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optimization of selective withdrawal systems in hydropower reservoir considering water quality and quantity aspects

EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷

作者： Saadatpour, Motahareh Javaheri, Shima Afshar, Abbas Solis, Samuel Sandoval Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran Univ Lethbridge Environm Engn Lethbridge AB Canada Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA

The selective withdrawal system (SWS) has often been used to alleviate downstream temperature pollutions and in-reservoir unacceptable oxygen concentrations whilst increasing hydropower energy generation. The aim of this study is to a) design the optimal locations of intakes in SWS, and, b) derive optimal parameterized selective withdrawal operation rules. The integration of water quality and quantity aspects to meet long-term environmental services and economic benefits is challenging. The main challenge lies in coupling search-based optimization algorithms with a numerical hydrodynamic and water quality simulation model (i.e., CE-QUAL-W2). To cope with the computational burdens of CE-QUAL-W2, surrogate models have been developed to represent the dynamics of temperature and water quality according to various SWSs. The surrogate models and the hydropower energy computation module were coupled with a multi-objective particle swarm optimization algorithm in an adaptive surrogate-based simulation-optimization framework (ASBSOF). The ASBSOF is applied in Karkheh reservoir, Andimeshk, Khuzestan, Iran, to address problems a and b. The results indicate that the optimal SWSs provide the advantage of sustainable management to balance the energy generation whilst minimizing environmental damages.

关键词： Adaptive surrogate based simulation-optimization CE-QUAL-W2 Karkheh reservoir Surrogate model Multi-objective particle swarm optimization algorithm

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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 113卷

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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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

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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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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Advanced Applications Using Bio-Inspired optimization algorithms for Adaptive Digital Filters Designed with FIR and IIR Lattice-Ladder Structures 52

Advanced Applications Using Bio-Inspired Optimization Algori...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hussain, M. Jenkins, W. K. Radhakrishnan, C. Penn State Univ Sch Elect & Comp Engn University Pk PA 16802 USA Univ Illinois Dept Elec & Comp Engr Urbana IL 61801 USA

ISBN: (纸本)9781728133201

Recently published results demonstrated that the Levy Flight Firefly algorithm (LFFA) can be effectively applied to IIR adaptive filter structures designed with parallel or cascade second order sections. Recently published results also demonstrated that the LFFA can be effectively used for IIR adaptive filters designed with second order coupled form sections. This paper now proposes that bio-inspired optimization algorithms can be applied to FIR and IIR adaptive lattice-ladder structures, which have been well known throughout the literature for many past decades. Experimental results are presented to demonstrate that the LFFA performs effectively on several different types of adaptive lattice-ladder structures.

关键词： IIR adaptive filters bio-inspired optimization L'evy Flight Firefly algorithm particle swarm optimization algorithm lattice-ladder filters

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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

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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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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particle swarm optimization method for small retinal vessels detection on multiresolution fundus images

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JOURNAL OF BIOMEDICAL OPTICS 2018年第5期23卷 1-13页

作者： Khomri, Bilal Christodoulidis, Argyrios Djerou, Leila Babahenini, Mohamed Chaouki Cheriet, Farida Univ Biskra LESIA Lab Biskra Algeria Polytech Montreal Lab LIV4D Montreal PQ Canada

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high-and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two lowr-esolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： retinal blood vessel segmentation fundus imaging multiscale line detection multiobjective optimization particle swarm optimization algorithm image segmentation

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The Influence of the Solar Flywheel Energy Storage Control System Based on the Improved Disturbance Observation Method on the Improvement of New Energy Efficiency 5

The Influence of the Solar Flywheel Energy Storage Control S...

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5th International Conference on Environmental Science and Material Application (ESMA)

作者： Zhu, Leyu North China Elect Power Univ Baoding Baoding Hebei Peoples R China

The purpose of this study was to improve the utilization efficiency of solar energy, and accelerate the innovation and development of solar flywheel energy storage control system, so as to enhance the utilization value of new energy. Firstly, the definition and working method of flywheel energy storage control system were described, and the characteristics of flywheel energy storage control system were introduced in detail. Secondly, the concept and working principle of solar flywheel energy storage control system were introduced, and the principle and calculation method of perturbation and observation method (P&O) were described in detail. Finally, the improved P&O based on particle swarm optimization (PSO) algorithm was described in detail, and its principle and operation methods were explained. The research results show that the biggest characteristic of flywheel energy storage device is that it has no pollution to the environment, the energy stored per unit area is large, and the efficiency of converting it into electric energy is very high, which can be used for a long time. The working property of solar energy storage system is the introduction, maintenance, and output of electric energy. In order to obtain the best tracking data, the flywheel energy storage control system must control the disturbance amplitude and time accurately and make a comprehensive choice. The P&O improved by PSO algorithm is applied to the flywheel energy storage control system, and the experience time to reach the maximum efficiency position is relatively shortened. When the system's derived power reaches the balance, the variation of wave shape in the improved dynamic observation method is also relatively reduced.

关键词： Solar flywheel energy storage control system particle swarm optimization algorithm disturbance observation method new energy

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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

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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