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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Qiu, Runze Niu, Xiaobing Liu, Shenglin Wang, Heng Zhou, Xin Dalin Maritime University College of Marine Electrical Engineering Dalian China

ISBN: (纸本)9798350339673

The modular multilevel converter (MMC) arm current contain AC current, DC current, and circulating current. Therefore, by controlling the arm current, the control structure can be simplified and indirect suppression of the circulating current can be achieved. This paper establishes a mathematical model of the MMC based on the arm current in a three-phase stationary coordinate system, establishes the state equations of each arm current considering perturbations. Proposes a proportion integrated resonant and phase-looking loop observer controller with decoupled tracking and immunity performance. The problem of poor resilience of the PIR controller to disturbances is addressed. The controller parameter adjustment problem under unbalanced grid is solved by using the seagull optimization algorithm to achieve online adaptive parameter adjustment of the controller. An MMC rectifier-side simulation model is built to demonstrate the advantages of this control method over the conventional fixed-parameter quasi-proportional resonance control under balanced and unbalanced grid conditions. © 2023 IEEE.

关键词： arm current control modular multilevel converter phase-looking loop observer seagull optimization algorithm

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Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model

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WATER 2023年第21期15卷 3724页

作者： Peng, Jianhe Xie, Wei Wu, Yan Sun, Xiaoran Zhang, Chunlin Gu, Hao Zhu, Mingyuan Zheng, Sen Anhui & Huaihe River Inst Hydraul Res Anhui Prov Water Conservancy Engn Qual Testing Ctr Hefei 230088 Peoples R China Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210098 Peoples R China Xinjiang Inst Water Resources & Hydropower Res Urumqi 830049 Peoples R China Anhui Huaihe River Management Bur Bengbu 233099 Peoples R China

Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features of long-time data series of the traditional statistical model, in this paper, an improved long short-term memory (LSTM) model and weighted Markov model are introduced to predict sluice deformation. In the method, the seagull optimization algorithm (SOA) is utilized to optimize the hyper-parameters of the neural network structure in LSTM primarily to improve the model. Subsequently, the relevant error sequences of the fitting results of SOA-LSTM model are classified and the Markovity of the state sequence is examined. Then, the autocorrelation coefficients and weights of each order are calculated and the weighted and maximum probability values are applied to predict the future random state of the sluice deformation. Afterwards, the prediction model of sluice deformation on the SOA-LSTM-weighted Markov model is proposed. Ultimately, the presented model is used to predict the settlement characteristics of an actual sluice project in China. The analysis results demonstrate that the proposed model possesses the highest values of R2 and the smallest values of RMSE and absolute relative errors for the monitoring data of four monitoring points. Consequently, it concluded that the proposed method shows better prediction ability and accuracy than the SOA-LSTM model and the stepwise regression model.

关键词： deformation prediction model sluice long short-term memory seagull optimization algorithm weighted Markov model

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An enhanced energy management system for coordinated energy storage and exchange in grid-connected photovoltaic-based community microgrids

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JOURNAL OF ENERGY STORAGE 2024年 99卷

作者： Abdelhameed, Esam H. Abdelraheem, Samah Mohamed, Yehia Sayed Abouheaf, Mohammed Marey, Samy A. Diab, Ahmed A. Zaki Aswan Univ Fac Energy Engn POB 81528 Aswan Egypt Modern Univ Technol & Informat Cairo Egypt Minia Univ Fac Engn Al Minya Egypt Bowling Green State Univ Sch Engn Bowling Green OH 43403 USA King Saud Univ Deanship Sci Res POB 2460 Riyadh 11451 Saudi Arabia

This paper introduces an enhanced coordinated community energy management system (CEMS) for a community microgrid. It is designed to optimize residential energy consumption and facilitate energy sharing among smart homes. The proposed solution integrates constrained mixed-integer programming (CMIP) approach along with advanced optimization technique and cooperative game theory-based pricing mechanism to address critical challenges in existing energy management strategies, including load scheduling and efficient peer-to-peer energy trading. The CEMS approach is adopted for grid-connected community power systems that incorporate local energy sources such as photovoltaic and battery storage systems. This solution is implemented under a time-ofuse dynamic pricing model for energy transfer from the main grid to the community peers, a modified midmarket pricing mechanism for the P2P energy exchange, and feed-in tariff for energy transfer from the community peers to the main grid. Besides, seagull optimization algorithm is applied to relax the CMIP problem to get the optimal rational solution of the scheduling problem which is subjected to a rounding process to obtain the best feasible solution. The obtained results indicate a substantial reduction in electricity costs, achieving daily savings of 72.21 % and reducing grid dependency by 41.95 %. Moreover, 82.2 % of the community's energy demand was met through locally generated resources with 18.39 % enhancement of the overall CMG selfconsumption ratio comparing to the reference operating scenario. Ultimately, the study demonstrates the effectiveness of the management system in improving load profiles, maximizing the use of local renewable energy sources, and reducing electricity expenses, thereby providing a sustainable and economically viable model for future smart grids.

