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Reconfigured single- and double-diode models for improved modelling of solar cells/modules

SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Celik, Emre Karayel, Mehmet Maden, Dincer Abdel-Salam, Mahmoud Ozturk, Nihat Kaplan, Orhan Tejani, Ghanshyam G. Sharma, Sunil Kumar Baljon, Mohammed Duzce Univ Engn Fac Dept Elect & Elect Engn Duzce Turkiye Cankiri Karatekin Univ Vocat Sch Dept Elect & Energy Cankiri Turkiye Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Gazi Univ Technol Fac Dept Elect & Elect Engn Ankara Turkiye Yuan Ze Univ Dept Ind Engn & Management Taoyuan 320315 Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Majmaah 11952 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Comp Engn Majmaah 11952 Saudi Arabia

Proper modeling of PV cells/modules through parameter identification based on the real current-voltage (I-V) data is important for the efficiency of PV systems. Most related works have concentrated on the classical single-diode model (SDM) and double-diode model (DDM) and their parameter extraction by various metaheuristic algorithms. In order to render more accurate and representative modeling, this paper adds a small resistance in series with the diodes in SDM and DDM. The new models are named reconfigured SDM (Reconfig-SDM) and reconfigured DDM (Reconfig-DDM), and they have not been studied so far as we know. A squirrel search algorithm (SSA) is employed to globally find the parameters of the new models. The performance achieved is experimentally tested on both a commercial RTC France solar cell and a CS6P-220P polycrystalline PV module located at D & uuml;zce University in T & uuml;rkiye. A vivid comparison of experimental findings, observation, and analysis clearly demonstrates that the proposed Reconfig-SDM and Reconfig-DDM tuned by the SSA have better capacity and effectiveness for modeling PV devices than some cutting-edge approaches. Specifically, compared with the best-performing approach in the literature, Reconfig-SDM and Reconfig-DDM could reduce the error rate up to 0.37% and 2.58% for the solar cell, and 3.21% and 29.0% for the solar module.

关键词： PV modeling I-V characteristic Reconfigured SDM Reconfigured DDM Parameter identification squirrel search algorithm

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squirrel search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

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Computers, Materials & Continua 2023年第3期74卷 6081-6099页

作者： K.Ishwarya A.Alice Nithya Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

Human pose estimation(HPE)is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision(CV)*** finds its applications in several fields namely activity recognition and human-computer *** the benefits of HPE,it is still a challenging process due to the variations in visual appearances,lighting,occlusions,dimensionality,*** resolve these issues,this paper presents a squirrel search optimization with a deep convolutional neural network for HPE(SSDCNN-HPE)*** major intention of the SSDCNN-HPE technique is to identify the human pose accurately and ***,the video frame conversion process is performed and pre-processing takes place via bilateral filtering-based noise removal ***,the EfficientNet model is applied to identify the body points of a person with no problem ***,the hyperparameter tuning of the EfficientNet model takes place by the use of the squirrel search algorithm(SSA).In the final stage,the multiclass support vector machine(M-SVM)technique was utilized for the identification and classification of human *** design of bilateral filtering followed by SSA based EfficientNetmodel for HPE depicts the novelty of the *** demonstrate the enhanced outcomes of the SSDCNN-HPE approach,a series of simulations are *** experimental results reported the betterment of the SSDCNN-HPE system over the recent existing techniques in terms of different measures.

关键词： Parameter tuning human pose estimation deep learning squirrel search algorithm activity recognition

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Energy Conscious squirrel search based Task Scheduling Mechanism in Fog Environment

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2024年第10期35卷

作者： Singh, Raj Mohan Sikka, Geeta Awasthi, Lalit Kumar Dr B R Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India Natl Inst Technol Dept Comp Sci & Engn Delhi India Natl Inst Technol Dept Comp Sci & Engn Pauri Uttarakhand India

