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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Abu, Sayem M. Hannan, M.A. Mansor, M. Ker, Pin Jern Yaw Long, Chua Universiti Tenaga Nasional Coe Department of Mechanical Engineering Kajang43000 Malaysia Sunway University School of Engineering and Technology Petaling Jaya Bandar Sunway47500 Malaysia Universiti Tenaga Nasional Coe Department of Electrical and Electronic Engineering Kajang43000 Malaysia

ISBN: (纸本)9781665471640

This study focuses on a sustainable microgrid-based hybrid energy system (HES), primarily focusing on analyzing the performance of the fuel cell and its impact on the overall HES into optimizing system performance. This system relies on a single renewable energy source, a photovoltaic (PV) system that is integrated with the energy storage system (ESS) including hydrogen-based fuel cell, battery, and supercapacitor for effective power management. The optimization of HES performance is achieved through fine-tuning of the proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. The load profile utilized in the microgrid (MG) is characterized by a constant power output, ensuring a stable and uninterrupted supply of electricity from 6am to 6pm in a 24-hour time period. This approach is comparable to meeting the specific demands of industrial and critical facilities, such as manufacturing plants and hospitals, where continuous power is important. The selection of a constant load profile is benchmark with the alignment of Denham Hydrogen Demonstration Plant, Western Australia, enhancing the MG's overall reliability and validation into real-world application. Through simulation and analysis using MATLAB Simulink, the results demonstrate the remarkable impact of PSO on enhancing the fuel cell system's efficiency with air consumption, and fuel consumption reduction by utilization of 90% of the H2 to electrical energy. Significantly, the optimized total source power output enables seamless energy storage and intelligent load matching, leading to a stable and reliable grid power supply. This research study findings highlights the essential role of PSO in elevating sustainability and maximizing resource utilization within microgrid-based hybrid energy systems, establishing a pathway towards a greener and more sustainable energy future. © 2023 IEEE.

关键词： battery fuel cell hybrid energy storage integrated grid hydrogen optimization algorithm solar supercapacitor sustainable

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A Decision support tool to analyze food properties from near infrared spectroscopy 15

A Decision support tool to analyze food properties from near...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： Parrenin, Loïc Lorbieski, Rodolfo Jaraceski, John Cleber Danjou, Christophe Agard, Bruno Department of Mathematics and Industrial Engineering Polytechnique of Montreal MontrealQC Canada SENAI Innovation Institute for Embedded Systems SC Florianópolis Brazil

ISBN: (纸本)9798350314182

Near infrared spectroscopy (NIRS) is an analytical technique that is gaining popularity in the food industry due to its low operating costs, rapid analysis and non-destructive sample technique. Numerous studies have shown the relevance of NIR spectra analysis to determine certain quality attributes of food. This makes it attractive for use in quality control and continuous monitoring of food processing. However, the calibration process of NIR is difficult and time-consuming. Depending on the configuration of the NIR instrument, the sample to be analyzed and the attribute to be predicted, the analysis methods and techniques vary. This makes calibration a challenge for many manufacturers. This article aims to develop a decision support tool to assess food properties based on the analysis of selected features of NIR spectra. It intends to provide support to calibrate a predictive model based on NIR spectra. The methodology-based decision support tool was evaluated on cocoa bean samples. The tool suggested using the SG filter technique and PLSR machine learning model to predict the moisture and fat content of cocoa beans. The PLSR model with 4 components trained from 63 wavelengths obtained excellent results for the prediction of moisture content with an R2CV of 0.9 and an RMSEP of 0.28. While the PLSR model with 8 components trained from 166 wavelengths obtained satisfactory results for the prediction of fat content with an R2CV of 0.93 and an RMSEP of 1.52. ©2023 IEEE.

关键词： decision support tool food quality Industry 4.0 machine learning near-infrared spectroscopy optimization algorithm

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Construction of an Optimized Configuration Model for Wind Power System Energy Storage Based on OOB-GWO-SVR

Construction of an Optimized Configuration Model for Wind Po...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Ren, Xiaojun Guangzhou Salvage Guangzhou Design and Construction of Naval Architecture and Ocean Structure Guangdong China

ISBN: (纸本)9798350313413

Wind power systems are volatile and uncertain. In this paper, the wind power system was studied for optimal configuration to improve the stability and reliability of the wind power system. This article proposed a wind power system energy storage optimization configuration model based on OOB-GWO-SVR. Through the simulation analysis of the actual data of a wind farm, this paper verified the accuracy and effectiveness of the proposed model, and compared it with other commonly used algorithms. By experimental analysis, the optimal energy storage capacity of the proposed model was 110 kWh, which had higher prediction accuracy and optimization capability than empirical formulas and genetic optimization algorithm (GOA). © 2023 IEEE.

