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Chinese Automation Congress (CAC)

作者： Yao, Yuan Gong, Xin Xin, Huang Zhao, Luping Song, Shengjun Liaoning Elect Power Co Ltd Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728176871

The steam turbine regulating system is an important link of a steam turbine generator unit The operating effect of the steam turbine is determined by the quality of control of the regulating system. In this paper, a particle swarm algorithm and fuzzy PID control are combined according to the regulation principle and characteristics of a steam turbine digital electro-hydranlic regulation system(DEM. Respective advantages of two control methods are taken to control the parameters in a loop and regulate the control quality of the system in real time according to the operation deviation change of the DEH control system. Finally, the system is better controlled.

关键词： DEH PID control particle swarm optimization algorithm fuzzy control

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Forecasting the seasonal natural gas consumption in the US using a gray model with dummy variables

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 108002-108002页

作者： Wang, Zheng-Xin He, Ling-Yang Zhao, Yu-Feng Zhejiang Univ Finance & Econ Sch Econ Hangzhou 310018 Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300350 Peoples R China

To forecast the seasonal fluctuations of US natural gas consumption accurately, a novel gray model based on seasonal dummy variables and its derived model are separately established. Then, the approximate time response formula in the proposed seasonal forecasting model is optimally calculated by using particle swarm optimization algorithm. On this basis, empirical analysis is conducted using data pertaining to natural gas consumption in the US during 2010-2019. The results show that gray model based on seasonal dummy variables and its derived model can recognize seasonal fluctuations in US natural gas consumption, whose prediction performances are much better than that of a traditional gray model, autoregressive integrated moving average (ARIMA), support vector regression (SVR), recurrent neural network (RNN), transformer, and fuzzy time series forecasting models. The mean absolute percentage errors (MAPEs) of the proposed seasonal gray forecasting model and its derived model, the classic gray model, ARIMA, SVR, RNN, Transformer, and Fuzzy time series forecasting models are 3.46%, 2.37%, 12.23%, 3.39%, 2.38%, 3.08%, 3.84%, and 5.02% in the training set, while those are 4.57%, 4.42%, 12.44%, 7.9%, 8.09%, 11.33%, 35.19% and 13.19% in the test set, respectively. The predicted and empirical results obtained by utilizing the proposed gray model implied that natural gas consumption in the US from 2020 to 2022 will maintain its seasonal growth and periodic changes, with the highest and the lowest values in the first and second quarters, respectively. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Gray model Seasonal dummy variables Natural gas consumption particle swarm optimization algorithm

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Fault Diagnosis Approach for Pneumatic Control Valves Based on Modified Expert System 19

Fault Diagnosis Approach for Pneumatic Control Valves Based ...

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19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Zhang, Daoming Hao, Jixing Chen, Liming Zhou, Linna China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781728197241

This paper studies the fault diagnosis method of pneumatic control valve. Firstly, the faults characteristics of pneumatic control valves are analyzed according to the operating principle and status of pneumatic control valves;secondly, the expert experience of the fault diagnosis of pneumatic control valves is summarized, which is verified according to the operating mechanism;thirdly, a fault diagnosis approach for pneumatic control valves based on modified expert system is proposed, by combining particle swarm optimization (PSO) algorithm with expert rules. Finally, the availability and advantages of the proposed approach is verified by the designed valve experimental system platform. The results show that compared with the basic expert system-based method, the modified method improves the accuracy and reduces the false negative rate effectively.

关键词： pneumatic control velve fault diagnosis expert system particle swarm optimization algorithm

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Adaptive Weighted Hybrid PSOGSA algorithm and its Application in Power System Economic Load Dispatch Problem 32

Adaptive Weighted Hybrid PSOGSA Algorithm and its Applicatio...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Pu, Xingcheng Zhao, Longlong Xiong, Chaowen Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China

ISBN: (纸本)9781728158556

Gravity Search algorithm (GSA) is a swarm intelligence optimization algorithm based on the gravity law. The standard GSA algorithm has strong global search capability, while its convergence speed is slow. The particle swarm optimization (PSO) algorithm has high convergence speed and search efficiency. Based on the advantages of the above two algorithms, a hybrid algorithm(PSOGSA) is proposed in this paper, and two adaptive weighted update strategies are introduced into the optimization process to improve the search accuracy of the hybrid algorithm. At the same time, we added variable mutation probability to solve the problem that particles are easily be trapped in local optimum. In order to verify the effectiveness of the two improved hybrid algorithms, the two algorithms are applied to the power system economic load dispatch (ELD) problem. Power generation cost optimization performance tests are computed for three groups of power systems with different unit numbers. The simulation results show that the two adaptive weighted hybrid algorithms which are proposed in this paper can effectively reduce the generation cost of the power system.

