This paper outlines a new procedure for computer modeling and optimum design for the dynamic mechanical and electrical study of a high-speed backplane connector, which is a key electrical interconnection technology in...
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This paper outlines a new procedure for computer modeling and optimum design for the dynamic mechanical and electrical study of a high-speed backplane connector, which is a key electrical interconnection technology in large communications equipment, ultra-high performance servers, supercomputers, industrial computers, high-end storage devices, and so on. The optimum structure design of contact pairs is important for a backplane connector in meeting multiple challenges in terms of minimizing the maximum insertion force and the contact resistance. Current optimization schemes, such as the quadrature method, are relatively complex. Therefore, we designed the connector contact pairs for simultaneously obtaining the proper insertion force and the contact resistance through a multi-objective particleswarmoptimization (MCDPSO) method with simpler settings and faster convergence speed. In this paper, the required insertion force was minimized during the entire process, and the minimum contact resistance was maintained after insertion. To this end, an MCDPSO algorithm was proposed for the connector design. A dynamic weight coefficient was developed to calculate the interval values of the reserved solutions for the selection of the operator, and an external archive update based on roulette wheel selection and gbest selection strategies was developed to increase the diversity of the solutions. A set of optimal structure solutions of the contact pairs was obtained for the subsequent design optimization. The feasibility and effectiveness of the proposed method were verified by comparing with the results from ANSYS finite element simulation.
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 t...
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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 optimizationalgorithms 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.
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 p...
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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 particleswarmalgorithm 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.
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 respons...
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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.
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 contr...
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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 particleswarmoptimization (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.
Gravity Search algorithm (GSA) is a swarm intelligence optimizationalgorithm based on the gravity law. The standard GSA algorithm has strong global search capability, while its convergence speed is slow. The particle...
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ISBN:
(纸本)9781728158556
Gravity Search algorithm (GSA) is a swarm intelligence optimizationalgorithm based on the gravity law. The standard GSA algorithm has strong global search capability, while its convergence speed is slow. The particleswarmoptimization (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.
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) ...
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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 particleswarmoptimization (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.
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 ...
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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), particleswarmoptimization (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.
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) o...
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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.
To remedy the defects of the single kernel function and PSO algorithm, a novel rolling force prediction model is proposed, combining particleswarmoptimization (PSO) algorithm, beetle antennae search (BAS) algorithm ...
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To remedy the defects of the single kernel function and PSO algorithm, a novel rolling force prediction model is proposed, combining particleswarmoptimization (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.
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