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Optimal sizing of solar-wind based hybrid energy system using modified dragonfly algorithm for an institution

ENERGY CONVERSION AND MANAGEMENT 2023年第1期283卷

作者： George, D. X. Tittu Raj, R. Edwin Rajkumar, Ananth Mabel, M. Carolin St Xaviers Catholic Coll Engn Dept Mech Engn Nagercoil 629003 Tamil Nadu India Gati Shakti Vishwavidyalaya Dept Mech Engn Vadodara 390004 Gujarat India

Globally, most solar and wind power systems can produce power cheaper than fossil fuel-powered systems under certain circumstances according to the international renewable energy agency (IRENA). Despite that, the penetration rate of renewable energy has slowed down recently due to its non-availability and reliability in providing an uninterruptible power supply. A hybrid renewable energy system (HRES) with the conventional grid to counter the intermittency challenges without battery storage is a way forward for a greener solution. In this paper, unutilized rooftop space and underutilized potential locations of an educational institution are customized for renewable Solar-Wind power generation together with the conventional grid for effective and cost-optimal power solutions. A modified dragonfly algorithm (MDA) was adopted for mathematical modeling to reduce the power systems' net present value (NPV) over its lifespan. The model reckons various scenarios and found that the HRES with grid interaction is the best case to significantly improve the reliability economically. The result is encouraging to promote HRES in potential institutions to boost the growth of renewable energy, economically to achieve the net zero emission goal of IRENA by 2050.

关键词： Energy optimization Hybrid renewable energy system Solar-wind energy Reliability dragonfly algorithm

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Optimization of Size and Cost of Static VAR Compensator using dragonfly algorithm for Voltage Profile Improvement in Power Transmission Systems

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2018年第1期8卷 56-66页

作者： Vanishree, J. Ramesh, V. VIT Univ Sch Elect Engn Vellore 14 Tamil Nadu India

Voltage stability is a major concern in power transmission systems due to mismatch between power generation and demand Hence maintenance of voltage profile within the acceptable limit becomes a challenging task. In this paper the weak buses to implement the reactive power compensators are identified by applying eigenvalue decomposition technique on partitioned Y-admittance matrix. The dragonfly algorithm is used to optimize the size and cost of the SVC with the overload factor, voltage deviation and VAR regulation as the design constraints. The dragonfly algorithm is implemented on IEEE 14 and 30 bus systems and the results obtained with and without the placement of Static VAR Compensators are compared with other algorithms to show its effectiveness. To emphasize the possibility to integrate the renewable energy and to demonstrate its effectiveness a 2 MW solar generation is installed at one of the weakest buses of IEEE 30 bus system and the improvement in voltage profile is shown. The further scope of this work is to implement the wind generators at the weakest buses so as to reduce the electrical distance between the generators and the farthest load buses and to secure the system from voltage collapse.

关键词： Voltage profile Static VAR Compensator dragonfly algorithm Eigenvalue decomposition Y-admittance matrix wind generators

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A hybrid dragonfly algorithm for the vehicle routing problem with stochastic demands

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2023年 18卷

作者： Marinaki, Magdalene Taxidou, Andromachi Marinakis, Yannis Tech Univ Crete Sch Prod Engn & Management Univ Campus Khania 73100 Greece

A number of swarm intelligence algorithms have been proposed during the last years. Most of them are suitable for the solution of continuous optimization problems. One of them is the dragonfly algorithm that has been proved very efficient in the problems in which it has been applied. However, few of the newly proposed algorithms have been used for the solution of a routing problem. In this paper, a new hybridized version of the dragonfly algorithm with the Combinatorial Expanding Neighborhood Topology is proposed and analyzed in details. The proposed Combinatorial Expanding Neighborhood Topology dragonfly algorithm is an algorithm that combines a very powerful swarm intelligence algorithm, the dragonfly algorithm, with a very effective procedure, the Combinatorial Expanding Neighborhood Topology. This algorithm was used for solving a well known routing problem, the Vehicle Routing Problem with Stochastic Demands. The algorithm was tested in 40 benchmark instances from the literature and gave, in some of them new, best solutions. It was, also, compared with 10 other swarm intelligence algorithms from the literature proving its effectiveness, as it was ranked in the first place among all the algorithms.

