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Application of IPSO-MCKD-IVMD-CAF in the compound fault diagnosis of rolling bearing

MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第3期34卷 035113-035113页

作者： Zhou, Jun Wu, Shi-Shuai Liu, Tao Wu, Xing Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Peoples R China

The vibration signals of rolling bearings are mixed with a variety of noises and a variety of faults which are coupled to each other, and which make the fault frequencies interfere with each other. This in turn leads to difficulty of accurate extraction of the features of composite faults. Therefore, it is necessary not only to suppress the noise of vibration signals but also to separate and extract the features of compound faults. A compound fault feature extraction method based on improved particle swarm optimization (IPSO) algorithm optimized maximum correlation kurtosis deconvolution (MCKD), improved variational mode decomposition (IVMD) and cyclic autocorrelation function (CAF), named IPSO-MCKD-IVMD-CAF is proposed. Firstly, the parameters of MCKD are selected adaptively by the IPSO, and the original signal is pre-processed by the parameterized MCKD. Secondly, the IVMD is utilized to decompose the signal, several intrinsic mode functions (IMFs) are obtained, the kurtosis and correlation coefficients of each IMF component are calculated. Finally, appropriate IMF components are selected by kurtosis and correlation coefficient to be superimposed into the reconstructed signals, and the reconstructed signals are analyzed by CAF. The proposed method has been successfully applied in simulation and measured vibration signal analysis, and the features of compound faults can be separated and extracted with high accuracy. The IPSO algorithm shortens the optimization time, and the IVMD solves the parameter selection problem. The analysis results of simulation case and measured data show the advantages of the proposed method IPSO-MCKD-IVMD-CAF. Compared to existing methods, the proposed method not only increases the accuracy of compound fault feature extraction, but also has a very good performance on the separation and extraction of compound fault features. More importantly, the improved algorithm reduces the computational complexity.

关键词： maximum correlation kurtosis deconvolution improved particle swarm optimization algorithm improved variational modal decomposition cyclic autocorrelation function fault diagnosis

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Research on Two-Stage Energy Storage optimization Configurations of Rural Distributed Photovoltaic Clusters Considering the Local Consumption of New Energy

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ENERGIES 2024年第24期17卷 6272-6272页

作者： Liu, Yang Liu, Dawei Kang, Keyi Wang, Guanqing Rong, Yanzhao Wang, Weijun Liu, Siyu State Grid Inner Mongolia Eastern Elect Power Co 11 Ordos East St Hohhot 010020 Peoples R China North China Elect Power Univ Sch Econ & Management 2 Beinong Rd Beijing 100006 Peoples R China China Railway Construct Engn Grp First Construct C Room 120212th Floor201 Bldg 42nd 13th Floor1 Y Beijing 100071 Peoples R China North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071003 Peoples R China

As photovoltaic technologies are being promoted throughout the country, the widespread installation of distributed photovoltaic systems in rural areas in rural regions compromises the safety and stability of the distribution network. Distributed photovoltaic clusters can be configured with energy storage to increase photovoltaic local consumption and mitigate the impact of grid-connected photovoltaic modes. Against this background, this paper focuses on rural areas, combines typical operation modes of distributed photovoltaic clusters, and constructs the two-stage energy storage optimization configuration model for rural distributed photovoltaic clusters. Taking a Chinese village as an example, the proposed model is optimized with an improved particle swarm optimization algorithm. Given different combinations with and without energy storage and demand response, comparative analyses are conducted on photovoltaic local consumption and the economic benefits of independent operators in various scenarios. Simulations indicate that the photovoltaic local consumption proportion of distributed photovoltaic clusters with energy storage reaches 62.64%, which is 34.02% more than the scenario without energy storage. The results indicate that configuring energy storage for rural distributed photovoltaic clusters significantly improves the photovoltaic local consumption level. Meanwhile, implementing demand response can achieve the same photovoltaic local consumption effect while reducing the energy storage configuration, and the life-cycle economic benefits are appreciable. The simulation results show that participating in demand response can reduce the energy storage system cost by 7.15% at a photovoltaic local consumption proportion of 60%. This research expands application channels of rural distributed photovoltaic clusters and provides references for investment and operation decisions of distributed photovoltaic energy storage systems.

