using the Bayesian machine learning (TCCSA-BML) technique to promote sustainable development in rural areas. This technique exploits ubiquitous learning with NLP technologies to identify climate change in rural areas. Also, the TCCSA-BML technique undergoes data preprocessing in several ways to make the input data compatible with processing. Besides, the TCCSA-BML technique utilizes the TF-IDF model for the word embedding process. Moreover, the classification of various kinds of sentiments occurs using the Bayesian model averaging (BMA) technique comprising three classifiers, namely attention long short-term memory (ALSTM), extreme learning machine (ELM), and gated recurrent unit (GRU). Finally, the parameter tuning of the classifier is implemented by the coyote optimization algorithm (COA) model. The performance analysis of the TCCSA-BML approach is evaluated on the Kaggle SA dataset. The experimental validation of the TCCSA-BML approach portrayed a superior accuracy value of 94.07% over other models.

关键词： Twitter NLP coyote optimization algorithm Climate Change Sentiment Analysis Bayesian Machine Learning

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Advanced frequency stabilization framework for multi-area renewable energy grids with EV aggregator support: A multi-stage control perspective

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2025年 46卷

作者： Shahzad, Muhammad Inshal Gulzar, Muhammad Majid Habib, Salman Shafiullah, Md Shahzad, Aqsa Khalid, Muhammad King Fahd Univ Petr & Minerals KFUPM Dept Control & Instrumentat Engn Dhahran 31261 Saudi Arabia KFUPM Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran 31261 Saudi Arabia KFUPM Interdisciplinary Res Ctr Smart Mobil & Logist IRC Dhahran 31261 Saudi Arabia COMSATS Elect Engn Dept Abbottabad 22020 Pakistan KFUPM Elect Engn Dept Dhahran 31261 Saudi Arabia

In the modern world, the surge of renewable energy has become a focal point, drawing global attention due to its ability to merge cost-effectiveness with sustainability. This shift has made renewable energy an inescapable component of our power grids. A fresh approach is being proposed for load frequency control (LFC) in multi-area power systems, integrating diverse energy sources like photovoltaic (PV), electric vehicles, wind turbines, and thermal plants. This study dives deep into the complex domain of control systems by examining the intercommunication between multi-stage controllers, specifically comparing the 2DOF proportional integral and derivative with filter-PI (2 Degrees of Freedom PIDn-PI) models against the classic PI and 2DOF-PIDn controllers. The key differentiator here lies in introducing an enhanced coyote optimization algorithm (ECOA), aimed at determining the optimal parameters for these advanced controllers. A unique facet of this research is its inclusion of uncertainty, addressing variability by allowing the system parameters to fluctuate within a range of +/- 40 %. The robustness of the suggested controllers is tested under dynamic load changes, with these variations applied independently across multiple regions. Two distinct test scenarios are employed, each subject to varying disturbances, to gauge the controllers' adaptability. The operational restrictions of the governor dead band (GDB) impact on the reheat thermal governor unit and generation rate constraint (GRC) in the reheat thermal generating units are simulated using the proper dynamic models. This research includes GDB after the governor unit and GRC after re-heat unit and studies the effect of nonlinearity in power system. CTD is added before the controller and because, in a realistic scenario, there is a time delay in communication with the system. So, the proposed controller helps to give the results as close as the real-time scenario. The findings reveal that by incorporating th

关键词： Load frequency control coyote optimization algorithm Renewable energy sources Multi-area power system

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Design and optimization of Controller-Based Approach for Magnetic-Field Driven Robotic Arm Joints and End-Effector

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JOURNAL OF FIELD ROBOTICS 2025年

作者： Kaur, Manpreet Sondhi, Swati Yanumula, Venkata Karteek Thapar Inst Engn & Technol Dept Elect & Instrumentat Engn Patiala India