关键词： Adaptive Weighted Power System Economic Load Dispatch Problem particle swarm optimization algorithm Gravity Search algorithm

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Energy consumption prediction method based on LSSVM-PSO model for autonomous underwater gliders

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OCEAN ENGINEERING 2021年 230卷 108982-108982页

作者： Song, Yang Xie, Xudong Wang, Yanhui Yang, Shaoqiong Ma, Wei Wang, Peng Tianjin Univ Sch Mech Engn Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300350 Peoples R China Tianjin Univ Qingdao Inst Ocean Engn Qingdao 266237 Shandong Peoples R China Pilot Natl Lab Marine Sci & Technol Joint Lab Ocean Observing & Detect Qingdao 266237 Shandong Peoples R China

Currently, most autonomous underwater gliders (AUGs) operate on primary lithium batteries. As the state of charge of a primary lithium battery and the influence of marine environment on the glider are difficult to measure, it is hard to forecast the energy consumption of a glider accurately, which has caused the failure of many glider missions. For the purpose of safely deploying the AUG mission and effectively optimizing the motion parameters to increase the endurance, it is very important to make an accurate energy consumption prediction model of the AUG. In this paper, a novel model based on the least squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithm, namely the LSSVM-PSO model, is proposed to forecast the energy consumption of the AUG. Considering that the kernel function and the LSSVM related parameters have a great influence on the performance of the prediction model, several LSSVM models based on different kernel functions for energy consumption prediction are established, and the parameters are optimized by the PSO algorithm. The performance of LSSVM-PSO models with different kernel functions are compared based on the sea trial data. The results indicate that the LSSVM-PSO model with a radial basis kernel function has a higher accuracy than other models for energy consumption prediction. Moreover, the performance of the LSSVM-PSO model trained by different sample sizes and that of the conventional mathematical energy consumption prediction model are compared. The results demonstrate that the LSSVM-PSO model is superior with a large enough training sample size.

关键词： Autonomous underwater gliders Energy consumption prediction Least squares support vector machines particle swarm optimization algorithm

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Online Detecting of Inter-Turn Short-Circuit in Generator Rotor Winding Relying on ν-SVR Machine

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2021年第9期35卷 2150026-2150026页

作者： Pan, Feng Guo, Xiansheng Pan, Shengwang Chengdu Univ Sch Architecture & Civil Engn 2025 Chengluo Ave Chengdu 610106 Peoples R China Univ Elect Sci & Technol China Dept Informat & Commun Engn 2006 Xiyuan AveHi Tech Zone West Zone Chengdu 611731 Peoples R China

To probe an accurate diagnosing approach for synchronous generator (SG) with rotor winding inter-turn short-circuit, a novel online monitoring and detecting method relying on the nu-support vector regression (nu-SVR) machine was proposed, and its effectiveness was further verified by the micro-synchronous generator dynamic simulation. Terminal voltage, active and reactive power of SG were selected as input variables for a novel prediction model based on the nu-SVR, and field current was selected as an output variable of the prediction model. The structures and parameters of the field current prediction model were optimized with the particle swarm optimization (PSO) algorithm and training samples, then the prediction model was established and the field current prediction got under way. By comparing the predicted field current with the corresponding online measured field current, inter-turn short-circuit of rotor winding in SG could be detected sensitively once its absolute value of the prediction relative error exceeded a specific threshold. The micro-synchronous generator dynamic simulation indicated that the proposed online detecting approach based on the nu-SVR machine overcame the shortage of the back-propagation (BP) diagnosis method for misdiagnosis, and its accuracy, sensitivity and threshold setting range of the diagnosis method was the most prominent among these diagnosis methods such as the BP diagnosis method, the Bayesian regularization back-propagation (BRBP) diagnosis method and the epsilon-support vector regression (epsilon-SVR) diagnosis method.

关键词： Online diagnosis synchronous generator inter-turn short-circuit field current nu-support vector regression machine particle swarm optimization algorithm

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Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network

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ENERGY 2021年 227卷 120478-120478页

作者： Li, Jinchao Wu, Qianqian Tian, Yu Fan, Liguo North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071000 Peoples R China

The global trade scale of natural gas is expanding, and its price forecasting has become one of the most critical issues in the planning and operation of public utilities. In this paper, a hybrid forecasting model of monthly Henry Hub natural gas prices based on variational mode decomposition (VMD), particle swarm optimization (PSO) and deep belief network (DBN) is proposed. In addition, influencing factors of the long-term natural gas price variation are investigated and considered on the natural gas price forecasting. Empirical forecasting results validate that the newly proposed hybrid forecasting model has better forecasting performance than the traditional models. The results also show that natural gas consumption, natural gas gross withdrawals, monthly West Texas Intermediate (WTI) crude oil spot prices, the proportion of extreme high temperature weather, and the proportion of extreme low temperature weather all contribute to long-term Henry Hub natural gas spot prices forecasting to varying degrees. By comparing the accuracy of forecasting models with different combinations of influencing factors, it is found that the hybrid model with natural gas consumption and WTI crude oil spot prices has the best forecasting performance. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Natural gas prices forecasting Hybrid model Variational mode decomposition particle swarm optimization algorithm Deep belief network

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Photovoltaic Array Power Prediction Model Based on EEMD and PSO-KELM 47

Photovoltaic Array Power Prediction Model Based on EEMD and ...

