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Modified seagull optimization algorithm based MPPT for augmented performance of Photovoltaic solar energy systems

AUTOMATIKA 2022年第1期63卷 1-15页

作者： Subramanian, Annapoorani Raman, Jayaparvathy Agni Coll Technol Dept Elect & Elect Engn Thalambur India Sri Sivasubramaniya Nadar Coll Engn Dept Elect & Commun Engn Kalavakkam India

The changing weather conditions and Partial Shading Situation (PSS) create numerous challenges in harvesting available maximum power from the solar Photovoltaic (PV) systems. The limitations of classical and bio-inspired optimization-based Maximum Power Point Tracking (MPPT) methods are incapable of extracting maximum power under PSS. Therefore, this paper presents a Modified seagull optimization algorithm (MSOA) based MPPT approach by incorporating Levy Flight Mechanism (LFM) and the formula for heat exchange in Thermal Exchange optimization (TEO) in the original seagull optimization algorithm (SOA) for accurate tracking of Global Maximum Power Point (GMPP) under transient and steady state operating conditions. The MSOA increases the capability of optimization in finding the optimal value of boost DC-DC converter's duty cycle, D, for operating at GMPP. The superiority of the presented MPPT approach is contrasted with SOA MPPT under uniform irradiation situation and partial shading situations using Matlab Simulink platform. With the presented MSOA MPPT, the settling time and percentage maximum overshoot are reduced by 0.92 times and 0.55 times in comparison to SOA MPPT with increased efficiency. The hardware results validated the simulation results proving the proposed MSOA MPPT as an efficient MPPT for solar PV systems.

关键词： Solar Photovoltaic systems maximum power point tracking seagull optimization algorithm partial shading condition DC-DC boost converter

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Application of Improved seagull optimization algorithm on Optimal Allocation optimizations of Distributed Generation

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ENGINEERING LETTERS 2023年第3期31卷 1页

作者： Qian, Jie Peng, Yuhan Zheng, Haoling Wang, Xi Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China State Grid Chongqing Elect Power Co Bishan Power Supply Branch Chongqing 402760 Peoples R China State Grid Sichuan Elect Power Co Leshan Power Supply Co Chongqing 614000 Sichuan Peoples R China

Optimal allocation optimization (OAO) research is an important technology to construct new distribution networks (DNs) with environmental protection and high efficiency. The OAO research in this paper aims to achieve the more ideal operation of DNs by exploring superior allocation schemes including the optimal power factor, access node and capacity of distributed generations (DGs). In order to solve the complex OAO problems, an improved seagull optimization algorithm with elite reserve (ISOAE) is proposed and applied to two different OAO scenarios. In traditional OAO cases with constant loads, the suggested ISOAE finds better DG allocation schemes with less power loss than the basic seagull optimization algorithm (BSOA) and multiple published algorithms. Simulation experiments show that the number, location, capacity and power factor of DGs all have a non-negligible impact on the operation of DNs. In OAO cases with practical seasonal loads, the access of traditional generator (CG), renewable distributed generators represented by photovoltaic (PV) and wind turbine (WT) into the DNs is studied. Multiple experiments on the IEEE 33-bus network demonstrate that CG achieves the relatively best operation status than PV and WT. However, considering resource conservation and environmental protection, renewable PV and WT are more favored by actual power grids. Furthermore, WT operating at the optimal power factor is superior to the PV with uneven radiation intensity distribution in reducing daily active energy loss and increasing node voltage of DNs. In general, the reasonable integration of renewable energy such as WT is of great significance for achieving the two-carbon strategy of power grids.

关键词： seagull optimization algorithm Renewable energy Optimal allocation Wind turbine

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seagull optimization algorithm-Based Fractional-Order Fuzzy Controller for LFC of Multi-Area Diverse Source System With Realistic Constraints

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Naga Sai Kalyan, CH. Goud, B. Srikanth Reddy, Ch. Rami Udumula, Ramanjaneya Reddy Bajaj, Mohit Sharma, Naveen Kumar Elgamli, Elmazeg Shouran, Mokhtar Kamel, Salah Vasireddy Venkatadri Inst Technol Elect & Elect Engn Guntur India Anurag Univ Dept Elect & Elect Engn Ghatkesar India Malla Reddy Engn Coll Dept Elect & Elect Engn Secunderabad India SRM Univ Dept Elect & Elect Engn Amaravati India Natl Inst Technol Delhi Dept Elect & Elect Engn Delhi India IKG Punjab Tech Univ Elect Engn Dept Jalandhar India Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff Wales Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt

This study initiates the implementation of fractional-order (FO) fuzzy (F) PID (FOFPID) controller fine-tuned using a seagull optimization algorithm (SOA) for the study of load frequency control (LFC). Initially, the SOA-tuned FOFPID regulator is implemented on the widely utilized model of dual-area reheat-thermal system (DARTS), named test system-1 in this work for a perturbation of 10% step load (10% SLP) on area-1. Dynamical analysis of the DARTS system reveals the viability of the SOA-tuned FOFPID control scheme in regulating frequency deviations effectively compared to other control schemes covered in the literature. Later, the presented regulator is implemented on the multi-area diverse sources (MADS) system possessing realistic constraints in this study, termed test system-2. The sovereignty of the presented FOFPID controller is once again evidenced with controllers of PID/FOPID/FPID fine-tuned with the SOA approach. Moreover, the effect of considering practical realistic nonlinearity constraints such as communication time delays (CTDs) on MADS system performance is visualized and the necessity of its consideration is demonstrated. Furthermore, AC-DC lines are incorporated with the MADS system to enhance the performance under heavy-load disturbances and the robustness of the proposed regulatory mechanism is deliberated.

