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A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression

JOURNAL OF ENERGY STORAGE 2023年 66卷

作者： Li, Jiabo Ye, Min Wang, Yan Wang, Qiao Wei, Meng Xian Shiyou Univ Mech Engn Coll Xian 710065 Peoples R China Changan Univ Engn Res Ctr Expressway Construct & Maintenance Eq Xian 710064 Peoples R China

The battery will inevitably deteriorate in its service life. Therefore, accurate prediction of the remaining useful life (RUL) is critical to the durability of the battery energy storage systems (ESS). The RUL prediction framework of Gaussian process regression(GPR) based on automatic stack autoencoder (SAE) and improved whale opti-mization algorithm (WOA) is proposed in this research paper. Firstly, the SAE and gray relation analysis are proposed to extract indirect health indicators (HIs) from battery degradation data. Secondly, the Gaussian process regression (GPR) approach combined with the WOA method to establish the RUL prediction framework. Besides, aiming at the disadvantage that WOA is prone to fall into local optimization, the WOA based on the sine cosine algorithm of decentralized foraging strategy is proposed to optimize the prediction ability of the GPR. Finally, compared with the prediction results of existing approaches under the same conditions, the simulation results indicate that the RUL prediction errors based on the proposed algorithm are controlled within 2 %, which has good advantages and prediction ability.

关键词： Remaining useful life Energy storage systems Gaussian process regression whale optimization algorithm Health indicators

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Damage detection of offshore jacket structures using structural vibration measurements: Application of a new hybrid machine learning method

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OCEAN ENGINEERING 2023年 288卷

作者： Leng, Jiaxuan Incecik, Atilla Wang, Mengmeng Feng, Shizhe Li, Yongbo Yang, Chunsheng Li, Zhixiong Ocean Univ China Dept Marine Technol Qingdao 266001 Peoples R China Univ Strathclyde Dept Naval Architecture Ocean & Marine Engn Glasgow G11XQ Scotland Hebei Univ Technol Sch Mech Engn Tianjin 300130 Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Guangzhou Univ Inst Artificial Intelligence Guangzhou 510006 Peoples R China Donghai Lab Zhoushan 316021 Peoples R China Opole Univ Technol Fac Mech Engn PL-45758 Opole Poland

The artificial intelligence (AI) technologies, such as meta-heuristic computing and deep learning, have provides solid technical support for structural health monitoring (SHM) of offshore jackets. In this paper, a physicsenhanced AI method based on the parametric damage identification is developed for SHM of the offshore jacket structures. In this new method, a hybrid kernel function-based kernel extreme learning machine (HKELM) is proposed to construct an AI structure to enhance the SHM detection capacity on the structural modal parameters extracted by the parametric damage identification technique. Simulation analysis is carried out to verify the feasibility of the HKELM-based method using the response signals of a jacket structure under impact force, and the result demonstrates good damage location capability of the proposed method. Furthermore, to select proper parameters for the HKELM, the whale optimization algorithm (WOA) is applied to optimize the values of the regularization coefficient and kernel parameter array. Then, the wavelet denoising (WD) is introduced to preprocess the vibration signals to improve the damage detection ability of the WOA-HKELM. Lastly, experimental tests are performed to validate the effectiveness of the proposed method in utilizing the structural modal parameters for identifying the structural damage. The analysis results illustrate that the proposed method produces satisfactory damage location ability under the influence of actual noise in the vibration signals. Meanwhile, this method has broad application prospects in the SHM of other offshore structures.

关键词： Damage location Hybrid kernel extreme learning machine whale optimization algorithm Offshore jacket structures

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Design of uniform field coils coupled with a magnetic shield cylinder?