关键词： dragonfly algorithm Vehicle routing problem with stochastic demands Combinatorial expanding neighborhood topology Path relinking Variable neighborhood search

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Hybridized dragonfly and Jaya algorithm for optimal sensor node location identification in mobile wireless sensor networks

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JOURNAL OF SUPERCOMPUTING 2023年第15期79卷 16940-16962页

作者： Khedr, Ahmed M. Rani, S. Sheeja Saad, Mohamed Univ Sharjah Dept Comp Sci Sharjah 27272 U Arab Emirates Univ Sharjah Dept Comp Engn Sharjah 27272 U Arab Emirates Zagazig Univ Math Dept Zagazig Egypt

A wireless sensor network (WSN) consists of an extensive number of low-power sensor nodes to gather information from their environment and monitor physical activities. This makes node localization a crucial aspect in most WSN applications since measurement data is worthless unless the location from where the data is acquired is known precisely. The majority of localization solutions rely on anchor nodes for estimating the node locations with different localization accuracy, complexity, and hence different applicability. But, the cost and complexity in the localization of large-scale WSNs are not significantly reduced. In this paper, a novel Hybridized dragonfly and Jaya Optimization technique (HyDAJ) is introduced for improving localization accuracy and performance of mobile WSNs. The proposed hybrid technique combines the advantages of dragonfly algorithm and Jaya algorithm to localize the sensor nodes in a more efficient way and overcomes the limitations of the original algorithm. The hybrid algorithm verifies that all target nodes are precisely localized with higher accuracy. Simulation results reveal that HyDAJ outperforms existing methods under multiple metrics including localization efficiency, mean localization error, computation time, and energy consumption.

关键词： Mobile WSN Sensor node location identification Time of flight (ToF) Received signal strength indicator (RSSI) dragonfly algorithm Jaya optimization

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A novel hybrid dragonfly optimization algorithm for agricultural drought prediction

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2021年第12期35卷 2459-2477页

作者： Aghelpour, Pouya Mohammadi, Babak Mehdizadeh, Saeid Bahrami-Pichaghchi, Hadigheh Duan, Zheng Bu Ali Sina Univ Dept Water Engn Fac Agr Hamadan Hamadan Iran Lund Univ Dept Phys Geog & Ecosyst Sci Solvegatan 12 SE-22362 Lund Sweden Urmia Univ Water Engn Dept Orumiyeh Iran Sari Agr Sci & Nat Resources Univ Dept Water Engn Fac Agr Engn Sari Iran

Palmer Drought Severity Index (PDSI) is known as a robust agricultural drought index since it considers the water balance conditions in the soil. It has been widely used as a reference index for monitoring agricultural drought. In this study, the PDSI time series were calculated for nine synoptic stations to monitor agricultural drought in semi-arid region located at Zagros mountains of Iran. Autoregressive Moving Average (ARMA) was used as the stochastic model while Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) were applied as Machine Learning (ML)-based techniques. According to the time series analysis of PDSI, for the driest months the most PDSI drought events are normal drought and mild drought conditions. As an innovation, dragonfly algorithm (DA) was used in this study to optimize the SVM's parameters, called as the hybrid SVM-DA model. It is worthy to mention that the hybrid SVM-DA is developed as a meta-innovative model for the first time in hydrological studies. The novel hybrid SVM-DA paradigm could improve the SVM's accuracy up to 29% in predicting PDSI and therefore was found as the superior model. The best statistics for this model were obtained as Root Mean Squared Error (RMSE) = 0.817, Normalized RMSE (NRMSE) = 0.097, Wilmott Index (WI) = 0.940, and R = 0.889. The Mean Absolute Error values of the PDSI predictions via the novel SVM-DA model were under 0.6 for incipient drought, under 0.7 for mild and moderate droughts. In general, the error values in severe and extreme droughts were more than the other classes;however, the hybrid SVM-DA was the best-performing model in most of the cases.