Magnetic Levitation (Maglev) is a technique that involves suspending an object using a magnetic field. This study presents a novel approach for robotic arm joints and end effectors by utilizing the functioning prototype of the Maglev system due to their similar functionality. The proposed approach utilizes a fractional-order enhanced model reference adaptive controller (FOEMRAC) in conjunction with the coyote optimization algorithm (COA) to control the stability of levitating magnetic objects. The FOEMRAC system employs a modified MIT rule as its adaptation mechanism. The simulation is performed using the Quanser Maglev system, and a comparison is done with other state-of-the-art techniques such as linear quadratic regulator (LQR), particle swarm optimization-LQR (PSO-LQR), LQR + proportional integral (LQR + PI), LQR + proportional integral derivative (LQR + PID), proportional integral voltage + PI (PIV + PI), enhanced model reference adaptive controller (EMRAC), FOEMRAC, and PSO-FOEMRAC, respectively. The robustness of the controllers is assessed using various integral error criteria, such as integral absolute error (IAE), integral square error (ISE), and integral time absolute error (ITAE), respectively. Additionally, rise time, settling time, overshoot, and undershoot have been employed for comparison purposes with load disturbance and parametric uncertainties. The results are also validated on real-time hardware, demonstrating the superior performance of COA-FOEMRAC as compared to various controllers. Thus, it can be effectively employed to improve the functionality of the magnetic joints and magnetic end effectors in real-time applications. A video demonstrating the functioning of the Maglev system is available at this link: .

关键词： adaptive controller coyote optimization algorithm fractional order magnetic end effectors magnetic levitation robotic arm

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coyote optimization Based on a Fuzzy Logic algorithm for Energy-Efficiency in Wireless Sensor Networks

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IEEE ACCESS 2020年 8卷 185816-185829页

作者： Mohamed, Asmaa Saber, Walaa Elnahry, Ibrahim Hassanien, Aboul Ella Port Said Univ Dept Elect Engn Fac Engn Port Said 42511 Egypt Cairo Univ Fac Comp & Informat Cairo 12613 Egypt

Internet of Things (IoT) is an important technique in the modern wireless telecommunications field. It is based on a collection of sensor nodes connected through wireless sensor networks (WSNs). The lifetime of this network is affected by the battery power of the connected sensor nodes. Network clustering techniques are used to improve energy consumption and extend the lifetime of the WSN. These techniques divide the sensor nodes into clusters and every cluster has a unique cluster head (CH) node. Recently, clustering-based metaheuristic techniques are used to solve this problem and find the optimal CH nodes under certain considerations such as less energy consumption and high reliability. This paper proposes a new clustering scheme for heterogeneous WSN using coyote optimization based on a Fuzzy Logic (COFL) algorithm. It uses the coyote optimization algorithm (COA) in conjunction with fuzzy logic (FL) system to reinforce and balance the clustering process for increasing the wireless network lifetime and reducing energy consumption. FL based clustering is adapted to determine a tentative set of CHs. The output of the FL is added as a solution within the initial solutions of the COA. Furthermore, a new fitness function has been adapted to minimize the total intra-cluster distance between each CH node and its cluster members and minimize the inter-cluster distance between the CHs nodes and the base station. An extensive simulation with three different scenarios is performed. The performance of the proposed COFL algorithm is compared with the well-known algorithms;namely low-energy adaptive clustering hierarchy protocol (LEACH) and stable election protocol (SEP) as traditional protocols and also coyote optimization algorithm (COA), grey wolf optimization (GWO), and particle swarm optimization (PSO). The COFL algorithm outperforms other algorithms in terms of alive node analysis, energy consumption, throughput, and central tendency measurements for alive nodes and norm

关键词： Wireless sensor networks Fuzzy logic Clustering algorithms Protocols Energy consumption optimization Wireless communication IoT clustering coyote optimization algorithm energy fuzzy logic coyote optimization algorithm network lifetime

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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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Chaotic coyote algorithm applied to truss optimization problems

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COMPUTERS & STRUCTURES 2021年 242卷 106353-106353页