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2023年 583卷

作者： Huang, Binyue Cao, Qian Chen, Zhengyu Wang, Yuhao Yue, Ziqian Zhai, Yueyang Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Shen Yuan Honors Coll Beijing 100191 Peoples R China

Active magnetic field coils and magnetic shields are widely employed to achieve uniform and low magnetic fields, and the coil generating magnetic field would be influenced as a result of the coupling with the shields. It turns out to increase the magnetic field strength and shrink the uniform area, which is detrimental to atomic devices. An analytical solution that takes into account the coupling effect of shields and coils is established by using the magnetic vector potential. Analyses for three different configurations are first performed, including mu-metals, 2K-MnZn ferrites and 10K-MnZn ferrites. Analytical calculations reveal that the optimal radii of the standard Lee-Whiting coils with high uniformity are all around 0.81 of shield radii. Additionally, adopting the whale optimization algorithm (WOA), a modified coil with improved uniformity and comparable size is proposed. Experimental results show that a better uniformity of 2.68e-5 and a 34% longer uniform area is obtained when compared with the standard coil. The accuracy of the internal magnetic field distributions is compared and tested by simulations and experiments. Situations utilizing shields and coils will be facilitated by this effective application of WOA in uniform field coil design.

关键词： Uniform magnetic field coil Magnetic shield Shield-coil coupling whale optimization algorithm Atomic magnetometer

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Optimal design of adaptive and proportional integral derivative controllers using a novel hybrid particle swarm optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第8期42卷 1492-1510页

作者： Bejarbaneh, Elham Yazdani Ahangarnejad, Arash Hosseinian Bagheri, Ahmad Bejarbaneh, Behnam Yazdani Binh Thai Pham Buyamin, Salinda Shirinzadeh, Fatemeh Univ Teknol Malaysia Fac Elect Engn Skudai 81310 Johor Malaysia Dept Mech Engn Blacksburg VA USA Univ Guilan Fac Mech Engn Dept Dynam Control & Vibrat Rasht Iran Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Skudai Johor Malaysia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ahrar Inst Technol & Higher Educ Dept Elect Engn Rasht Iran

Controlling of a rotational inverted pendulum is considered as a challenging problem, mainly due to the system's inherent nonlinear and unstable dynamics. In fact, the goal of this control is to maintain the pendulum vertically upward regardless of external disturbances. This paper aims to optimally design a model reference adaptive proportional integral derivative (PID) control for rotary inverted pendulum system based on a novel hybrid particle swarm optimization algorithm, combining sine cosine algorithm and levy flight distribution. Evaluation of the performance quality of the proposed adaptive controller is accomplished based on the stabilization and tracking control of rotary inverted pendulum system. In addition, two other PID controllers are designed to get a better understanding of the performance and robustness of the proposed controller. To make a complete comparison, the performance of the hybrid particle swarm optimization algorithm is examined against two other optimization techniques known as simple particle swarm optimization and whale optimization algorithm. Finally, the obtained simulation results demonstrate that the proposed optimal adaptive controller is superior to the other controllers, especially in terms of the transient response characteristics and the magnitude of control output signal.

关键词： Rotational inverted pendulum model reference adaptive PID control particle swarm optimization sine cosine algorithm levy flight whale optimization algorithm

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A WOA-Based optimization Approach for Task Scheduling in Cloud Computing Systems

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IEEE SYSTEMS JOURNAL 2020年第3期14卷 3117-3128页

作者： Chen, Xuan Cheng, Long Liu, Cong Liu, Qingzhi Liu, Jinwei Mao, Ying Murphy, John Zhejiang Ind Polytech Coll Design & Art Shaoxing 312000 Peoples R China Dublin City Univ Sch Comp Dublin D09 NA55 Ireland Shandong Univ Technol Sch Comp Sci & Technol Qingdao 266590 Peoples R China Wageningen Univ Informat Technol Grp NL-6708 PB Wageningen Netherlands Florida A&M Univ Dept Comp & Informat Sci Tallahassee FL 32307 USA Fordham Univ Dept Comp & Informat Sci New York NY 10458 USA Univ Coll Dublin Sch Comp Sci Dublin 4 Ireland

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this article, for the first time, we apply the latest metaheuristics whale optimization algorithm (WOA) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called Improved WOA for Cloud task scheduling (IWC) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks.