关键词： Capacity optimization Energy storage system optimization algorithm Predicted power

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Optimal Selection of Switch Model Parameters for ADC-Based Full-Bridge Converter

Optimal Selection of Switch Model Parameters for ADC-Based F...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Alsarayreh, Saif Süto, Zoltán University of Technology and Economics Department of Automation and Applied Informatics Budapest Budapest Hungary

ISBN: (纸本)9798350327816

The incorporation of real-time Hardware-In-the-Loop (HIL) simulators has become one of the pillars of the power electronics control design cycle. This integration is necessary to verify the effectiveness of controller implementations. Despite the rapid evolution in FPGA devices' computational capacity employed by Hardware-in-the-Loop (HIL) tools, developers are consistently faced with the need to strike a balance between acceptable accuracy and the practicality of implementing models and techniques. The methodology presented in this paper is for implementing a full-bridge converter using the Associate Discrete Circuit (ADC) model, specifically designed for real-time simulator applications. Furthermore, this study presents a novel methodology for parameter selection of (ADC) parameters utilizing the Artificial Bee Colony algorithm (ABC). The utilization of the ADC-based model facilitates the establishment of a uniform converter topology during simulation, irrespective of the switch states. The simulation results demonstrate the effectiveness of the introduced methodology in selecting optimal parameters for an ADC switch-based full-bridge converter, reducing overshoot in the converter's output voltage and current. © 2023 IEEE.

关键词： Artificial bee colony field-programmable gate array Full-bridge converter optimization algorithm Real-time Simulation Terms-Associate Discrete Circuit

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Research on the Operation optimization of Wind Storage Combined System Based on Compressed Air Energy Storage Technology

Research on the Operation Optimization of Wind Storage Combi...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Lang, Yitao Zhou, Zuxu Liu, Quanliang PowerChina Renewable Energy Company Ltd. Beijing China Institute of Renewable Energy and Energy Storage Technologies PowerChina Renewable Energy Company Ltd. Beijing China POWERCHINA HeBei Electric Power Engineering Co. Ltd. HeBei China Hebei Electric Power Design & Survey Technology Innovation Center HeBei China

ISBN: (纸本)9798331506797

With the expansion of the scale of renewable energy units connected to the power system, the problems of volatility and instability brought about by them are becoming more and more prominent. Compressed air energy storage technology has the advantages of high storage capacity, long life and small special investment, which is expected to become an effective means to promote the consumption of renewable energy and maintain the stability of the power grid. However, the current relationship between CAES and renewable energy capacity is not clear, which leads to its low economic efficiency and affects its promotion. In this paper, CAES technology is combined with wind power generation to construct a new joint system configuration scheme, constructs a physical structure model, and considers the equipment operation constraints, establishes a two-layer optimization model, with the upper layer taking the ROI as the objective, and the lower layer taking the volume of the storage tank as the objective, and solves the problem using the NSGA-II algorithm. The example results show that the two-layer optimization model is economical and can provide a reference for the operation decision of the wind storage system. © 2025 IEEE.

关键词： combined wind storage system compressed air energy storage operation optimization optimization algorithm

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Adaptive Control Method for Driverless Cars Considering Probabilistic Graph Path Planning for Intelligent Vehicles 3

Adaptive Control Method for Driverless Cars Considering Prob...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Zhou, Zhen School of Intelligent Manufacturing Panzhihua University Panzhihua China

ISBN: (纸本)9798350317022

Traditional path planning methods usually aim to optimize the shortest path length or time, which makes it difficult to accurately plan safe and feasible vehicle driving paths in complex environments. To solve this problem, the algorithm combines a potential field model and a probability graph algorithm. Under the premise of comprehensive evaluation of vehicle driving risks, an artificial potential field algorithm is used to construct a potential field model for the environment. This model mainly considers the coupling effects of multiple obstacles, environmental threats, and road conditions in the environment, and can accurately represent the interaction between vehicles and the environment. At the same time, probability graph algorithm is used for path planning to optimize the multi-dimensional transportation costs between nodes. This method establishes a multidimensional access cost evaluation matrix between sampling points, integrating multiple aspects of vehicle driving, such as road conditions, traffic flow, and turning radius. Finally, the simulation results indicate that the algorithm comprehensively considers the coupling effects of obstacles, environmental threats, and road conditions in the path planning process. The probability graph algorithm has been used to optimize multidimensional travel costs, which can generate feasible, safe, and efficient travel paths, providing a multi-objective optimization path planning algorithm for intelligent vehicles. The application of this algorithm can reduce the load of intelligent vehicle path planning, effectively solve path planning problems in complex environments, and further promote the development of unmanned intelligent vehicle technology. © 2023 IEEE.

