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Advances in the Application of Intelligent Algorithms to the optimization and control of Hydrodynamic Noise: Improve Energy Efficiency and System optimization

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APPLIED SCIENCES-BASEL 2025年第4期15卷 2084-2084页

作者： Xu, Maosen Fan, Bokai Lin, Renyong Lin, Rong Wu, Xian Zheng, Shuihua Gu, Yunqing Mou, Jiegang China Jiliang Univ Coll Metrol Measurement & Instrument Hangzhou 310018 Peoples R China Leo Grp Pump Zhejiang Co Ltd Wenling 317500 Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Peoples R China Zhejiang Univ Coll Energy Engn Hangzhou 310012 Peoples R China

Hydrodynamic noise is induced by hydrodynamic phenomena, such as pressure fluctuations, shear layers, and eddy currents, which have a significant impact on ship performance, pumping equipment efficiency, detection accuracy, and the living environment of marine organisms. Specifically, hydrodynamic noise increases fluid resistance around the hull, reduces speed and fuel efficiency, and affects the stealthiness of military vessels;whereas, in pumping equipment, noise generation is usually accompanied by energy loss and mechanical vibration, resulting in reduced efficiency and accelerated wear and tear of the equipment. Traditional physical experiments, theoretical modeling, and numerical simulation methods occupy a key position in hydrodynamic noise research, but each have their own limitations: physical experiments are limited by experimental conditions, which make it difficult to comprehensively reproduce the characteristics of the complex flow field;theoretical modeling appears to be simplified and idealized to cope with the multiscale noise mechanism;and numerical simulation methods, although accurate, are deficient in the sense that they are computationally expensive and difficult to adapt to complex boundary conditions. In recent years, intelligent algorithms represented by data-driven algorithms and heuristic algorithms have gradually emerged, showing great potential for development in hydrodynamic noise optimization applications. To this end, this paper systematically reviews progress in the application of intelligent algorithms in hydrodynamic noise research, focusing on their advantages in the optimal design of noise sources, noise prediction, and control strategy optimization. Meanwhile, this paper analyzes the problems of data scarcity, computational efficiency, and model interpretability faced in the current research, and looks forward to the possible improvements brought by hybrid methods, including physical information neural networks, in future researc

关键词： hydrodynamic noise numerical simulation intelligent algorithm energy efficiency optimization and control

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optimization and control of Composite Energy System Based on NSGA2 Algorithm 3

Optimization and Control of Composite Energy System Based on...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Gao, Chong Yang, Kaifan Lai, Jiayuan Xia, Zipeng Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Ltd Zhuhai519000 China

ISBN: (纸本)9798350341546

With the continuous development of economy and society, people have higher and higher requirements for quality of life. It also brings serious environmental problems, such as global warming and ozone layer destruction. To solve these problems, countries all over the world are actively exploring new technologies to achieve sustainable development. Among them, distributed power generation has attracted much attention as a new type of clean energy utilization. However, because of the intermittent, fluctuating and non-dispatchable characteristics of distributed power sources, their grid connection will bring certain impact on the safe and stable operation of the power system. Therefore, how to effectively improve the access capacity and power quality of distributed power sources has become an urgent problem to be solved. In this paper, we use the improved NSGA-II algorithm to solve the problem by establishing a mathematical model to improve the performance of the composite energy system. © 2023 IEEE.

关键词： Compound energy system NSGA2 algorithm optimization and control

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optimization and control of cyber-physical power systems under dual-network interactive cascading failure

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control ENGINEERING PRACTICE 2021年 111卷 104789-104789页

作者： Li, Xue Jiang, Cheng Du, Dajun Wang, Ruijie Fei, Minrui Li, Xiaohui Tian, Yu-Chu Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia

Due to deep integration between power grids and communication networks, cascading failure is spreading more readily in cyber-physical power systems (CPPS), even leading to blackouts. This paper mainly investigates optimized control under dual-network interactive cascading failure to support the operation of CPPS. Firstly, the topological and interactions characteristics between power grids and communication networks are analyzed, and a CPPS model is established based on the concepts of complex networks where the correlation of nodes is described by "degree to degree". Secondly, considering communication constraints including simultaneous bandwidth consumption and delay, an optimal routing policy with constraints based on publish-subscribe network (PSN) architecture is employed to reconstruct the transmission paths of corrupted data packets during cascading failure. Furthermore, a round trip time is defined to determine the received states of data packets and to reflect the impact of packets delay on cascading failure. Thirdly, according to direct current (DC) power flow and optimal routing policy with constraints, an optimization and control algorithm under the interactive cascading failure is proposed to provide optimized control strategy of CPPS. Finally, simulation results demonstrate the effectiveness of the proposed method in comparison with other approaches.