The swift explosion of Internet of Things (IoT) devices in the past few years has resulted in a deluge of data that requires timely processing. However, transmitting such massive amounts of data in and out from cloud leads to high latency and network bandwidth. Consequently, the field of fog computing has turned out as a feasible platform for utilizing resources within the vicinity of IoT devices for providing services in a timely manner. The resource constrained trait of fog nodes demands judicious management of resources in satisfying the requirements of IoT applications. This makes energy efficiency a core issue in fog networks. This article addresses the issue of energy efficiency by proposing an energy conscious squirrel search algorithm (EcSSA) for scheduling tasks in fog environment. A salient feature of EcSSA lies in the novel objective function that aims to optimize the energy consumption by minimizing the cumulative active time of all nodes in the network. Simulation studies conducted as part of this work demonstrate that the proposed approach significantly improves average energy consumption, average makespan, and carbon dioxide emission rate in comparison to the existing algorithms. We propose an energy conscious task scheduling approach (EcSSA) for fog computing environments by making use of a recent nature inspired approach called squirrel search algorithm (SSA). The objective function to minimize the energy consumption was achieved by minimizing cumulative active time of all nodes in the ***

关键词： energy efficiency fog computing metaheuristic squirrel search algorithm task scheduling

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Seismic performance of lead-filled steel tube damper: Laboratory test, parameter identification and application

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ENGINEERING STRUCTURES 2020年 219卷 110764-110764页

作者： Luo, Weili Li, Hongtao Zhou, Yun Zhou, Huanzhao Guangzhou Univ Sch Civil Engn Guangzhou Peoples R China

In this paper, a lead-filled steel tube damper (LFSTD) is presented consisting in a steel tube, a lead cylinder, and two steel plate caps. Seven LFSTDs were designed into three groups based on the thickness-diameter ratio, the weakening ratio, and the height-diameter ratio. They were subjected to cycling loading in a laboratory to determine device parameters such as hysteretic behavior, strength, ductility, energy dissipation and damping, and characteristic failure modes, Then, the Bouc-Wen model was used to analytically depict the hysteretic behavior of LFSTDs and the model parameters were identified using an improved squirrel search algorithm. The LFSTDs are later applied into a six-storey reinforced concrete (RC) frame model, and the seismic performance of the dampers with identified Bouc-Wen model parameters is numerically assessed when the RC frame model is under strong and destructive real earthquakes. Both laboratorial and numerical results indicate that the LFSTDs are effective in absorbing seismic energy and in reducing lateral displacements of the protected structure and they have potential of practical applications.

关键词： Lead-filled steel tube damper Bouc-Wen model Energy dissipation Parameter identification squirrel search algorithm

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Research on photovoltaic energy storage micro-grid systems based on improved sliding mode control

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IET RENEWABLE POWER GENERATION 2023年第3期17卷 750-761页

作者： Fu, Changxin Zhang, Lixin Li, Huaisheng Shihezi Univ Coll Mech & Elect Engn Shihezi 832000 Xinjiang Peoples R China Inst Agr Sci 9th Div Xinjiang Prov Tacheng 834700 Peoples R China

In islanded microgrid systems, PV power generation efficiency and energy loss of storage battery are the current research trends. Due to the intermittent and fluctuating characteristics of PV power generation, various loads connected to the DC microgrid system would also bring DC bus voltage low-frequency fluctuations and other problems. In order to solve the above problems, an maximum power point tracking (MPPT) tracking strategy (sliding mode control based on squirrel search algorithm) is proposed in this study;meanwhile, a sliding mode control strategy based on an improved reaching law is designed. The components of the PV energy storage system and the control method are mainly focused on, and the PV energy storage system is optimized by improving the sliding mode control. The proposed control algorithm is verified and analyzed by MATLAB/Simulink simulation platform. Compared with the double integral sliding mode control algorithm MPPT tracking case, sliding mode control based on squirrel search algorithm shows excellent performance (short response time, good tracking maximum power point effect etc.). In addition, the sliding mode control based on the proposed reaching law reduces the system jitter, and effectively reduces jitter and vibration of the PV energy storage battery.