关键词： Adaptive Control Driverless Car optimization algorithm Probabilistic Graph Path Planning algorithm

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optimization algorithms for proportionate flowshop scheduling problems with variable maintenance activities

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 117卷 164-170页

作者： Cheng, Chen-Yang Ying, Kuo-Ching Chen, Hsia-Hsiang Lin, Jia-Xian Natl Taipei Univ Technol Ind Engn & Management Taipei Taiwan Nanya Technol Corp New Taipei Taiwan

Proportionate permutation flowshop (PPF) is a class of flowshop problems that has appeared in the literature since the early 1980s. However, no research has been conducted on PPF with variable maintenance activities (VMAs). To remedy this research gap, this study proposed optimization algorithms for six PPFs with equal- and unequal-duration VMAs, aiming to minimize their total completion time, maximum lateness, and maximum tardiness, respectively. All the problems studied here were optimally solved in polynomial times using the proposed algorithms. Accordingly, the proposed optimization algorithms could be applied by decision-makers in actual PPF scheduling environments.

关键词： Scheduling Proportionate permutation flowshop Variable maintenance activities optimization algorithm

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Research and Implementation of Parallel CART algorithm Based on Distributed Database 6

Research and Implementation of Parallel CART Algorithm Based...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Wang, Jie The Open University of Guangdong School of Artificial Intelligence Guangzhou China

ISBN: (纸本)9798350313444

This paper presents a parallel CART method for multivariate association query. The amount of communication information is reduced by performing two semi-joins. This method adopts a multi-node parallel method, which greatly speeds up the execution efficiency of the system. The sequence of operations generated by this algorithm has global optimization characteristics. One of its important development trends is to realize effective data query by optimizing the database. The simulation experiment proves that the method has fast processing speed, high node utilization rate and strong practicability. This method is suitable for large data processing. © 2023 IEEE.

关键词： Distributed database optimization algorithm parallel CART algorithm query processing

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A Comparative Study of Inversion optimization algorithms for Underground Metal Target Detection

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IEEE ACCESS 2020年 8卷 126401-126413页

作者： Wan, Yadong Wang, Zhen Wang, Peng Zhang, Chao Duan, Shihong Li, Na Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Datang Gohigh Data Networks Technol Co Ltd Beijing 100191 Peoples R China Univ Sci & Technol Beijing Sch Mat Sci & Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China

Underground metal target detection refers to estimating the properties of underground metal targets based on a set of observed data. Electromagnetic induction (EMI) method and the least-squares inversion provide the data acquisition method and the parameter estimation method for the detection, respectively. As an important part of least-squares inversion, optimization algorithms directly affect the efficiency of the least-squares inversion. To improve the efficiency of underground metal target detection, it is necessary to compare the performance of different optimization algorithms. In this paper, we analyzed the characteristic of the complex EMI forward model using sensitivity analysis and inverse sensitivity analysis at first. Then, the EMI forward model and least squares were used to build the objective function. The estimation error, run time and number of iterations of six numerical optimization algorithms were compared under different signal-to-noise ratios (SNRs) and under different data acquisition spacing. The algorithms include gradient descent, steepest descent, Newton's method, Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, conjugate gradient method and Levenberg-Marquardt (LM) algorithm. A simulation platform was established to generate observed data and compare the optimization algorithms. The results of algorithm comparison showed that the BFGS, conjugate gradient and LM algorithm can efficiently and accurately estimate the properties of underground metal targets and the LM algorithm has the shortest run time and the least number of iterations. Finally, we carefully analyzed the time consumption of each optimization algorithm. The results show that the calculation of the gradient and step length selection greatly affect the efficiency of the optimization algorithms.

关键词： Electromagnetic induction (EMI) least-squares inversion optimization algorithm sensitivity analysis underground metal target detection

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Heterogeneous UAV Swarm Collaborative Search Mission Path optimization Scheme for Dynamic Targets

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INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING 2024年第1期2024卷

作者： Cheng, Kaixin Hu, Tao Wu, Di Li, Tingli Wang, Shu Liu, Kaiyue Tian, Zhifu Yi, Dong Strateg Support Force Informat Engn Univ Coll Data Target Engn Zhengzhou 450001 Peoples R China

To solve the problem of collaborative search mission planning (CSMP) for a heterogeneous unmanned aerial vehicle (UAV) swarm for dynamic targets, this paper proposes a new CSMP scheme for a heterogeneous UAV swarm. First, a new polar coordinate system motion method is used to establish the UAV motion model and decision input solution model, which effectively solves the path-unified coding problem of a heterogeneous UAV swarm. Then, in the mission area, a dynamically updated multisearch situation map model is established. Finally, to improve the global searching capability of a UAV, an improved path optimization algorithm PSO-Rolling Horizon Control (PSO-RHC) is designed. Multiple Monte Carlo simulations are performed for three time-sensitive moving target types and three constraint types. The simulation results show that the task execution efficiency indexes of the proposed scheme for the decision input solution model, pheromone update mechanism, and optimization algorithm are improved by 188%, 72%, and 102%, respectively, and the overall performance is improved by 227%.

关键词： heterogeneous UAV swarm mission planning optimization algorithm path optimization

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