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Assessment of MV XLPE cable aging state based on PSO-XGBoost algorithm

ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期221卷

作者： Pan, Qiaosheng Zhang, Chi Wei, Xinyuan Wan, Aode Wei, Zeping Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Anhui Peoples R China Sichuan Changhong Elect Co Ltd Mianyang 621000 Sichuan Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China

Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extreme gradient boosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models.

关键词： Cable aging status assessment Extreme gradient boosting algorithm particle swarm optimization algorithm

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Multi-model fusion stacking ensemble learning method for the prediction of berberine by FT-NIR spectroscopy

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INFRARED PHYSICS & TECHNOLOGY 2024年 137卷

作者： Li, Xiaoyu Chen, Huazhou Xu, Lili Mo, Qiushuang Du, Xinrong Tang, Guoqiang Guilin Univ Technol Sch Math & Stat Guilin 541004 Peoples R China Guilin Univ Technol Ctr Data Anal & Algorithm Technol Guilin 541004 Peoples R China Beibu Gulf Univ Coll Marine Sci Qinzhou 535011 Peoples R China Guilin Univ Technol Sch Math & Stat 12 Jiangan Rd Guilin 541004 Peoples R China

Rhizoma Coptidis is a Chinese herbal medicine with antibacterial and anti-inflammatory properties. It has extensive applications in modern medicine. The content of berberine in Rhizoma Coptidis directly determines its quality. Fourier transforms near-infrared (FT-NIR) spectroscopy is a commonly used non-destructive method for rapidly detecting berberine content. In contrast to single-supervised learning algorithms in machine learning, ensemble learning combines individual learning algorithms to create a stable and better-performing strong-supervised model. This study collected spectral data of Rhizoma Coptidis using FT-NIR spectroscopy technology and established a chemometric model using a stacking ensemble approach with multiple models. Partial Least Squares (PLS), Adaptive Boosting (AdaBoost), Gradient boosting decision trees (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) regression models were chosen as alternative base models, different Stacking models were established by random combinations. To fully leverage the strengths of each model and enhance predictive capability, an adaptive inertia weight particle swarm optimization algorithm (AWPSO) was used to search for the optimal parameters. The correlation coefficient of the test (RT) and the root mean square error of the test (RMSET) systematically evaluated the model performance. Finally, AWPSO-RF, AWPSOXGBoost, and AWPSO-AdaBoost were selected as the base models. The RMSET and RT for RF, XGBoost, and AdaBoost were 0.226, 0.250, 0.195, and 0.871, 0.830, 0.927. After optimizing with the AWPSO algorithm, the RMSET and RT for AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were 0.226, 0.245, 0.194, and 0.871, 0.843, 0.922, respectively. The RMSET and RT values for the stacking ensemble were 0.174 and 0.932. The prediction accuracy and generalization ability of multi -model fusion stacking ensemble learning are superior to the single -model regression methods. Therefore, the stacking ensemble learn

关键词： FT-NIR spectroscopy Berberine Stacking ensemble learning particle swarm optimization algorithm Adaptive inertia weight

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A non-dominated sorting based evolutionary algorithm for many-objective optimization problems

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SCIENTIA IRANICA 2021年第6期28卷 3293-3314页

作者： Mane, S. U. Rao, M. R. Narasinga Deemed Univ Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India

The optimization problems with more than three objectives are Many-objective optimization Problems (MaOPs) that exist in various scientific and engineering domains. The existing multi-objective evolutionary algorithms are not found effective in addressing the MaOPs. Its limitations initiated the need to develop an algorithm that efficiently solves MaOPs. The proposed work presents the design of the Many-Objective Hybrid Differential Evolution (MaOHDE) algorithm to address MaOPs. Initially, two multi-objective evolutionary algorithms viz. Non-dominated Sorting based Multi-Objective Differential Evolution (NS-MODE) and Non-dominated Sorting based Multi-Objective particle swarm optimization (NS-MOPSO) algorithms were designed. These algorithms were developed by incorporating the non-dominated sorting approach from Non-dominated Sorting-based Genetic algorithm II (NSGA-II), the ranking approach, weight vector, and reference points. Tchebycheff-a decomposition-based approach, was applied to decompose the MaOPs. The MaOHDE algorithm was developed by hybridizing the NS-MODE with the NS-MOPSO algorithm. The strength of the presented approach was determined using 20 instances of DTLZ functions, and its effectiveness and efficiency were verified upon its comparison with the recently developed state of algorithms existing in the literature. From the results, it is observed that the MaOHDE responds better than its competitors or is competitive for most of the test instances and the convergence rate is also good. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： Many-objective hybrid differential evolution algorithm Non-dominated sorting Decomposition-based approach Differential evolution algorithm particle swarm optimization algorithm Many-objective optimization problems

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Evaluating urban agglomeration resilience to disaster in the Yangtze Delta city group in China

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SUSTAINABLE CITIES AND SOCIETY 2022年 76卷 103464-103464页