关键词： Cloud computing metaheuristics multiobjective optimization task scheduling whale optimization algorithm

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Detection of skin cancer with adaptive fuzzy classifier using improved whale optimization

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2020年第5期65卷 605-619页

作者： Durgarao, Nagayalanka Sudhavani, Ghanta Acharya Nagarjuna Univ Dept ECE Guntur Andhra Pradesh India RVR & JC Coll Engn Dept ECE Guntur Andhra Pradesh India

Skin cancer is considered as a well-known type of cancer globally, and its occurrence has been found to be raised in current days. Researchers state that the disease requires early prediction so that the identification of precise signs will make it simple for the dermatologists and clinicians. This disorder has been established to be unpredictable. Hence, this paper intends to develop an efficient skin cancer detection scheme, which classifies the nature of cancer, whether it is normal, benign or malignant. Accordingly, the skin image which is given as input is segmented using k-means clustering model and the features are extracted from segmented image using Local Vector Pattern (LVP). Moreover, the extracted features are subjected to fuzzy classifier for recognizing the cancer. In addition, the limits of membership functions are optimally selected by improved whale optimization algorithm (WOA). Thus, the proposed scheme is termed as Improved Selection of Encircling and Spiral updating position of WO-based Fuzzy Classifier (ISESW-FC). From the optimized output, the type of skin cancer image can be determined, whether it is normal, benign or malignant. The performance of proposed model is compared over other conventional methods, and its efficiency is proved by means of Type I and Type II measures.

关键词： k-means clustering local vector pattern skin cancer whale optimization algorithm

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Improved Particle Swarm optimization With Spiral Updating Mechanism for Multimodal Image Registration

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷

作者： Saxena, Sanjeev Pohit, Mausumi Amity Univ Dept Elect & Commun Engn Noida India Gautam Buddha Univ Dept Appl Phys Greater Noida Uttar Pradesh India

A modified particle swarm optimization (PSO) algorithm is proposed with a spiral updating mechanism (spiral-PSO) to optimize the balance between exploration and exploitation phases of the standard PSO. The spiral-PSO approach reduces the possibility of the local optima trapping issue during the optimization process. The proposed algorithm has been tested with 23 standard benchmark functions to compare the spiral-PSO with the standard PSO algorithm. The authors report the application of the spiral-PSO for registration of multimodal remote sensing images and compared the registration results with that of the standard PSO. The proposed approach performs successful image registration and provides better optimization of objective functions as compared to conventional PSO.

关键词： Image Registration Metaheuristic algorithms Particle Swarm optimization Swarm Intelligence whale optimization algorithm

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Multipoint design optimization for a controlled diffusion airfoil compressor cascade

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第11期234卷 2143-2159页

作者： Huang, Song Yang, Chengwu Han, Ge Zhao, Shengfeng Lu, Xingen Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine 11 Beisihuanxi Rd Beijing 100190 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Beijing Peoples R China

Under high altitude and low Reynolds number conditions, the aerodynamic performance of compressor cascades deteriorates drastically. In this paper, an optimally designed system combining class-shape-transformation method, S1 surface flow solver and whale optimization algorithm was established to achieve for a controlled diffusion airfoil, called MANGHH. The aim of this work is to improve our understanding of the loss mechanism for the original cascade and optimal cascade under different inflow conditions. The study shows that the total pressure loss of the optimal cascade at an angle of attack of -4 degrees, 0 degrees, and 6 degrees decreases by 55.9%, 16.1%, and 16.3%, respectively, compared with the original controlled diffusion airfoil. The range of the available low loss incidence improves significantly. At different incidences, the optimal cascade moves the blade loading forward compared with that of the original controlled diffusion airfoil while reducing the growth rate of the boundary layer thickness, eliminating a wide range of flow separations. The optimal cascade reduces the total pressure loss mainly by reducing trailing edge mixing loss compared with that of the original controlled diffusion airfoil. Under different inlet Mach number conditions, a laminar separation bubble appears on the suction surface of the original controlled diffusion airfoil. As the inlet Mach number increases, the position of the laminar separation bubble moves slightly upstream, while the length and depth of the laminar separation bubble increase. Fortunately, the total pressure loss of the optimal cascade decreases significantly compared with that of the original controlled diffusion airfoil. Under different incoming turbulence intensity conditions, the total pressure loss of the optimal cascade is always lower than that of the original controlled diffusion airfoil. As the incoming turbulence intensity increases, the total pressure loss of the original controlled diffusion airf