关键词： optimization and control Cyber-physical power systems Cascading failure Complex networks Routing policy

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A Real-Time optimization and control Platform for Analysis of Gas-Steam Combined Cycle Cogeneration System

A Real-Time Optimization and Control Platform for Analysis o...

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2024 International Conference on Guidance, Navigation and control

作者： Chen, Xi Wang, Chao Chen, Qingshan Wang, Yue Wang, Fei He, Houbo Inspur Grp Co Ltd Natl Key Lab Efficient Server & Storage Technol Jinan Peoples R China

ISBN: (纸本)9789819622061;9789819622047;9789819622030

Optimizing gas-steam combined cycle cogeneration (GSCCC) systems holds immense promise for energy conservation, yet it presents considerable complexity for researchers to address. In this study, we introduce an advanced real-time platform with a focus on GSCCC systems. Our data cleaning subsystem and parameter calculation subsystem are particularly adept at gathering critical realtime operational parameters essential for optimization. To ensure rapid response times for real-time optimization, we incorporate artificial neural network technology into the system optimization module. Additionally, we adopt a hybrid approach that combines the Harmony Search (HC) algorithm with the Genetic Algorithm (GA), aiming to continuously enhance optimization performance through iterative database updates. The efficiency of our platform is initially validated under usual conditions. Subsequently, it is deployed at a combined cycle cogeneration system to evaluate its practical application. The field test results underscore the platform's effectiveness, revealing a notable reduction of 0.415 g/kWh in the cogeneration system's standard coal consumption when utilizing the platform. These findings affirm the platform's practical viability and its potential to deliver significant energy efficiency improvements in real-world settings.

关键词： Cogeneration System optimization and control GA algorithm AI Platform

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Overview of the PI (2DoF) algorithm in wind power system optimization and control

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Desalegn, Belachew Tamrat, Bimrew Bahir Dar Univ Bahir Dar Inst Technol Bahir Dar Energy Ctr Bahir Dar Ethiopia Wolaita Sodo Univ Coll Nat & Computat Sci Dept Phys Wolaita Sodo Ethiopia Bahir Dar Univ Bahir Dar Inst Technol Fac Mech & Ind Engn Bahir Dar Ethiopia

Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production while reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, power management systems have become increasingly sophisticated nowadays. For addressing the analysis, scheduling, and control problems of future sustainable power systems, conventional model-based methods are completely inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in recent years, power system operators have come to rely on smart metering and advanced sensing devices for collecting more extensive data. This, in turn, facilitates the application of advanced machine learning algorithms, which can ultimately cause the generation of useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems (WECSs). In addition, an enhanced proportional integral (PI) (2DoF) algorithm is particularly introduced and implemented in a doubly fed induction generator (DFIG)-based WECS to enhance the reliability of power production. The main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in a MATLAB-Simulink environment.

关键词： wind power system doubly fed induction generator-based wind energy conversion system optimization and control advanced algorithms proportional integral (2DoF)

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optimization and control of battery-flywheel compound energy storage system during an electric vehicle braking

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ENERGY 2021年 226卷 120404-120404页

作者： Wang, Wei Li, Yan Shi, Man Song, Yuling Northwest A&F Univ Coll Mech & Elect Engn 22 Xinong Rd Yangling 712100 Shaanxi Peoples R China Minist Agr Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China

Combining the advantages of battery's high specific energy and flywheel system's high specific power, synthetically considering the effects of non-linear time-varying factors such as battery's state of charge (SOC), open circuit voltage (OCV) and heat loss as well as flywheel's rotating speed and its motor characteristic, the mathematical models of a battery-flywheel compound energy storage system are established. Taking the recovered braking energy of the system as an objective, an energy optimization method based on GA is proposed to obtain the optimal electric braking torque and current distribution factor under different working conditions, which realizes the current distribution between the battery and the flywheel as well as the allocation between the mechanical braking torque and the electric braking torque. Simultaneously, a double neural networks-based adaptive PI vector control method is proposed to regulate the rotating speed of the flywheel motor. Research results demonstrate that using the proposed methods the overall recovered energy increases by 1.17times and the maximum charging current of the battery decreases by 42.27% compared with a single battery system, and the stability and robustness of the flywheel system are significantly improved, which provides theoretical and technical references for making the energy management plan of electric vehicles. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Energy storage Battery-flywheel system optimization and control Energy recovery Electric vehicle

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A unified kinematics modeling, optimization and control of universal robots: from serial and parallel manipulators to walking, rolling and hybrid robots

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AUTONOMOUS ROBOTS 2020年第7期44卷 1233-1248页

作者： Tarokh, Mahmoud San Diego State Univ Dept Comp Sci San Diego CA 92182 USA

The paper develops a unified kinematics modeling, optimization and control that is applicable to a wide range of autonomous and non-autonomous robots. These include hybrid robots that combine two or more modes of operations, such as combination of walking and rolling, or rolling and manipulation, as well as parallel robots in various configurations. The equations of motion are derived in compact forms that embed an optimization criterion. These equations are used to obtain various useful forms of the robot kinematics such as recursive, body and limb-end kinematic forms. Using the modeling, actuation and control equations are derived that ensure traversing a desired path while maintaining balanced operations and tip-over avoidance. Various simulation results are provided for a hybrid rolling-walking robot, which demonstrate the capabilities and effectiveness of the developed methodologies.