关键词： distributed photovoltaic cluster energy storage optimization configuration demand response PV local consumption improved particle swarm optimization algorithm

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Joint Use of Scalar and Vector Magnetometers for Underwater Magnetic Target Localization

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1-1页

作者： Wang, Zhen Liu, Shengwei Guo, Yanguo Wu, Yongpeng Wang, Yi Zhao, Jing Jilin Univ Coll Instrumentat & Elect Engn Minist Educ Changchun 130061 Peoples R China Jilin Univ Key Lab Geophys Explorat Equipment Minist Educ Changchun 130061 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhanjiang Zhanjiang 524000 Peoples R China

Autonomous underwater vehicle (AUV) with towed magnetic anomaly detection (MAD) system is a reliable technique for detecting underwater magnetic targets. However, arranging too many magnetometers in the towed MAD system is not suitable, and the key lies in their reasonable arrangement. As a result, this letter proposes a new MAD system equipped with two scalar and one vector magnetometers, along with the corresponding multiparameter magnetic target localization method. By using two scalar magnetometers and the geometric relationship with vector magnetometer, the time when the vector magnetometer reaches the closest point approach (CPA) on the trajectory and the value range of the shortest distance are obtained. The value range of the shortest distance is cleverly set as the search boundary for the improved particle swarm optimization algorithm in this dimension, which greatly mitigates the randomness of the optimization algorithm and enhances the accuracy of the solution. Subsequently, the unique solution for the magnetic target parameters can be easily obtained by using the vector magnetometer. The combination of scalar and vector magnetometers proves to be both economical and effective. Numerical simulation and field experiments show that this method provides a new idea for static magnetic target detection.

关键词： improved particle swarm optimization algorithm magnetic anomaly detection (MAD) localization magnetic target multiparameter scalar magnetometer vector magnetometer

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Dynamic control method of flue gas heat transfer system in the waste heat recovery process

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ENERGY 2022年第0期259卷

作者： Ma, Hongqiang Xie, Yue Duan, Kerun Song, Xingpeng Ding, Ruixiang Hou, Caiqin East China Jiaotong Univ Sch Civil Engn & Architecture Nanchang 330013 Jiangxi Peoples R China Lanzhou Univ Technol Sch Civil Engn Lanzhou 730050 Peoples R China Shanxi Vocat Univ Engn Sci & Technol Sch Equipment Engn Taiyuan 030000 Shanxi Peoples R China

A new flue gas waste heat recovery system and its control strategy are proposed based on the intelligent control technology and the principle of phase-change heat transfer. It is composed of flue gas heat transfer system (FGHTS) and air heat transfer systems (AHTS). The system pressure and outlet flue gas temperature are adjusted by blower frequency in the AHTS and the concentration of lithium bromide-water in the FGHTS, respectively. For FGHTS, a dynamic control method is proposed based on improved particle swarm optimization (PSO). In this method, the optimization ability and the convergence of PSO algorithm are affected by inertia weight and fitness function. The inertia weight strategy with the inverted S-shaped function (IWS-ISF) can enhance the global search ability in the initial stage and the local search ability in the later stage compared with other inertia weight strategies. Meanwhile, the integral time absolute error (ITAE) fitness function is improved by introducing overshoot into ITAE, and the improved fitness function is defined as IITAE fitness function. The improved PSO algorithm based on IITAE fitness function can reduce the overshoot of FGHTS by 66.23%. This paper can provide a theoretical basis for improving the control performance of the new system.

关键词： Flue gas waste heat recovery system Dynamic control method IITAE fitness Function improved particle swarm optimization algorithm

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Modes decomposition forecasting approach for ultra-short-term wind speed

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APPLIED SOFT COMPUTING 2021年 105卷 107303-107303页

作者： Tian, Zhongda Shenyang Univ Technol Coll Artificial Intelligence Shenyang 110870 Peoples R China

The accurate forecasting of ultra-short-term wind speed is of great significance in theory and practice. This paper proposes a modes decomposition forecasting approach based on adaptive variational mode decomposition and weighted combination models for ultra-short-term wind speed. First, an adaptive variational mode decomposition algorithm is used for decomposing the original ultra-short-term wind speed time series into several modal components. Second, auto regressive integrated moving average, support vector machine and the improved long short-term memory are determined as forecasting models of different components by Hurst exponent analysis. Then, an improved particle swarm optimization algorithm is proposed to optimize the weight coefficient of each forecasting model. Finally, the final forecasted value is obtained by multiplying the forecasted value of each sub-forecasting model by their respective weight coefficient. Four groups of measured ultra-short-term wind speed data with 5-minute, 10-minute, 20-minute and 30-minute sampling periods are taken as the research object. Compared with other single or combination forecasting models, the proposed forecasting approach has higher prediction accuracy and more promising prediction performance for ultra-short-term wind speed. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Ultra-short-term wind speed Forecasting Variational mode decomposition Weighted combination model improved particle swarm optimization algorithm

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Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network

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ENERGIES 2022年第18期15卷 6734-6734页

作者： Wu, Xiaomei Jiang, Songjun Lai, Chun Sing Zhao, Zhuoli Lai, Loi Lei Guangdong Univ Technol Sch Automat Dept Elect Engn Guangzhou 510006 Peoples R China Brunel Univ London Brunel Interdisciplinary Power Syst Res Ctr Dept Elect & Elect Engn London UB8 3PH England

A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural network, and the input data are further studied for the implied features by convolutional neural network (CNN), which should be passed into the long and short-term memory neural network (LSTM) as input for prediction. At the same time, the improved particle swarm optimization algorithm (IPSO) is adopted to optimize the parameters of each prediction model. By superimposing each predicted sub-sequence, the predicting wind power could be obtained. Simulations based on a short-term power prediction in different months with huge weather differences is carried out for a wind farm in Guangdong, China. The simulated results validate that the proposed model has a high prediction accuracy and generalization ability.

