1,066 research outputs found

    Multivariate calibration of a water and energy balance model in the spectral domain

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    The objective of this paper is to explore the possibility of using multiple variables in the calibration of hydrologic models in the spectral domain. A simple water and energy balance model was used, combined with observations of the energy balance and the soil moisture profile. The correlation functions of the model outputs and the observations for the different variables have been calculated after the removal of the diurnal cycle of the energy balance variables. These were transformed to the frequency domain to obtain spectral density functions, which were combined in the calibration algorithm. It has been found that it is best to use the square root of the spectral densities in the parameter estimation. Under these conditions, spectral calibration performs almost equally as well as time domain calibration using least squares differences between observed and simulated time series. Incorporation of the spectral coefficients of the cross-correlation functions did not improve the results of the calibration. Calibration on the correlation functions in the time domain led to worse model performance. When the meteorological forcing and model calibration data are not overlapping in time, spectral calibration has been shown to lead to an acceptable model performance. Overall, the results in this paper suggest that, in case of data scarcity, multivariate spectral calibration can be an attractive tool to estimate model parameters

    Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control

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    We propose a two-stage, on-line signal control strategy for dynamic networks using a linear decision rule (LDR) approach and a distributionally robust optimization (DRO) technique. The first (off-line) stage formulates a LDR that maps real-time traffic data to optimal signal control policies. A DRO problem is solved to optimize the on-line performance of the LDR in the presence of uncertainties associated with the observed traffic states and ambiguity in their underlying distribution functions. We employ a data-driven calibration of the uncertainty set, which takes into account historical traffic data. The second (on-line) stage implements a very efficient linear decision rule whose performance is guaranteed by the off-line computation. We test the proposed signal control procedure in a simulation environment that is informed by actual traffic data obtained in Glasgow, and demonstrate its full potential in on-line operation and deployability on realistic networks, as well as its effectiveness in improving traffic

    Numerical estimation and experimental verification of optimal parameter identification based on modern optimization of a three phase induction motor

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    The parameters of electric machines play a substantial role in the control system which, in turn, has a great impact on machine performance. In this paper, a proposed optimal estimation method for the electrical parameters of induction motors is presented. The proposed method uses the particle swarm optimization (PSO) technique. Further, it also considers the influence of temperature on the stator resistance. A complete experimental setup was constructed to validate the proposed method. The estimated electrical parameters of a 3.8-hp induction motor are compared with the measured values. A heat run test was performed to compare the effect of temperature on the stator resistance based on the proposed estimation method and the experimental measurements at the same conditions. It is shown that acceptable accuracy between the simulated results and the experimental measurements has been achieved

    Parameter identification of induction motor

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    Numerous recent techniques of induction motor parameters calculating are hard to be done and expensive. Accurate calculations of the parameters of these motors would allow savings in different prospective like energy and cost. The major problem in calculating induction motor parameters is that it\u27s hard to measure output power precisely and without harm during the operation of the machines. It will be better to find other way to find out the output power with certain amount of inputs like input voltage and current.;Particle swarm optimization (PSO) and genetic algorithms (GAs) are often used to estimate quantities from limited information. They belong to a class of weak search procedures, that is, they do not provide the best solution, but one close to it. It is a randomized process in which follows the principles of evolution.;In this thesis genetic algorithm and partial swarm optimization are used to identify induction motor parameters. The inputs used to estimate electrical and mechanical parameters are measured stator voltages and currents. The estimated parameters compare well with the actual parameters. Data Acquisition (DAQ) is used to obtain these variable with the help of LABVIEW software. The induction motor used is a 7.5-hp with a constant frequency and in free acceleration. IEEE standard test of 7.5-hp induction motor is used to compare with performance of the simulated and measured data obtained. According to the output results, method of optimizing induction machine can be used in different models of induction motor
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