关键词： Unified kinematics Hybrid robots optimization and control

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optimization and control of offshore wind farms with energy storage systems

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IFAC-PapersOnLine 2018年第18期51卷 862-867页

作者： Wang, Xiaonan Li, Lanyu Palazoglu, Ahmet El-Farra, Nael H. Shah, Nilay Department of Chemical and Biomolecular Engineering National University of Singapore 117585 Singapore Department of Chemical Engineering University of California DavisCA95616 United States Centre for Process Systems Engineering Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom

This paper studies the optimal control strategies of hybrid renewable energy systems, focusing on offshore wind farms with energy storage systems (ESS), considering challenges of economic costs, operational reliability, and environmental impacts. Wind energy is widely exploited as a promising renewable energy source worldwide. The development of offshore wind farms (OWF) is emerging to utilize wind energy on a large scale, but face more complicated operational conditions and higher costs. A systematic methodology is proposed to optimize the costs of an OWF with ESS based on a framework that extends from the supervisory level dispatch strategies to individual pitch control for load reduction and fatigue mitigation. This holistic approach is able to improve the efficiency and economic performance of a wind farm through overall system optimization, while explicitly operating each wind turbine using a formally designed control framework leading to an extension of their service lifespan. This work provides novel solutions of wind energy and storage components deployment and has strong application potential. © 2018

关键词： Offshore wind farms control engineering Economics Electric utilities Energy storage Environmental impact Offshore oil well production optimization Wind turbines Energy storage systems Hybrid renewable energy systems Individual pitch control Operational reliability Optimal control strategy optimization and control Renewable energy source Systematic methodology

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A Review on Soft Sensors for Monitoring, control, and optimization of Industrial Processes

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IEEE SENSORS JOURNAL 2021年第11期21卷 12868-12881页

作者： Jiang, Yuchen Yin, Shen Dong, Jingwei Kaynak, Okyay Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Tech Univ Munich Chair Automat Control Engn D-80333 Munich Germany Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Bogazici Univ Dept Elect & Elect Engn TR-34342 Istanbul Turkey Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

Over the past twenty years, numerous research outcomes have been published, related to the design and implementation of soft sensors. In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. However, novel opportunities are accompanied by novel challenges. This work is motivated by these observations and aims to present a comprehensive review of the developments since the start of the millennium. While a few books and review articles are published on the related topics, more focus on the most up-to-the-date advancement is put in this work, from the perspective of systems and control.

关键词： Soft sensor soft sensing process monitoring optimization and control industrial process

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Multiobjective optimization and Data-Driven Constraint Adaptive Predictive control for Efficient and Stable Operation of PEMFC System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第12期68卷 12418-12429页

作者： Li, Qi Yin, Liangzhen Yang, Hanqing Wang, Tianhong Qiu, Yibin Chen, Weirong Southwest Jiaotong Univ Dept Elect Engn Chengdu 611756 Peoples R China

Efficient and stable operation (ESO) of the proton exchange membrane fuel cell (PEMFC) system is challenging because the nonlinearity of system not only aggravates the difficulty of performance analysis but also puts forward higher requirements for controller design. In this article, a hierarchical performance enhancement control strategy (HPECS) is proposed, which can deal with optimal trajectory seeking under multiobjective constraints and nonlinear problem of system separately. In upper optimization level, the multiobjective optimization based on dynamic adjustment of weighting factor is proposed to seek optimal trajectory for ESO of PEMFC system considering the harm of oxygen starvation, oxygen saturation to system, and contradiction characteristic between net power and efficiency;in lower control level, a data-driven constraint adaptive predictive control is proposed for optimal trajectory tracking with the nonlinearity of system. The proposed HPECS is implemented and validated on the experimental PEMFC system. The comparative studies show that the proposed HPECS is superior in performance enhancement, disturbance rejection, and tracking ability. The normalized multiobjective optimization function can be basically guaranteed to above 0.9, and a nearly 7.2% enhancement in average can be also observed, which shows that ESO of PEMFC system can be achieved in changeable working conditions under proposed HPECS.

关键词： optimization Valves Vehicle dynamics Predictive control Oxygen Hydrogen Fuel cells Experimental implementation fuel cell optimization and control performance enhancement

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