关键词： Matlab sliding mode control strategy DC microgrid system Solar power stations and photovoltaic power systems DC-DC power convertors PV energy storage system maximum power point trackers PV energy storage battery variable structure systems maximum power point tracking tracking strategy energy storage control method DC bus voltage low-frequency fluctuations improved sliding mode control double integral sliding mode control distributed power generation Power system control power generation control control algorithm Control of electric power systems photovoltaic energy storage microgrid systems power grids PV power generation energy loss Multivariable control systems squirrel search algorithm Power electronics, supply and supervisory circuits Power engineering computing power engineering computing photovoltaic power systems islanded microgrid systems Distributed power generation good tracking maximum power point effect etc jitter

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An effective parameter tuned deep belief network for detecting anomalous behavior in sensor-based cyber-physical systems

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THEORETICAL COMPUTER SCIENCE 2022年 931卷 142-151页

作者： Narayanan, S. Krishna Dhanasekaran, S. Vasudevan, V. Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Srivilliputtur Tamil Nadu India

Cyber-physical system (CPS) integrates cyber systems and the corporal world to perform critical processes that are begun from the developments in digital electronics. The sensor placed in CPS is used to monitor and control processes that are vulnerable to anomalies (reliability failures and cyber-attacks). For the detection of anomalies and prevention of destructive issues, Abnormality Detection System (ADS) is introduced. But ADS suffered from a false alarm rate and missed detection rate, which has resulted in the degraded performance in CPS applications. A novel deep learning approach for detecting Anomalous Behavior in sensor-based CPS is presented in this study. Preprocessing is used in the conceptual scheme to remove noisy data. CPS irregularity is then detected using a DL -based deep belief network (DBN) framework. Furthermore, the number of hidden layers, number of iterations, period frequency, and learning rate of DBN are all tuned using a squirrel search algorithm (SSA). Several tests were conducted with the help of data from device CPS to validate the performance of the SSA-DBN model. To assure the SSA-DBN framework's appropriate detection accuracy, a complete comparable study is conducted, and the produced extensive experiments confirmed the framework's outstanding quality on the applicable information over the comparable approaches.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Cyber -physical system Deep learning Anomaly detection squirrel search algorithm

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Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink

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COMPUTING 2021年第4期103卷 707-733页

作者： Mehto, Anjula Tapaswi, Shashikala Pattanaik, K. K. ABV Indian Inst Informat Technol & Management Gwalior India

In rendezvous points (RPs) based data collection, the mobile sink (MS) visits a set of sensor nodes known as RPs for data gathering from wireless sensor networks to minimize the trajectory of MS. Existing RPs based methods are suitable for the scenarios where sensor nodes have uniform data generation rates along with having limited buffer capacity to store the forwarded data. However, in some situations, the sensing rate increases due to the occurrence of unusual events in the surrounding, and the RPs receive more data packets than their capacity. This creates data loss due to buffer overflow. Therefore, the selection of optimal RPs for reliable data gathering, while minimizing the trajectory of MS, is a challenging task. This paper proposes a squirrel search algorithm-based rendezvous points selection (SSA-RPS) method that chooses a set of optimal RPs for reliable data collection. The objective of the SSA-RPS is to minimize the trajectory of MS while visiting a set of optimal RPs under non-uniform data generation and limited buffer capacity constraints of sensor nodes for reliable data acquisition. The SSA-RPS applies an efficient encoding scheme to generate variable dimension squirrels that represent each possible trajectory of MS, and the dimension of squirrel presents the number of RPs. The SSA-RPS also adopts the reselection of RPs to provide a fair energy share among sensor nodes. The simulation results demonstrate that the SSA-RPS outperforms the existing state-of-the-art methods in terms of the number of dropped packets, data gathering ratio, energy consumption, and network lifetime.

