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Enhanced frequency control of a hybrid microgrid integrated with EV aggregator using [FOI-(PDN+1)] controller optimized by osprey optimization algorithm

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ENGINEERING RESEARCH EXPRESS 2025年第2期7卷 025303-025303页

作者： Santra, Swapan Mondal, Sourav De, Mala Natl Inst Technol NIT Elect Engn Dept Patna India Cent Inst Technol Elect Engn Dept Kokrajhar India

Progressively increasing power demand and increasing penetration of renewable energy resources (RESs) have led to decreased system inertia that in turn increased frequency fluctuation in power system and resulted into increased focus on load frequency control (LFC). Different controllers are proposed in recent time to improve the LFC of microgrid. This paper proposes a novel fractional order integral minus proportional derivative with filter plus one [FOI-(PDN+1)] controller to minimize frequency deviation of the microgrid containing hydro-thermal power plant with photovoltaic and wind power generation. This controller provides more flexibility due to having fractional order control and also provides improved dynamics. To compensate the frequency deviation caused by load disturbance and RES variation, fast acting energy storage system capacitive energy storage (CES) and electric vehicle aggregators are employed. Multiple nonlinearities such as generation rate constraints, governor dead band, communication time delay, boiler dynamic are also taken into consideration. The proposed controller parameters are tuned by a bio-inspired metaheuristic algorithm namely osprey optimization algorithm. Sensitivity of the proposed controller is inspected at different scenarios considering load perturbations, wide variation of system parameters, as well as different weather conditions. Robustness of the proposed controller is validated on Typhoon-HIL based real time emulator and the resilience of the proposed controller is investigated for LFC during denial-of-service attack. The proposed controller is effective in controlling the frequency deviation under distinct load change by stabilizing it 2.5 to 4 time faster compared to conventional, CES unit and 1.5 to 2.5 time faster compared to 2DOF-FOPID, 3DOF-FOPID and FOPTID+1. The result establishes the superiority of the studied approach.

关键词： capacitive energy storage (CES) DoS attack EV aggregator load frequency control [FOI-(PDN+1)] controller osprey optimization algorithm

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Integration of Solar Photovoltaic with Modular Multiport Converter Using a Pi Controller Optimized Through Hybrid osprey optimization algorithm and Relational Bi-Level Aggregation Graph Network

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2025年第0期

作者： Balasani, Srinivasa Rao Krishnan, T. Santhana Prasad, P. Venkata Jaseem, Leeth Hassen Prasad Inst Technol Dept Elect Engn Jaunpur Uttar Pradesh India Rajalakshmi Engn Coll Autonomous Rajalakshmi Nagar Dept Elect & Elect Engn Thandalam Chennai Tamil Nadu India Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad Telangana India Islamic Univ Coll Tech Engn Najaf Iraq Islamic Univ Al Diwaniyah Coll Tech Engn Al Diwaniyah Iraq

The integration of solar photovoltaic (SPV) systems with modular multiport converters (MMPC) enables efficient energy conversion and distribution, enhancing the overall performance and reliability of renewable energy systems (RES). However, the complexity of the control algorithms and potential issues related to the dynamic response can pose challenges in achieving optimal performance and stability in varying operating conditions. This paper proposes a hybrid method for integrating SPV systems with MMPC to achieve efficient power management in modern renewable energy grids. The proposed hybrid method is the combined execution of the osprey optimization algorithm (OOA) and Relational Bi-level Aggregation Graph Convolutional Network (RBAGCN). Hence it is named as OOA-RBAGCN technique. The aim is to ensure optimal power transfer, minimize total harmonic distortion (THD), maintain voltage stability under dynamic operating conditions, and ultimately improve the overall energy efficiency, reliability, and performance of SPV-based RES within smart grid applications. The OOA is used to optimize the control parameter of the proportional-integral (PI) controller. The RBAGCN is used to predict these optimized parameters. By then, the proposed approach is used on the MATLAB platform and compared with other approaches such as Starling Murmuration optimization (SMO), Dung Beetle Optimizer (DBO), Improved Harris Hawks optimization (IHHO), Grey Wolf optimization (GWO), and Particle Swarm optimization (PSO). The proposed method achieves a high efficiency of 98.1%, and a reduced THD of 2.9% significantly surpassing all existing methods.