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47th IEEE Photovoltaic Specialists Conference (PVSC)

作者： Ding, Kun Chen, Xiang Zhang, Jingwei Li Feng Hamelmann, Frank U. Weng, Shuai Hohai Univ Coll Mech & Elect Engn Changzhou Peoples R China Univ Appl Sci Bielefeld Solar Comp Lab Minden Germany Changzhou Key Lab Photovolta Syst Integrat & Prod Changzhou Peoples R China

ISBN: (纸本)9781728161150

In order to improve the accuracy of short-term photovoltaic (PV) power prediction, the PV array power prediction model based on ensemble empirical mode decomposition (EEMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization algorithm (PSO-KELM) is proposed in this paper. The EEMD algorithm is used to decompose the power sequence of the PV array into a series of intrinsic mode functions (IMFs) with different features, the IMFs with similar features are divided into new components by sample entropy (SE), and establishing the KELM power prediction model corresponding to the new component. The PSO is used to optimize the output weight of KELM, seeking the optimal solution of upper and lower limits, and improving the performance of KELM. The actual data is used to compare the proposed prediction model with the traditional back-propagation (BP) and extreme learning machine (ELM) prediction models. The results validate that the proposed model can predict power of PV array with good accuracy and calculation speed.

关键词： short-term PV power prediction ensemble empirical mode decomposition sample entropy particle swarm optimization algorithm kernel extreme learning machine

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A Novel Data-Driven Rolling Force Predictive Model Based on PSO-BAS-HKSVR Approach

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电脑学刊 2021年第4期32卷 25-41页

作者： Ming-Hua Liu Qiang Zhang Ying-Hua Liu Wen-Li Wang

To remedy the defects of the single kernel function and PSO algorithm, a novel rolling force prediction model is proposed, combining particle swarm optimization (PSO) algorithm, beetle antennae search (BAS) algorithm and hybrid kernel function support vector regression (HKSVR), i.e., PSO-BAS-HKSVR model. Hybrid kernel function (HKF) is incorporated to reduce the defect of the single kernel function of support vector regression. In the meantime, PSO algorithm is improved and combined with BAS algorithm to optimize the HKSVR model parameters (C , g ,d, ε ,m) Statistical indicators (R^2, RMSE, MAE and MAPE) are introduced to assess the comprehensive property of the model. The experimental data of the training and testing model originate from the actual production line of the steel plant. Rolling temperature, thickness reduction, initial strip thickness and width, front tension, back tension, roll diameter, and rolling speed are taken as the input variables. Under the identical experimental conditions, compared with the single SVR, PSO-SVR, PSO-HKSVR, BPNN, GRNN and RBF models, PSO-BAS-HKSVR model exhibits the highest prediction accuracy and the optimal generalization ability. As indicated from the results PSO-BAS-HKSVR method is suited for the rolling force prediction and the optimization of model parameters in the hot strip rolling process.

关键词： beetle antennae search algorithm hybrid kernel function particle swarm optimization algorithm rolling force support vector regression

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optimization of inbound vehicle routes in the collection of bio-medical wastes

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Materials Today: Proceedings 2021年 45卷 692-699页

作者： U. Mohamed Faizal R. Jayachitra P. Vijayakumar M. Rajasekar Department of Mechanical Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore 641062 India Department of Mechanical Engineering PSG College of Technology Coimbatore 641062 India

In Multi-specialty hospitals, the quantity of accumulation of Bio-Medical Waste (BMW) is enormous when compared to clinics. The safe and timely disposal of BMW is very essential to avoid harmful effects to humans and environment. In this article, the inbound logistics involved in the collection of Bio-Medical Waste at a Private Multi-Specialty Hospital located in Coimbatore which contains 59 wards has been improved to avoid time delay. An optimized vehicle routing model has been framed for a set of 6 dedicated vehicles with the objective to minimize the time taken during the collection of BMW. For this purpose a mathematical model is generated and solved using particle swarm optimization algorithm (PSO). The results infer that, by following the optimized vehicle routes, the time delay is totally eliminated and in addition the time taken for collecting the BMW is reduced by 42%, i.e. from 6 h to 3 h 46 min.

关键词： Vehicle routing Bio-medical waste Time delay Inbound logistics particle swarm optimization algorithm

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