关键词： Palmer drought severity index Drought prediction dragonfly algorithm Stochastic model Iran

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A New Meta Heuristic dragonfly Optimizaion algorithm for Optimal Reactive Power Dispatch Problem

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2018年第4期31卷 1107-1121页

作者： Palappan, Anbarasan Thangavelu, Jayabarathi VIT Univ Sch Elect Engn Vellore Tamil Nadu India

This research accesses a novel approach of utilising an advanced Meta-heuristic Optimization technique with a single objective to pledge with optimal reactive power dispatch problem in electrical power system network. The prime focus of reactive power dispatch is to curtail the total active power loss in transmission lines. In this detailed study, the dragonfly algorithm was realized on standard IEEE-14 bus and 30 bus systems. The outcome of dragonfly algorithm lucidly indicate the capablity of increasing the antecedent random population size for a liable global optimization problem, focalized close to the global optimum and contributing precise outcome results related to another popular algorithm.

关键词： Optimal reactive power dispatch dragonfly algorithm Real power loss minimization and Swarm intelligence

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Optimization of Leaky-ESN parameters based on dragonfly and whale algorithm

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PHYSICA SCRIPTA 2024年第1期99卷 016002-016002页

作者： Gong, Yuanpeng Lun, Shuxian Li, Ming Lu, Xiaodong Bohai Univ Coll Math Sci Jinzhou 121013 Peoples R China Bohai Univ Coll Control Sci & Engn Jinzhou 121013 Peoples R China Suqian Univ Suqian 223800 Peoples R China

In this research, we present a spiral flying mode-based dragonfly algorithm (DA-WOA). Since the traditional dragonfly algorithm (DA) has a Levy flight, the final results obtained are random in nature and have poor global search capability. Changing to a spiral flight pattern not only enlarges the search area but also makes it easy to find the optimal solution. The Swish function is also used to optimize the various flight coefficients of the DA to avoid the gradient descent approaching 0. Adding a fitness coefficient to the final position updating equation can better control the distance between dragonflies and natural enemies and thus find the optimal solution. The convergence speed of the DA-WOA algorithm and the superiority of the global search are verified by 13 test functions. In this paper, DA-WOA is applied to Leaky-ESN to optimize three parameters of the Leaky-ESN system, namely, the leakage rate (alpha), the spectral radius of the connectivity weight matrix (rho), and the input scale factor (s in ). The experimental results demonstrate that DA-WOA has very strong prediction ability as well as very quick convergence speed for the predicted Sin and MG time series.

关键词： time series prediction meta-heuristic algorithm dragonfly algorithm whale optimization algorithm

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The importance of reconfiguration of the distribution network to achieve minimization of energy losses using the dragonfly algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 5卷

作者： Rahmati, Katayun Taherinasab, Sina Department of Electrical and Computer Engineering Islamic Azad University Gorgan Iran Department of Electrical and Computer Engineering Institute of Higher Education Fakhruddin As'ad Gorgani Gorgan Iran