关键词： Photovoltaic modular multiport converter osprey optimization algorithm Relational Bi-level Aggregation Graph Convolutional Network total harmonic distortion

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Peak-to-Average Power Ratio Reduction of OFDM Systems Towards 6G Communications Using osprey optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2024年 1-22页

作者： Sharma, Sanjeev Karthikeyan, M. Manoj, G. Das, R. Mohan Shanmugam, C. Kumar, U. Arun New Horizon Coll Engn QA & IIE Qual Assurance & Innovat Incubat & Entrep Bengaluru 560103 Karnataka India SRM Inst Sci & Technol Dept Elect & Elect Engn Chennai 600089 Tamilnadu India Karunya Inst Technol & Sci Div Elect & Commun Engn Coimbatore 641114 Tamilnadu India New Horizon Coll Engn Dept EEE Bengaluru 560103 Karnataka India Jansons Inst Technol Dept ECE Coimbatore Tamilnadu India Karpagam Acad Higher Educ Fac Engn Dept Elect & Elect Engn Coimbatore 641021 Tamil Nadu India

The development of 6G communication networks requires increased transmission speeds, extensive data processing capabilities, and minimal communication delays. Orthogonal frequency division multiplexing (OFDM) systems has higher peak-to-average power ratio (PAPR) adopting 5G applications. Exploring all possible sequences of phase weighting factors is necessary since the PTS technique significantly decreases the PAPR. Since PTS strategy reduces the PAPR greatly, it necessitates the exploration of all conceivable sequences of phase weighting variables. As a result, the computerized cost expands proportionally as several number of separated sub obstruct grows. In this manuscript, Peak-to-Average Power Ratio Reduction of Orthogonal Frequency Division Multiplexing Signals utilizing osprey optimization algorithm (PAPRR-OFDMS-OOA) is proposed for reduction of PAPR and computerized cost of OFDM systems. Orthogonal Frequency Division Multiplexing (OFDM) is used in 5G communication to achieve increased data transfer rates. The 6th generation of communication requires OFDM due to its higher spectral efficiency and suitability for massive data transfer. To lower the high computational costs and PAPR of OFDM systems, the osprey optimization algorithm (OOA) is used with reduce OFDM systems high PAPR and high computational costs. The proposed system is implemented in MATLAB and the performance of proposed technique is analyzed with that of other existing techniques. The proposed method attains 19.69%, 23.54% and 17.28% low Computational complexity, 17.69%, 21.24% and 12.18% low Cumulative Distribution Function of Optimal Partial Transmit System, 12.19%, 24.17% and 18.25% lower cost function values, and 16.22%, 22.29% and 13.21% lower BER execution of the proposed OFDM compared with the existing methods such as have presented an Ant colony optimization process for PAPR decreasing of OFDM signals (PAPRR-OFDMS-ACO), low-complexity PAPR decreasing technique based on TLBO process for OF

关键词： osprey optimization algorithm PAPR PTS 6G

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Attack-defense strategy assisted osprey optimization algorithm for PEMFC parameters identification

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RENEWABLE ENERGY 2024年 225卷

作者： Yuan, Yongliang Yang, Qingkang Ren, Jianji Mu, Xiaokai Wang, Zhenxi Shen, Qianlong Zhao, Wu Henan Polytech Univ Sch Mech & Power Engn Jiaozuo 454003 Peoples R China Henan Polytech Univ Sch Software Jiaozuo 454003 Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Jilin Univ Sch Commun Engn Changchun 130012 Peoples R China

The electrochemical parameter identification of Proton-exchange membrane fuel cells (PEMFCs) is a highly nonlinear optimization problem. In this study, a new optimization algorithm, named attack defense strategy assisted osprey optimization algorithm (ADSOOA), is proposed to identify PEMFC parameters. ADSOOA incorporates an attack defense strategy to improve convergence performance and prevent falling into local optima. The mean squares error (MSE) between the FC experimental and calculated output voltages is selected as the objective function. To assess the performance of ADSOOA, a comparative analysis is conducted against various state-of-the-art optimization algorithms. Results show that the ADSOOA algorithm performs better than existing algorithms in electrochemical parameter identification of PEMFCs. The optimization results for 250 W PEMFC, BCS-500 W PEMFCM, and NedStack PS6 PEMFC are 9.555e-3 (range 1), 2.773e-18 (range 2), 6.499e-4, and 7.260e-2, respectively.