关键词： short-term wind power prediction data decomposition combined deep neural network improved particle swarm optimization algorithm optimal parameter

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A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

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ENERGIES 2019年第7期12卷 1347页

作者： Zhao, Wenting Zhao, Juanjuan Yao, Xilong Jin, Zhixin Wang, Pan Taiyuan Univ Technol Sch Econ & Management Jinzhong 030600 Peoples R China Taiyuan Univ Technol Sch Informat & Comp Jinzhong 030600 Peoples R China Shanxi Coking Coal Grp Co Ltd Taiyuan 030024 Shanxi Peoples R China

Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China's primary energy demand and energy structure.

关键词： forecasting primary energy demand ensemble model improved particle swarm optimization algorithm

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The Intelligent Task Scheduling algorithm in Cloud Computing with Multistage optimization

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第4期9卷 313-323页

作者： He, XiaoLi Song, Yu Binsack, Ralf Volker Sichuan Univ Sci & Engn Sch Comp Sci Zigong 643000 Sichuan Peoples R China Sichuan Univ Sci & Engn Dept Network Informat Management Ctr Zigong 643000 Sichuan Peoples R China Winterstein 14 D-61194 Niddatal Germany

There're huge numbers of users and various tasks need to be handled in the cloud computing environment, the high effective task scheduling algorithm is one of the crucial problems that the cloud computing need to solve. Aiming to the model structure of cloud computing, in this article it introduces the particle swarm optimization algorithm (PSO) and Ant Colony optimization algorithm (ACO) to combine with optimized task scheduling algorithm. First it takes the particle swarm optimization algorithm to generate the initial scheduling results, and introduces the random inertia weight to improve the scheduling ability of the algorithm, then to take the generated results of improved particle swarm optimization algorithm as the initial pheromones of the ant colony algorithm to find out the optimal scheduling scheme, and use the elitist strategy and crossover operator in the genetic algorithm to improve the ant colony algorithm, among the algorithms to use multistage optimization algorithm to improve the operating efficiency. The experimental results show that under the same conditions, the total task completion time of improved algorithm has been reduced and its performance advantage are getting more obvious with the increased task measures.

关键词： Cloud computing Task scheduling improved ant colony algorithm improved particle swarm optimization algorithm Total task completion time

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A SVI Soft Sensor Model Based on improved PSO-Elman Neural Network 11

A SVI Soft Sensor Model Based on Improved PSO-Elman Neural N...

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11th World Congress on Intelligent Control and Automation

作者： Guo Min Geng Ya-nan Han Hong-gui Beijing Univ Technol Coll Elect & Control Engn Beijing Peoples R China

ISBN: (纸本)9781479958252

SVI, a sludge bulking index, is difficult to be obtained online. A soft sensor model of SVI based on improved PSO-Elman neural network is proposed in this paper. First, to solve the problems of nonlinear, hysteresis characteristics and so on of sludge bulking process, an Elman neural network with dynamic recursive properties is introduced to determine the model structure. Secondly, to improve the learning ability and convergent accuracy of the proposed SVI soft sensor model, an improved particle swarm algorithm is studied to optimize the connection weights of Elman neural network. Finally, the proposed SVI soft sensor model is applied to the actual process of wastewater treatment process. The simulation results show that the soft sensor model can predict the SVI values online, and owns better predicting accuracy.

关键词： SVI improved particle swarm optimization algorithm Elman neural network Soft senor

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Intelligent Modeling and optimization Method Based on Comprehensive Product Indices for Lead-Zinc Sintering Process

Intelligent Modeling and Optimization Method Based on Compre...

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27th Chinese Control Conference

作者： Cui Kai Cao Weihua Wu Min Wang Chunsheng Cent S Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China

ISBN: (纸本)9787900719706

The lead-zinc sintering process has the characters of complex mechanism, multi-parameter and strong interconnected coupling. This paper presents an intelligent modeling and optimization method based on comprehensive production Indices. First, the neural network prediction model of the comprehensive production indices is proposed, which is synthesizing some techniques, including gray correlation analysis, principal components analysis, and neural network and so on. And the target function is deduced and the model based on comprehensive production Indices is given by using the multi-objective optimization technique. At last, this paper proposes an improved multi-objective particle swarm optimization algorithm based on SPEA2 to calculate the optimization executing parameters. The applied results of actual runs show that the intelligent modeling and optimization method proposed in this paper attains a better industrial effect, and provides an effective and new way to implement the global optimization.

关键词： Lead-Zinc sintering process Neural network Multi-objective optimization improved particle swarm optimization algorithm

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