关键词： Community energy management system Load scheduling Energy sharing Renewable energy resource Peer-to-peer energy trading seagull optimization algorithm

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An early fault diagnosis method of common-rail injector based on improved CYCBD and hierarchical fluctuation dispersion entropy

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DIGITAL SIGNAL PROCESSING 2021年 114卷 103049-103049页

作者： Ke, Yun Yao, Chong Song, Enzhe Dong, Quan Yang, Liping Harbin Engn Univ Coll Power & Energy Engn Harbin 150001 Peoples R China

Early fault diagnosis of common rail injectors is essential to reduce diesel engine testing and maintenance costs. Therefore, this paper proposes a new common rail injector early fault diagnosis method, which combines the Maximum Second-order Cyclostationary Blind Deconvolution (CYCBD) optimized by the seagull optimization algorithm (SOA) and Hierarchical Fluctuation Dispersion Entropy (HFDE). First, we use SOA adaptively to seek the optimal filter length of CYCBD and use the optimal CYCBD to filter the fuel pressure signal of the high-pressure fuel pipe. Then, in order to make up for the shortcomings of Multi-scale Fluctuation Dispersion Entropy (MFDE) ignoring high-frequency component information, this paper proposes HFDE to extract the fault characteristics after filtering. Finally, we input the fault characteristics into Least Squares Support Vector Machines (LSSVM) for classification and recognition. Through the analysis of experimental data, the method proposed in this paper can effectively identify the early failure state of the common rail injector. Compared with the existing methods, the proposed method has a higher fault recognition rate. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Maximum second-order cyclostationarity blind deconvolution Hierarchical fluctuation dispersion entropy seagull optimization algorithm Early fault diagnosis Common-rail injector

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Three Dimensional Pulse Coupled Neural Network Based on Hybrid optimization algorithm for Oil Pollution Image Segmentation

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REMOTE SENSING 2019年第9期11卷 1046-1046页

作者： Jia, Heming Xing, Zhikai Song, Wenlong Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

关键词： oil pollution image segmentation 3DPCNN seagull optimization algorithm thermal exchange optimization

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Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning

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SENSORS 2023年第22期23卷 9119页

作者： Lu, Na Wang, Bo Zhu, Xianglin Jiangsu Univ Sch Elect & Informat Engn Key Lab Agr Measurement & Control Technol & Equipm Zhenjiang 212013 Peoples R China

Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the entire fermentation process. Therefore, this study proposes a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm (ISOA) and Gaussian process regression (GPR). First, an improved density peak clustering algorithm (ADPC) was used to divide the sample dataset into multiple local sample subsets. Second, an improved seagull optimization algorithm was used to optimize and transform the Gaussian process regression model, and a sub-prediction model was established. Finally, the fusion strategy was determined according to the connectivity between the test samples and local sample subsets. The proposed soft sensor model was applied to the prediction of key biochemical parameters of the marine lysozyme fermentation process. The simulation results show that the proposed soft sensor model can effectively predict the key biochemical parameters with relatively small prediction errors in the case of limited training data. According to the results, this model can be expanded to the soft sensor prediction applications in general nonlinear systems.

关键词： marine lysozyme seagull optimization algorithm Gaussian process regression soft sensor grayscale correlation analysis

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An optimized joint medium access control and routing protocol with iterative contention window adjustment mechanism for a guaranteed lifetime in Internet of Things-based wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2022年第14期35卷 e5251-e5251页

作者： Nizampatnam, Srikanth Shankar, Thirunarayanan Yamuna, Govindarajan Annamalai Univ Dept ECE Annamalainagar Tamil Nadu India SASI Inst Technol & Engn Dept ECE Tadepalligudem Andhra Pradesh India Annamalai Univ Dept ECE Trichy Tamil Nadu India Govt Coll Engn Trichy Tamil Nadu India

The problem of extending the lifespan of wireless sensor networks (WSN) based on the Internet of Things (IoT) has been widely investigated over the last 20 years. This paper proposes an Optimized J-RMAC (optimized joint routing and media access control protocol) to guarantee the network lifetime in IoT-based WSN. Initially, all sensor nodes report their position and coverage information to the sink, which uses this information to pick a list of active nodes based on energy usage and active time. Then, the k-covered network is formed to execute the routing task by selecting the active nodes with the largest sensing areas. A multi-objective seagull optimization algorithm (MO-SOA) represents routing paths between the source and destination by considering two objective functions: energy consumption cost and end-to-end delay of a routing path. After that, the contention window of the nodes in the routing path is adjusted using a new iterative adaptive adjustment process of the contention window with adjustment parameters (IAACW-AP) to avoid message conflicts. The proposed protocol is simulated in the NS2 simulator. The performance of the proposed protocol will be compared with existing strategies in terms of network lifetime, packet delivery ratio, communication overhead, energy consumption, and delay.