关键词： Controlled diffusion airfoil whale optimization algorithm class-shape-transformation method laminar separation bubble turbulence intensity

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whale optimization Controller for Load Frequency Control of a Two-Area Multi-source Deregulated Power System

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第1期22卷 122-137页

作者： Kumar, Rajiv Sharma, V. K. Bhagwant Univ Elect Engn Dept Ajmer 305004 Rajasthan India

This article discusses a whale optimization (WO) controller for design analysis, stability management, application, and performance analysis of a deregulated two-area multi-source energy system. Within the power grid, the Automatic Generation Control (AGC) maintains the power balance and strengthens the sudden frequency disturbance of the grid. Various researchers are targeted many kinds of fast-acting energy-storing devices to dampen space frequency oscillations and diversion of the tie line because of unexpected load variation. To regulate the power flow between multi-areas, load frequency control (LFC) is used by keeping frequency constant. But, to design a controller for minimizing the issues of LFC remains a challenge within the deregulated atmosphere. The main objective of the proposed methodology is to design a controller for minimizing the issues in LFC within the deregulated atmosphere. A two-area deregulated hydrothermal power system is employed to analyze many LFC problems in the deregulated power grid. During this modeling, the Thyristor-Controlled Phase Shifters (TCPS) and Capacitive Energy Storage unit (CES) are placed in the tie line and its control space. To mitigate the various issues of LFC in a two-area deregulated hydrothermal power system, the CES-TCPS combination is placed between the two control areas and the transient performance of constant is evaluated. The WO controller will increase the system power size to focus on the better performance of low-frequency management tasks. In addition, the effectiveness of the WO algorithm in tuning the proportional integral controller is also compared in a different contract (unilateral, bilateral and contract violation cases) scenarios of the proposed deregulated power system. Finally, the simulation is carried out in MATLAB/Simulink platform and the results obtained for the WO controller is compared with the normal and fuzzy PI controllers.

关键词： Automatic generation control Capacitive energy storage Deregulation Load frequency control Multi-source power system Sine-cosine algorithm whale optimization algorithm

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Machine learning-based single-phase ground fault identification strategy for AC-DC transmission lines

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 223卷

作者： Wu, Shuo He, Baina Meng, Fantao Liu, Yang Lin, Xin Dai, Weihan Wei, Yuanlong Wang, Shuo Zhang, Dongjin Shandong Univ Technol Coll Elect & Elect Engn Zibo 255000 Peoples R China Shenyang Univ Technol Key Lab Special Machine & High Voltage Apparat Minist Educ Shenyang 110870 Peoples R China

In this paper, an improved single-phase grounding fault (SPGF) nature identification strategy based on machine learning is proposed. To solve the problem of conventional single-phase automatic reclosure cannot distinguishing the nature of SPGF in AC-DC hybrid lines, the fault phase voltage characteristics of AC lines with DC feed are analyzed, and the voltage harmonic energy feature vector is extracted. The whale optimization algorithm (WOA) designed by orthogonal experiment design (OED) is used to select the model parameters of the least square support vector machine (LSSVM), and a discriminative model for the fault properties of the hybrid transmission line is obtained. The simulation experiments based on PSCAD/EMTDC 4.5.0 platform are conducted for model training and fault identification performance testing, and the results verifies the effectiveness and feasibility of the proposed method. Compared with LSSVM, the fault identification strategy based on OED-WOALSSVM has a higher accuracy in fault identification. In addition, the accuracy of the model identification strategy under different fault locations, parallel compensation degree, transition resistance, and power system noise effects is also verified.

关键词： Single-phase grounding fault Identification strategy Fault phase voltage Harmonic energy whale optimization algorithm Least squares support vector machine

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