关键词： Wireless sensor network Mobile sink Rendezvous point squirrel search algorithm

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Smart energy management for optimal economic operation in grid-connected hybrid power system

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第1期47卷 10096-10116页

作者： Roy, Kallol Mandal, Kamal Krishna Mandal, Atis Chandra Univ Burdwan Univ Inst Technol Dept Elect Engn Burdwan W Bengal India Jadavpur Univ Power Engn Dept Kolkata India Univ Burdwan Dept Phys Burdwan W Bengal India

This manuscript presents a hybrid method for smart energy management (EM) in grid connected microgrid (MG) system. The grid connected micro grid system consists of photovoltaic (PV), wind turbine (WT), micro turbine (MT), and battery. The proposed method is the combined execution of Radial Basis Function Neural Network (RBFNN) and squirrel search algorithm (SSA), hence it is called RBFNN-SSA method. Here, the necessary load demand of grid-connected MG system is constantly monitored by AI strategy. SSA has developed the perfect combination of MG considering the forecast load demand. The major intention of the RBFNN-SSA method is fuel cost involvement, grid power hourly power variation, operation with maintenance cost of grid connected micro grid system. The constraints are the accessibility of renewable energy sources (RES), power requirement and state of charge (SoC) of storage elements. Batteries are used as an energy source, to stabilize and allow the renewable power system units for maintaining constant output power. The proposed method is activated in MATLAB/Simulink working site. Then, the efficiency is assessed with existing methods such as improved artificial bee colony (IABC), bacterial foraging optimizer and artificial neural network (BFOANN), ant lion optimizer (ALO), grasshopper optimization algorithm with particle swarm optimization including artificial neural network (GOAPSNN). The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean bias error (MBE) of proposed and existing methods under 50 and 100 count of trails are also analyzed. The proposed technique achieves the RMSE is 9.3, MAPE is 4.2, and MBE is 2 for 50 number of trails. For 100 count of trails, the proposed method achieves the RMSE is 13.5, MAPE is 3.9 and MBE is 5.7. The mean, median and standard deviation of RBFNN-SSA method achieves 0.9681, 0.9062 and 0.1099. The elapsed time of RBFNN-SSA method attains 30.15 s.

关键词： Energy Management power flow grid connected MG system micro turbine squirrel search algorithm

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Cat-squirrel Optimization algorithm for VM Migration in a Cloud Computing Platform

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2022年第1期18卷 1-23页

作者： Kumar, Ashok C. Sivakumar, P. PSNA Coll Engn & Technol Kothandaraman Nagar Tamil Nadu India PSG Coll Technol Coimbatore India

This paper introduces an approach for the VM migration based on an optimization algorithm named CS in cloud. Selecting the provider is carried out with the usage of multiple constraints, like delay, bandwidth, cost, and load. Subsequently, the effective searching criteria are computed for finding the optimal service on the basis of fitness constraints. The searching criteria are formulated as optimization problems, which are tackled using CS. The proposed CS is designed by integrating CSO with the SSA such that the fitness function is evaluated for the optimal VM migration by considering several parameters, such as delay, cost, bandwidth, and load. Thus, the cloud manager will perform the migration of VM in cloud based on the proposed CS-based VM migration approach. The performance of the CS-based VM migration is evaluated in terms of delay, cost, and load. The proposed CS-based VM migration method achieves the minimal delay of 0.146, minimal cost of 0.052, and the minimal load of 0.182.

关键词： Cat Swarm Optimization Cloud Computing Migration Factor squirrel search algorithm Virtual Machine Migration

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Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition

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China Communications 2022年第2期19卷 47-61页

作者： Miao Shi Chao Wang Wei Zhao Xinshi Zhang Ye Ye Nenggang Xie Department of Mechanical Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China College of Civil Engineering and Architecture Anhui Polytechnic UniversityAnhuiWuhu 241000PR China Department of Computer Science and Technology Anhui University of TechnologyAnhuiMa’anshan 243002PR China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterAnhuiHefei230000PR China Department of Management science and Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and *** Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing *** are two parameters in VMD that have a great influence on the result of signal ***,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based *** original seasonal monitoring condition in SSA is *** feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring ***-based learning is introduced to reposition the position of the population in this *** is applied to optimize the important parameters of ***-VMD model is established to remove ocular artifacts from EEG *** have verified the effectiveness of our proposal in a public dataset compared with other *** proposed method improves the SNR of the dataset from-2.03 to 2.30.

关键词： ocular artifact variational mode decomposition squirrel search algorithm global guidance ability opposition-based learning

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