关键词： load frequency control seagull optimization algorithm FOFPID controller 10% SLP AC-DC lines

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Improved seagull optimization algorithm to Optimize Neural Networks with Gated Recurrent Units for Network Intrusion Detection 11

Improved Seagull Optimization Algorithm to Optimize Neural N...

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ma, Sen Wang, Chunzhi Liu, Aijun Zhang, Yucheng Wang, Junfang Chang, Yuguang Yang, Jie Hubei Univ Technol Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781665426053

In the field of network intrusion detection based on a neural network with Gated Recurrent Units (GRU), the accuracy of classification decreases due to the improper setting of the parameters. Therefore an Improved seagull optimization algorithm (ISOA) to optimize GRU parameters has been proposed. The ISOA is proposed to address the shortcomings of the seagull optimization algorithm (SOA) of low convergence accuracy and slow convergence speed in dealing with global optimization problems. The algorithm uses an opposition-based learning method instead of a random method to initialize the population so that the population has a better diversity. To balance the overall and local search capability of the algorithm, in this paper, we design a special factor update formula that converges nonlinearly with the number of iterations, allowing the algorithm to better jump out of locally optimal solutions and speed up convergence. The improved algorithm is used to obtain better parameters and uses them to set the parameters of the GRU to build a better-performing classification model. Multiple UCI datasets are used and compared with other methods and finally validated using the NSL-KDD datasets for network intrusion detection. The experimental results show that compared to the performance of long short-term memory(LSTM) units, multilayer perceptron(MLP), and the traditional neural network with GRU, the classification accuracy of the method with ISOA for optimizing GRU is improved on each dataset, which helps to improve the classification performance in network intrusion detection.

关键词： seagull optimization algorithm Gated Recurrent Units Network Intrusion Detection

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Sign Language Recognition using Improved seagull optimization algorithm with Deep Learning Model 2

Sign Language Recognition using Improved Seagull Optimizatio...

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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Sivaraman, R. Santiago, S. Chinnathambi, K. Sarkar, Swagata Sangeethaa, S. N. Srimathi, S. Dwaraka Doss Goverdhan Doss Vaishnav Coll Dept Math Chennai 600106 Tamil Nadu India St Josephs Coll Autonomous Dept Comp Sci Trichy 620002 India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Comp Sci & Engn Chennai Tamil Nadu India Sri Sairam Engn Coll Artificial Intelligence & Data Sci Chennai 44 Tamil Nadu India Bannari Amman Inst Technol Comp Sci & Engn Sathyamangalam Tamil Nadu India SIMATS Saveetha Sch Engn Dept Bio Technol Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Sign Language Recognition (SLR) covers the ability to translate Sign Language (SL) signals into written or spoken languages. This technique is useful for hearing-impaired people by offering them an effective method to communicate with persons having trouble in recognizing SLs. It can also be employed for generating automatic captions in real-time for live actions and videos. Various models of SLR include machine and deep learning, and computer vision techniques. A commonly employed technique involves using a camera to capture the body and hand movements of the signer, processing the video data to identify the signs. One of the high tasks of SLR includes the flexibility in SL over numerous individuals and cultures, the complex of definite signs, and the need for real-time process. This manuscript presents a SL Recognition Using an Improved seagull optimization algorithm with Deep Learning (SLR-ISOADL) methodology. The SLR-ISOADL approach aims to exploit a hyperparameter-tuned DL model to recognize and classify the SLs. In the SLR-ISOADL approach, a bilateral filtering (BF) approach can be applied to get rid of the noise. For learning and deriving intrinsic patterns, the SLR-ISOADL approach employs the AlexNet model. Besides, the ISOA can be applied for optimal hyperparameter election of the AlexNet model. Finally, the multilayer perceptron (MLP) technique can be exploited to detect and classify the SLs. The analytical experiment of the SLR-ISOADL technique is conducted on a benchmark dataset. The investigational analysis highlighted that the SLR-ISOADL technique gains enhanced detection outcomes in terms of distinct measures.