In this article, a method for reconfiguring the distribution network with the presence of production sources to minimize energy losses is presented. Also, besides the reconfiguration issue, the Super distribution tap transformers and the power factor of resources in the network will have their optimal values based on the amount of energy consumption as factors affecting the number of losses. According to the stated parameters, the optimization problem in this article is a non-linear problem with a large search space, for which the dragonfly optimization algorithm was used. To make the results realistic, all types of loads, including commercial, residential and office, have been considered in 24 h and two seasons of the year, cold and hot. The program was prepared with the help of MATLAB software on a sample network of 16 buses, and its applications and results have been evaluated. The obtained results show that determining the optimal reconfiguration is very effective in reducing network losses because, with this method, the amount of losses and unsupplied energy of network subscribers has been reduced significantly. Also, by coordinating the reconfiguration with tap transformers and the power factor of distributed generation resources, the loss reduction rate can be made more effective. The results obtained are compared with the genetic algorithm and the results show the superiority of the dragonfly optimization method. © 2023 The Author(s)

关键词： Distribution network reconfiguration dragonfly algorithm Genetic algorithm Network loss reduction Power transformer

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Mathematical modeling and dragonfly algorithm for optimizing sustainable agritourism supply chains

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Zhang, Gongwang Chen, Chun-Nan Shokouhifar, Mohammad Goli, Alireza Hangzhou Administrative College Zhejiang Hangzhou 310024 China College of Management Chang Jung Christian University Tainan 711301 Taiwan Department of Electrical and Computer Engineering Shahid Beheshti University Tehran *** Iran Department of Industrial Engineering and Future Studies Faculty of Engineering University of Isfahan Iran

The management of agritourism supply chains plays a pivotal role in promoting sustainable rural development and cultivating economic growth within agricultural communities. The study devises a comprehensive methodology aimed at designing and optimizing agritourism supply chain networks. To achieve this purpose, a two-objective mixed-integer linear programming mathematical model is introduced, tailored to accommodate diverse water consumption priorities across different gardens within the chain. Recognizing the NP-hardness of this intricate problem, a metaheuristic-driven solution approach based on MIN-MAX goal programming and dragonfly algorithm is presented to solve the designed model, aiming to simultaneously maximize the total profit and minimize overall water consumption within the supply chain. To assess the effectiveness of the proposed methodology, we adopted a real-world case study of an agritourism supply chain in China. The obtained results indicate the adoption of the proposed method can efficiently lead to the development and mechanization of agriculture in villages. The numerical analysis highlights that recruitment and production costs significantly influence the total profit of the agritourism supply chain. Additionally, the research underscores the pivotal role of sustainable rural development in creating job opportunities within the agritourism supply chain, thereby stimulating economic growth and fostering more profitable enterprises. © 2024 The Authors

关键词： dragonfly algorithm Mathematical modeling Optimization Supply chain network design Sustainability Water consumption

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UNIQUE dragonfly OPTIMIZATION algorithm FOR HARVESTING AND CLUSTERING THE KEY FEATURES 18

UNIQUE DRAGONFLY OPTIMIZATION ALGORITHM FOR HARVESTING AND C...

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18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Devarakonda, Nagaraju Anandarao, Sarvani Kamarajugadda, Raviteja Wang, Yingxu LBR Coll Engn Mylavaram Andhra Pradesh India Univ Calgary Calgary AB Canada

ISBN: (纸本)9781728114194

In many applications, the feature selection plays an important role, as best feature can bring out the accurate results. The features selected must represent the entire dataset. Here we have considered "Sequential Forward Selection" for feature extraction and used refined dragonfly algorithm to approach and to migrate from the best and worst features respectively. We improvised the conventional dragonfly algorithm by adding the convergence and fitness functions. To access the accuracy of the algorithm we introduced the fitness function. This paper has discussed about the general hunting behaviour of the dragonfly and dragonfly algorithm (DA) with convergence and fitness functions. A comparative study was shown for the best search agent position between modified DA and traditional DA, at the same time test function values of refined dragonfly algorithm (RDA) is compared with whale optimization algorithm (WOA) and Tornadogenesis Optimization algorithm (TOA).We have evaluated refined DA on the 23 benchmark function corresponding values are shown in experiment.

关键词： dragonfly algorithm sequential forward selection convergence Ilinction benchmark functions Tornadogenesis Optimization algorithm whale optimization algorithm

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