关键词： contention window IAACW-AP IoT-WSN seagull optimization algorithm

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Breaking through the limitation of carbon price forecasting: A novel hybrid model based on secondary decomposition and nonlinear integration

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 362卷 121253页

作者： Lan, Yuqiao Huangfu, Yubin Huang, Zhehao Zhang, Changhong Southwestern Univ Finance & Econ Sch Stat Chengdu Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Guangzhou Univ Guangzhou Inst Int Finance Guangzhou Peoples R China George Washington Univ Dept Decis Sci Washington DC USA

Carbon trading is one of the pivotal means of carbon emission reduction. Accurate prediction of carbon prices can stabilize the carbon market, mitigate investment risks, and promote green development. In this study, firstly, the IVMD and ICEEMDAN are used to decompose carbon price quadratically;secondly, the Dispersion entropy is used to identify the sequence frequency, and then the SOA-LSSVM model and TCN model are used to predict the high-frequency and low-frequency sequences, respectively;finally, the prediction results are integrated by SOAGRU. As a result, the hybrid IVMD-ICEEMDAN-SOALSSVM/TCN-SOAGRU model was constructed. This framework consistently performs best under two carbon markets, the CEEX Guangzhou and the EU ETS, compared with 21 comparative models, with MAPEs of 0.42% and 0.83%, respectively. The main contributions are as follows: (1) A novel IVMD-ICEEMDAN secondary decomposition method is proposed, which improves the problem of poorly determining the value of the decomposition modal number K in the traditional VMD method and improves the efficiency of the carbon price sequence decomposition. (2) A hybrid forecasting model of LSSVM and TCN is proposed, effectively capturing the features of different sequences. (3) optimization for LSSVM and GRU using SOA improves the stability and adaptability of the model. The article provides governments, enterprises, and investors with novel and effective carbon price forecasting tool.

关键词： Carbon price forecasting Secondary decomposition Improved variational mode decomposition Machine learning seagull optimization algorithm

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IoT based energy management in smart energy system: A hybrid SO²SA technique

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2021年第5期34卷 e2893-e2893页

作者： Muthubalaji, Sankaramoorthy Srinivasan, Sundararajan Lakshmanan, Muthuramalingam CMR Coll Engn & Technol Dept EEE Hyderabad Telangana India CMR Inst Technol Dept Elect & Elect Engn Bengaluru India

In this manuscript, an energy management system (EMS) is proposed to the distribution system (DS) using Internet of Things (IoT) framework with a hybrid system. The proposed hybrid method is the combination of the seagull optimization algorithm (SOA) and Owl Search algorithm (OSA), hence it is called SO(2)SA technique. The principle objective of the SO(2)SA technique is to optimize managing distribution system power and resources through continuous monitoring of the data from a communication framework based on IoT. In SO(2)SA technique, every home device is connected to the module of data acquisition, which indicates an IoT object along with a unique IP address as a result of huge mesh wireless network devices. The sending data are processed through SO(2)SA technique. Similarly, the IoT architecture of the distribution system enhances the flexibility of these networks and gives optimal utilization of obtainable resources. In addition, the SO(2)SA technique is responsible for meeting the overall power and supply requirements. The proposed method is implemented in MATLAB/Simulink site and the efficiency is likened to the other different methods. In 50 trail numbers, the RMSE, MAPE, and MBE range of SO(2)SA technique represents 5.63, 0.90, and 1.035. Thus, the proposed technique is highly competent over all the existing approaches.

关键词： demand response distribution system fuel cell main grid Owl search algorithm photovoltaic seagull optimization algorithm wind turbine

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Smart predictive viscosity mixing of CO2-N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2024年第2期12卷

作者： Ewees, Ahmed A. Thanh, Hung Vo Al-qaness, Mohammed A. A. Abd Elaziz, Mohamed Samak, Ahmed H. Univ Bisha Coll Comp & Informat Syst Dept Informat Sci & Cybersecur POB 551 Bisha Saudi Arabia Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Computat Mech Ho Chi Minh City Vietnam Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Peoples R China Zhejiang Optoelect Res Inst Jinhua 321004 Peoples R China Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Menoufia Univ Fac Sci Shibin Al Kawm Egypt Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Middle East Univ MEU Res Unit Amman Jordan

Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the seagull optimization algorithm with Marine Predator algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets.

关键词： CO2 N2 CCUS Marine predators algorithm seagull optimization algorithm

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