关键词： PEMFC Parameter identification osprey optimization algorithm Attack defense strategy Fuel cell

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osprey optimization algorithm for MPPT of PV system with forward DC to DC converter under partial shading conditions

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2024年第4期6卷

作者： Rao, S. Nagaraja Kiran Kumar, B. M. Suresh, Y. Vijaya Kumar, A. Suresh Manjunatha, B. M. MS Ramaiah Univ Appl Sci Fac Engn & Technol Dept Elect Engn Bangalore India Rajeev Gandhi Mem Coll Engn & Technol Dept EEE Nandyal AP India

Electric power is required anywhere for multiple purposes to do various tasks. Production of electricity from renewable sources such solar energy is feasible solution for many problems. Photovoltaic modules are generally used to produce electricity through solar irradiance. However, a maximum power point tracking device (MPPTD) must be incorporated to Photovoltaic system to ensure its maximum utilization or best efficiency. A forward DC to DC circuit is selected to work as MPPTD in this paper. Many conventional algorithms including incremental conductance, perturb & observe are available to work effectively for MPPTD under uniform solar irradiances. However, non-uniform irradiances will be received by Photovoltaic modules where many are connected to form a power generation system due to trees, shading, dust, birds, clouds, etc Such phenomenon drags the system into Partial Shading Condition (PSC). Under PSC, conventional algorithms cannot identify the best location to harvest more energy. Hence, an optimization technique is required to operate Photovoltaic system at its outfit utilization during PSC. An optimization technique namely 'osprey optimization algorithm (OOA)' is developed in this paper on MPPTD to harvest more energy during PSC. The comparison among OOA, Grey Wolf optimization (GWO), Modified Invasive Weed optimization (MIWO) and Whale optimization algorithm (WOA) is also included in this paper. The Hardware-in the-Loop is establish by using OPAL-RT technology to collect various results for presenting analysis under different operating conditions.

关键词： photovoltaic osprey optimization algorithm maximum power point partial shading conditions hardware in the loop

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Parameters estimation and sensitivity analysis of lithium-ion battery model uncertainty based on osprey optimization algorithm

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ENERGY 2024年 304卷

作者： Alqahtani, Ayedh H. Fahmy, Hend M. Hasanien, Hany M. Tostado-Veliz, Marcos Alkuhayli, Abdulaziz Jurado, Francisco Publ Author Appl Educ & Training Coll Technol Studies Elect Engn Technol Dept Kuwait Kuwait Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Univ Jaen Dept Elect Engn Linares 23700 Spain King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the osprey optimization algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by +/- 5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions.

关键词： Battery model Electric vehicle energy storage systems Lithium -ion batteries osprey optimization algorithm

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SWARAM: osprey optimization algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

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SENSORS 2024年第2期24卷 521页

作者： Somula, Ramasubbareddy Cho, Yongyun Mohanta, Bhabendu Kumar Sunchon Natl Univ Dept Informat & Commun Engn Suncheon Si 57922 South Korea Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram 520002 Andhra Pradesh India

The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network's lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

关键词： Internet of Things wireless sensor network clustering protocol energy conservation osprey optimization algorithm

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Research on Reactive Power optimization Based on Hybrid osprey optimization algorithm

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ENERGIES 2023年第20期16卷 7101页

作者： Zhang, Yi Liu, Pengtao Jilin Jianzhu Univ Coll Elect & Comp Sci Changchun 130000 Peoples R China Jilin Jianzhu Univ Key Lab Comprehens Energy Saving Cold Reg Architec Minist Educ Changchun 130118 Peoples R China

This paper presents an improved osprey optimization algorithm (IOOA) to solve the problems of slow convergence and local optimality. First, the osprey population is initialized based on the Sobol sequence to increase the initial population's diversity. Second, the step factor, based on Weibull distribution, is introduced in the osprey position updating process to balance the explorative and developmental ability of the algorithm. Lastly, a disturbance based on the Firefly algorithm is introduced to adjust the position of the osprey to enhance its ability to jump out of the local optimal. By mixing three improvement strategies, the performance of the original algorithm has been comprehensively improved. We compared multiple algorithms on a suite of CEC2017 test functions and performed Wilcoxon statistical tests to verify the validity of the proposed IOOA method. The experimental results show that the proposed IOOA has a faster convergence speed, a more robust ability to jump out of the local optimal, and higher robustness. In addition, we also applied IOOA to the reactive power optimization problem of IEEE33 and IEEE69 node, and the active power network loss was reduced by 48.7% and 42.1%, after IOOA optimization, respectively, which verifies the feasibility and effectiveness of IOOA in solving practical problems.