作者： Pierezan, Juliano Coelho, Leandro dos Santos Mariani, Viviana Cocco de Vasconcelos Segundo, Emerson Hochsteiner Prayogo, Doddy Fed Univ Parana UFPR Dept Elect Engn Curitiba Parana Brazil Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Curitiba Parana Brazil Pontifical Catholic Univ Parana PUCPR Mech Engn Grad Program PPGEM Curitiba Parana Brazil Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei Taiwan Petra Christian Univ Dept Civil Engn Surabaya Indonesia

The optimization of truss structures is a complex computing problem with many local minima, while metaheuristics are naturally suited to deal with multimodal problems without the need of gradient information. The coyote optimization algorithm (COA) is a population-based nature-inspired metaheuristic of the swarm intelligence field for global optimization that considers the social relations of the coyote proposed to single-objective optimization. Unlike most widespread algorithms, its population is subdivided in packs and the internal social influences are designed. The COA requires a few control hyperparameters including the number of packs, the population size, and the number maximum of generations. In this paper, a modified COA (MCOA) approach based on chaotic sequences generated by Tinkerbell map to scatter and association probabilities tuning and an adaptive procedure of updating parameters related to social condition is proposed. It is then validated by four benchmark problems of structures optimization including planar 52-bar truss, spatial 72-bar truss, 120-bar dome truss and planar 200 bar-truss with discrete design variables and focus in minimization of the structure weight under the required constraints. Simulation results collected in the mentioned problems demonstrate that the proposed MCOA presented competitive solutions when compared with other state-of-the-art metaheuristic algorithms in terms of results quality. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Structural optimization Discrete truss structures coyote optimization algorithm Metaheuristic algorithms Chaotic sequences

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Enhanced coyote optimization with Deep Learning Based Cloud-Intrusion Detection System

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Computers, Materials & Continua 2023年第2期74卷 4319-4336页

作者： Abdullah M.Basahel Mohammad Yamin Sulafah M.Basahel E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia E-commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its *** the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to *** Detection System(IDS)refers to one of the commonly utilized system for detecting attacks on *** proves to be an effective and promising technique,that identifies malicious activities and known threats by observing traffic data in computers,and warnings are given when such threatswere *** current mainstream IDS are assisted with machine learning(ML)but have issues of low detection rates and demanded wide feature *** article devises an Enhanced coyote optimization with Deep Learning based Intrusion Detection System for Cloud Security(ECODL-IDSCS)*** ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic(ADASYN)*** detecting and classification of intrusions,long short term memory(LSTM)model is *** addition,ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud *** the presented ECODL-IDSCS model is tested on benchmark dataset,the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.

关键词： Intrusion detection system cloud security coyote optimization algorithm class imbalance data deep learning

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A hybrid deep kernel incremental extreme learning machine based on improved coyote and beetle swarm optimization methods

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COMPLEX & INTELLIGENT SYSTEMS 2021年第6期7卷 3015-3032页

作者： Wu, Di Li, Ting Wan, Qin Hunan Inst Engn Coll Elect & Informat Engn Xiangtan 411104 Peoples R China Hunan Inst Engn Hunan Prov Cooperat Innovat Ctr Wind Power Equipm Xiangtan 411104 Peoples R China

The iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the improved coyote and beetle swarm optimization methods was proposed in this paper. A hybrid intelligent optimization algorithm based on the improved coyote optimization algorithm (ICOA) and improved beetle swarm optimization algorithm (IBSOA) was proposed to optimize the parameters and determine the number of effectively hidden layer neurons for the proposed DKIELM. A Gaussian global best-growing operator was adopted to replace the original growing operator in the intelligent optimization algorithm to improve COA searching efficiency and convergence. In the meantime, IBSOA was designed based on tent mapping inverse learning and dynamic mutation strategies to avoid falling into a local optimum. The experimental results demonstrated the feasibility and effectiveness of the proposed DKIELM with encouraging performances compared with other ELMs.

关键词： Kernel incremental extreme learning machine coyote optimization algorithm Beetle swarm optimization algorithm Hybrid intelligence

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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