关键词： Sign Language Recognition seagull optimization algorithm Deep Learning AlexNet Multilayer Perceptron

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Improved seagull optimization algorithm Incorporating Golden Sine and Tent Chaotic Perturbations 6

Improved Seagull Optimization Algorithm Incorporating Golden...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Chen, Ange Peng, Hanzhang Zhong, Yu Ren, Huimin China Acad Launch Vehicle Technol Beijing Aerosp Automat Control Inst Beijing Peoples R China Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

Aiming at the defects of slow convergence and easy to fall into local optimum of the seagull optimization algorithm, this paper proposes an improved seagull optimization algorithm incorporating golden sine and chaotic perturbation of tent mapping. This algorithm enhances the global search ability through tent chaotic disturbance and Levy flight, accelerates the convergence through golden sine to improve the local search ability. In this paper, the original fixed convergence factor is transformed into a nonlinear decreasing convergence factor to improve the optimization efficiency. The performance is tested on the benchmark functions, and it is used to solving the multiprocessor task scheduling problem. Compared with other algorithms, experiments show that TGSOA has significant improvement over other algorithms in terms of convergence speed and robustness.

关键词： seagull optimization algorithm Golden sine Tent Mapping Levy flight

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Enhanced seagull optimization algorithm for Photovoltaic Cell Parameter Estimating 41

Enhanced Seagull Optimization Algorithm for Photovoltaic Cel...

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第41届中国控制会议

作者： Hongyin Wei Kailin Xu Jianming Zhang Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control TechnologyZhejiang University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,an enhanced seagull optimization algorithm based on a double group evolutionary mechanism(DSOA) is proposed for photovoltaic(PV) cell parameter *** the DSOA,a double group search strategy and time-based mutation strategy are added to the population renewal mechanism of the conventional *** performance of DSOA on standard benchmark functions is surprising;it was good at accuracy and exploitation,also convergence *** with SOA and the other two classical intelligent optimization algorithms,the performance of DSOA is comprehensively *** is adopted to estimate PV cell *** simulation results demonstrate that estimated currents of circuit model based on DSOA have high similarity to the measured value of the photovoltaic cell.

关键词： Photovoltaic(PV) parameter estimating seagull optimization algorithm intelligent optimization algorithms

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Improved seagull optimization algorithm Based on Multi-Strategy Integration 34

Improved Seagull Optimization Algorithm Based on Multi-Strat...

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34th Chinese Control and Decision Conference (CCDC)

作者： Shi, Haibin Li, Baoda Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781665478960

Aiming at the defects of seagull optimization algorithm (SOA) in solving optimization problems, such as local optimization, slow convergence speed and low optimization accuracy, a seagull optimization algorithm (SCSOA) based on integration of improved Sobol sequence and Cauchy variation is proposed. First, initialize the population using the Sobol sequence to make the seagulls more evenly distributed in the initial solution space;Secondly, use a nonlinear function to replace the original convergence factor to guide the seagull to always maintain a larger body degree of freedom during the search process, enhance the global search ability, and avoid falling into the local optimum;Then, the Cauchy variation strategy is introduced, so that the individual can better find the optimal solution and enhance the ability of the algorithm to jump out of the local optimum;Finally, use the benchmark function to test the improved algorithm, and compare it with the original algorithm and the experimental results of other algorithms. The results show that SCSOA performs better in convergence speed and optimization accuracy, and the global optimization capability is also improved.

关键词： seagull optimization algorithm Sobol Sequence Cauchy Variation

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Research on emergency vehicle routing method in emergency material support stage based on seagull optimization algorithm 24

Research on emergency vehicle routing method in emergency ma...

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International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Zhang, Bo Ding, Mengmeng Li, Qiaochu Shenyang Univ Sch Informat Engn Shenyang 110044 Liaoning Peoples R China

ISBN: (纸本)9798400709784

In the stage of emergency supplies reserve, in order to ensure the smooth implementation of the emergency supplies work plan, the problem of vehicle routing for emergency vehicles is studied. Firstly, a vehicle routing model for emergency vehicles with the lowest total cost including carbon emission cost is constructed. Secondly, a strategy is introduced to control parameter A by incorporating a nonlinear decreasing inverse S-shaped function into the attack behavior part of the basic seagull optimization algorithm, and the seagull optimization algorithm is improved. Finally, based on the classic Solomon test data, random data is selected to verify the algorithm and model, and compared with the basic seagull optimization algorithm. The experimental results show that the improved seagull optimization algorithm has better convergence than the basic seagull optimization algorithm, indicating that the model and algorithm have good applicability and practical value.

关键词： Emergency supplies seagull optimization algorithm Vehicle routing

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Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved seagull optimization algorithm

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ENERGIES 2022年第23期15卷 9197页

作者： Zhang, Suqi Zhang, Ningjing Zhang, Ziqi Chen, Ying Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China Tianjin Univ Commerce Sch Sci Tianjin 300134 Peoples R China South China Co China Construct Engn Bur Ltd Shenzhen 518000 Peoples R China State Grid Tianjin Mkt Serv Ctr Metrol Ctr Tianjin 300200 Peoples R China

Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved seagull optimization algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved seagull optimization algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the seagull optimization algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system.

关键词： power load forecasting electric management system Support Vector Machine Improved seagull optimization algorithm seagull optimization algorithm

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