作者： Lu, Hao Lu, Xin Jiao, Liudan Zhang, Yu Chongqing Jiaotong Univ Sch Econ & Management Chongqing Peoples R China

With the rapid acceleration of global urbanization, progressively more natural disasters and public safety problems are encountered in cities. Previous studies have shown that highly resilient cities can promptly and effectively respond to disasters. Therefore, in this study, a mixed-methods approach to urban agglomeration resilience estimation is proposed. First, the particle swarm optimization algorithm is used to optimize the back propagation neural network in order to evaluate the resilience of subsystems, including economy, society, environment, and science and technology resilience subsystems. Then, the entropy weight method is integrated to obtain the urban agglomeration resilience. Finally, the kernel density estimation and Moran's I are utilized for comprehensive analysis of the dynamic evolution and spatial correlation of the urban agglomeration resilience. The Yangtze River Delta cities are adopted as a case study, and the results indicated that the resilience in the most developed urban agglomeration in China showed the pyramidal spatial distribution. From the perspective of evolution, the resilience level is constantly improving, and the differences among cities are gradually decreasing from 2015 to 2019. The results indicate that the model is valuable for evaluating urban resilience, and thus it can help policymakers formulate proposals to effectively improve the resilience of urban agglomerations.

关键词： Urban resilience Urban agglomeration Resilience evaluation particle swarm optimization algorithm The Back Propagation Neural Network China

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Prior Recognition of Flash Floods: Concrete Optimal Neural Network Configuration Analysis for Multi-Resolution Sensing

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IEEE ACCESS 2020年 8卷 210006-210022页

作者： Khan, Talha Ahmed Alam, Muhammad Mansoor Shahid, Zeeshan Su'ud, Mazliham Mohd Univ Kuala Lumpur British Malaysian Inst Kuala Lumpur 53100 Malaysia Usman Inst Technol Karachi 75300 Pakistan Univ Kuala Lumpur Malaysia Inst Informat & Technol MIIT Kuala Lumpur 50250 Malaysia Inst Business Management Karachi 75190 Pakistan Univ Kuala Lumpur Malaysian France Inst MFI Kuala Lumpur 43650 Malaysia

Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forwa

关键词： Flash floods sensors multi-resolution sensing early forecasting system artificial intelligence modified multi-layer feed forward neural network particle swarm optimization algorithm multi-layer perceptron

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Dynamic space reduction optimization framework and its application in hull form optimization

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APPLIED OCEAN RESEARCH 2021年 114卷 1页

作者： Zheng, Qiang Feng, Bai-Wei Chang, Hai-Chao Liu, Zu-Yuan Wuhan Univ Technol Key Lab High Performance Ship Technol Minist Educ Wuhan Peoples R China Wuhan Univ Technol Sch Transportat Wuhan Peoples R China

Hull form optimization is a typical complex engineering problem. The complex design performance space often results in a low optimization efficiency as an optimal solution cannot be ensured. Presently, several methods, such as efficient optimization algorithms, approximate model technology, and high-performance computing are primarily used to reduce the calculation time. However, these methods cannot satisfy practical application requirements in terms of efficiency and solution accuracy. Thus, we investigated a dynamic space reduction optimization framework (DSROF), in this study, wherein data mining is continuously performed during the optimization process to dynamically reduce the range and number of variables. DSROF enables subsequent optimization only in the range that exhibits a high performance, thereby reducing redundant calculations, improving optimization efficiency, and ensuring a higher degree of accuracy. Furthermore, we applied DSROF to function examples and hull form optimization. The results indicate that the use of the DSROF can reduce the calculation cost in hull form optimization by 23% in comparison with that of the particle swarm optimization algorithm.

关键词： Hull form optimization Data mining Dynamic space reduction particle swarm optimization algorithm

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A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM

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ENERGY 2021年 234卷 121236-121236页

作者： Ren, Xiaoqing Liu, Shulin Yu, Xiaodong Dong, Xia Qilu Univ Technol Shandong Acad Sci Dept Elect Engn & Automat Jinan 250353 Peoples R China

State-of-charge (SOC) estimation of lithium-ion battery is one of the core functions of battery management system. In order to improve the estimation accuracy of SOC, this paper proposes a long shortterm memory neural network based on particle swarm optimization (PSO-LSTM). Firstly, the key parameters of LSTM are optimized by PSO algorithm, so that the data characteristics of lithium-ion battery can match the network topology. In addition, random noise is added to the input layer of PSO-LSTM neural network to improve the anti-interference ability of the network. Finally, experiments show that the proposed method can achieve accurate estimation under different conditions. The estimates based on PSO-LSTM converge to the real state-of-charge within an error of 0.5%. (c) 2021 Published by Elsevier Ltd.