关键词： osprey optimization algorithm Sobol sequence Weibull distribution firefly disturbance reactive power optimization

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Optimal sizing and allocation of multiple distribution generations for radial distribution networks enhancement using advanced metaheuristic-based osprey optimization algorithm

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2024年第4期6卷

作者： Adebayo, Isaiah Sun, Yanxia Ikeh, Emmanuel Salimon, Sunday Aborisade, David Adebiyi, Oluwaseyi Ladoke Akintola Univ Technol Dept Elect & Elect Engn PMB 4000 Ogbomosho Oyo Nigeria Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa Redeemers Univ Dept Elect & Elect Engn Ede Nigeria

Due to transmission over a long distance from the point of generation to the consumer residence, the distribution station is faced with various challenges which include but not limited to high-power losses, voltage instability and voltage deviation. Thus, it becomes imperative to mitigate these. In this paper, a metaheuristic optimization approach called osprey optimization algorithm (OOA) is proposed to evaluate the best sites and sizes of multiple Distributed Generations (DGs) on radial distribution networks. The suggested OOA is inspired based on the intelligent conduct of ospreys during hunting. The incorporation of multiple DGs at unity, 0.95, optimal power factor was evaluated, and various scenarios were examined. The effectiveness of OOA was scrutinized on the standard IEEE 33-bus distribution system and validated on a practical distribution network, Ayepe 11 KV 34- bus feeder of the Ibadan Electricity Distribution Company (IBEDC). The results obtained from the simulations depicts that three DG units operating at optimal power factor produced the best outcome when considering total active power loss (TAPL) and total reactive power loss (TRPL) on both networks. A 92.87% reduction on TAPL as well a 91.60% reduction on TRPL was attained on the 33-bus system while on the Ayepe 34-bus system both the TAPL and TRPL reduced by 98.51% when juxtaposing them with their respective base cases. The outcomes obtained corroborate the efficacy and superiority of OOA when comparing them to outcomes from recent optimization approach.

关键词： distributed generation distribution network optimal allocation optimal size osprey optimization algorithm power loss reduction

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A black-winged kite optimization algorithm enhanced by osprey optimization and vertical and horizontal crossover improvement

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SCIENTIFIC REPORTS 2025年第1期15卷 1-26页

作者： Li, Yancang Shi, Binli Qiao, Weitao Du, Zunfeng Hebei Univ Engn Sch Civil Engn Handan 056038 Hebei Peoples R China Hebei GEO Univ Shijiazhuang 050031 Hebei Peoples R China Jizhong Energy Fengfeng Grp Co LTD Handan 056201 Hebei Peoples R China Tianjin Univ Sch Civil Engn Tianjin 300354 Peoples R China

This paper addresses issues of inadequate accuracy and inconsistency between global search efficacy and local development capability in the black-winged kite algorithm for practical problem-solving by proposing a black-winged kite optimization algorithm that integrates the osprey optimization algorithm and Crossbar enhancement (DKCBKA). Firstly, the adaptive index factor and the fusion osprey optimization algorithm approach are incorporated to enhance the algorithm's convergence rate, and the probability distribution factor is updated throughout the attack stage. Second, the stochastic difference variant method is implemented to prevent the method from entering the local optima. Lastly, the longitudinal and transversal crossover technique is incorporated to enhance the algorithm's convergence accuracy and to dynamically alter the population's global and individual optimal solutions. Fifteen benchmark functions are chosen to test the effectiveness of the enhanced algorithm and to compare the optimization efficiency of each technique. Simulation experiments are performed on the CEC2017 and CEC2019 test sets, revealing that the DKCBKA algorithm surpasses five standard swarm intelligence optimization methods and six improved optimization algorithms regarding solution accuracy and convergence speed. The superiority in meeting real optimization challenges is further demonstrated by the optimization of three real engineering optimization problems by DKCBKA, with optimization capabilities 18.222%, 99.885% and 0.561% higher than BKA, respectively.

关键词： Black-Winged Kite algorithm Dynamic exponential factor osprey optimization algorithm Stochastic differential variance strategy Vertical and horizontal crossover strategy

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