关键词： Lithium-ion battery SOC estimation particle swarm optimization algorithm Long short-term memory neural network

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Optimal Multiuser MIMO Linear Precoding with LMMSE Receiver

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2009年第unknown期2009卷 197682-197682页

作者： Fang Shu Wu Gang Li Shao-Qian Univ Elect Sci & Technol China Natl Key Lab Commun Chengdu 611731 Peoples R China

The adoption of multiple antennas both at the transmitter and the receiver will explore additional spatial resources to provide substantial gain in system throughput with the spatial division multiple access (SDMA) technique. Optimal multiuser MIMO linear precoding is considered as a key issue in the area of multiuser MIMO research. The challenge in such multiuser system is designing the precoding vector to maximize the system capacity. An optimal multiuser MIMO linear precoding scheme with LMMSE detection based on particle swarm optimization is proposed in this paper. The proposed scheme aims to maximize the system capacity of multiuser MIMO system with linear precoding and linear detection. This paper explores a simplified function to solve the optimal problem. With the adoption of particle swarm optimization algorithm, the optimal linear precoding vector could be easily searched according to the simplified function. The proposed scheme provides significant performance improvement comparing to the multiuser MIMO linear precoding scheme based on channel block diagonalization method. Copyright (C) 2009 Fang Shu et al.

关键词： particle swarm optimization particle swarm optimization algorithm Minimum Mean Square Error MIMO Channel Linear Minimum Mean Square Error

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Prediction and evaluation of fuel properties of hydrochar from waste solid biomass: Machine learning algorithm based on proposed PSO-NN model

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FUEL 2022年 318卷 123644-123644页

作者： Mu, Lin Wang, Zhen Wu, Di Zhao, Liang Yin, Hongchao Dalian Univ Technol Sch Energy & Power Engn Dalian 116024 Peoples R China Minist Educ Key Lab Ocean Energy Utilizat & Energy Conservat Dalian 116024 Peoples R China

Hydrothermal carbonization is an effective and environmentally friendly biomass pretreatment technology, which converts high moisture biomass into homogeneous, carbon-rich, and high calorific value solid hydrochar. This study aimed to predict the fuel properties of the hydrochar based on hydrothermal conditions and biomass characteristics by machine learning (ML) models. Artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm was proposed and developed based on 296 data points collected from abundant previous studies, and the prediction capability is analyzed with ordinary ANN model. The results showed that particle swarm optimization-neural network (PSO-NN) model with optimal hyper-parameters can reduce iteration time, and improve the stability and accuracy of ANN model. Fuels properties of hydrochar were predicted by PSO-NN model with R-2 greater than 0.85 and the convergence speed is increased by 26.8%. Feature importance and correlation were explored by the integration of PSO-NN model and model explainer based on SHAP methodology. The result showed that the carbon content in raw biomass was the significant feature impacting mass yield, and the mass yield of hydrochar mainly depended on elemental composition of feedstock. The HTC temperature of water is the most important factor affecting HHV of the hydrochar, so raising hydro thermal temperature is the best way to improve the HHV. N content was considered as the most important parameter for the N/C molar ratio among all the evaluated features. The O content of the raw biomass had obvious influence on the ASH content of hydrochar, and the influence of operating conditions for ash content changes only accounted for 14.3%, which indicated that the removing efficiency of ash from biomass was low only by changing the operating conditions. Both DHD and DCD of hydrochars were most affected by temperature, and the ash content played a significant role in the prediction of the DHD. Furth

关键词： Hydrothermal carbonization particle swarm optimization algorithm Artificial neural network Single-task prediction Feature analysis

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Active power filter module function to improve power quality conditions using GWO and PSO techniques for solar photovoltaic arrays and battery energy storage systems

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JOURNAL OF ENERGY STORAGE 2023年 72卷

作者： Abou Houran, Mohamad Sabzevari, Kiomars Hassan, Alaaeldien Oubelaid, Adel Tostado-Veliz, Marcos Khosravi, Nima Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China Tech & Vocat Univ TVU Dept Elect Engn Tehran Iran Univ Bejaia Fac Technol Lab Technol Ind & Informat Bejaia 06000 Algeria Univ Jaen Dept Elect Engn Linares 23700 Spain R&D Management NPC Dept Elect & Instrumentat Engn Tehran Iran MOP Deputy Res & Dev Tehran Iran

This study aims to unbalanced power quality (PQ) conditions analysis of solar photovoltaic arrays and battery energy storage system (PV-BESS) integrated active power filter module (APFM). Here, the APFM's role is to mitigate the PQ issues that existed by the nonlinear loads. The standalone PV-APFM design is negligibly reliable approximated to a hybrid PV-BESS system because of its fluctuation and high environmental reliance. Further, here the research challenge is to optimise the APFM controller gain coefficients by the grey wolf optimization (GWO) algorithm and apply this technique to current and voltage harmonic loops (CHL-VHL) to achieve the best answer to enhance the unbalanced PQ conditions. The multi-objective functions (MOFs) to crack the problem include the total harmonic distortion (THD) of current and voltage components. The particle swarm optimization (PSO) approach will be considered a complementary method for validation and comparison with the outcomes obtained from the GWO procedure. Four case studies in imbalance conditions have been considered to confirm the proposed method in MATLAB-Simulink software.

关键词： Harmonic condition Total harmonic distortion Active power filter module Grey wolf optimization particle swarm optimization algorithm

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