This study presents a single-stage grid-tied three-level neutral point clamped photovoltaic inverter with a centralised model-predictive decoupled active-reactive power control. The proposed centralised model predicti...
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This study presents a single-stage grid-tied three-level neutral point clamped photovoltaic inverter with a centralised model-predictive decoupled active-reactive power control. The proposed centralised model predictive control (CMPC) incorporates the constraints of maximum power extraction, dc-link capacitor voltage balancing and active-reactive power tracking in a single objective function. The dc-link voltage of the inverter is regulated to its reference for extracting the maximum power. In order to eliminate the impact of reactive power exchange on floating dc-link voltage regulation, a decoupled active-reactive power control is used in the CMPC. Furthermore, a preference selective index-based dynamic weighting factor selection approach is introduced to maintain the relative importance between the power tracking and dc-link capacitor voltage balancing. The proposed control approach eliminates the outer dc-link voltage control loop and also, the empirical approach required for the selection of weighting factors. As a result, it ensures an optimal control action in each sampling period to improve the steady-state and dynamic tracking performance of the control objectives. The proposed control approach is experimentally verified by using a 1.2kW laboratory-scale prototype and the results are presented to demonstrate its effectiveness compared to the classical proportional-integral-based model predictive control.
In this paper a novel approach for making a statistical comparison of meta-heuristic stochastic optimization algorithms over multiple single-objective problems is introduced, where a new ranking scheme is proposed to ...
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In this paper a novel approach for making a statistical comparison of meta-heuristic stochastic optimization algorithms over multiple single-objective problems is introduced, where a new ranking scheme is proposed to obtain data for multiple problems. The main contribution of this approach is that the ranking scheme is based on the whole distribution, instead of using only one statistic to describe the distribution, such as average or median. Averages are sensitive to outliers (i.e., the poor runs of the stochastic optimization algorithms) and consequently medians are sometimes used. However, using the common approach with either averages or medians, the results can be affected by the ranking scheme that is used by some standard statistical tests. This happens when the differences between the averages or medians are in some 6-neighborhood and the algorithms obtain different ranks though they should be ranked equally given the small differences that exist between them. The experimental results obtained on Black-Box Benchmarking 2015, show that our approach gives more robust results compared to the common approach in cases when the results are affected by outliers or by a misleading ranking scheme. (C) 2017 Elsevier Inc. All rights reserved.
Small cell aims at improving the cell coverage and capacity of macrocell. Network operators investigate cell planning for improving system performance and for satisfying user requirements with minimal construction cos...
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Small cell aims at improving the cell coverage and capacity of macrocell. Network operators investigate cell planning for improving system performance and for satisfying user requirements with minimal construction cost and least unserved users. Most of the existing literature merely considered a single objective function or investigated cell planning for small-scale and homogeneous networks. This study aims to optimise multiple objectivefunctions of a large-scale and heterogeneous wireless network with multiple macrocells and multiple small cells. The authors formulate the multi-objective optimisation problem of cell planning for heterogeneous cellular networks. The three objects include construction cost, number of unserved users and network capacity. Then they propose the large-scale cell planning genetic algorithm (LSCPGA) to find a planning result with the lowest fitness value. Simulation results show that LSCPGA economises 9-20% construction cost, eliminates the number of unserved users from 4 to 10% and enhances 2-15% in network capacity compared to other planning algorithms.
This paper presents a Genetic Algorithm (GA) based method to determine the location and size of DG sources in distribution systems using single DG placement algorithm for determining the locations at first. Then, the ...
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ISBN:
(纸本)9781538609903
This paper presents a Genetic Algorithm (GA) based method to determine the location and size of DG sources in distribution systems using single DG placement algorithm for determining the locations at first. Then, the GA is utilized to determine the global sizes of DG sources which minimize single- or multi-objectivefunction related to these systems. The influence of active- and reactive-power injection on the sizing and placement of DG sources is investigated. The predictions of the proposed method as regards the sizing and placement of DG sources are compared with those obtained before using particle swarm optimization at steady weather conditions.
In the calibration of flood forecasting models, different objectivefunctions and their combinations could lead to different simulation results and affect the flood forecast accuracy. In this paper, the Xinanjiang mod...
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In the calibration of flood forecasting models, different objectivefunctions and their combinations could lead to different simulation results and affect the flood forecast accuracy. In this paper, the Xinanjiang model was chosen as the flood forecasting model and shuffled complex evolution (SCE-UA) algorithm was used to calibrate the model. The performance of different objectivefunctions and their combinations by using the aggregated distance measure in calibrating flood forecasting models was assessed and compared. And the impact of different thresholds of the peak flow in the objectivefunctions was discussed and assessed. Finally, a projection pursuit method was proposed to composite the four evaluation indexes to assess the performance of the flood forecasting model. The results showed that no single objective function could represent all the characteristics of the shape of the hydrograph simultaneously and significant trade-offs existed among different objectivefunctions. The results of different thresholds of peak flow indicated that larger thresholds of peak flow result in good performance of peak flow at the expense of bad simulation in other aspects of hydrograph. The evaluation results of the projection pursuit method verified that it can be a potential choice to synthesize the performance of the multiple evaluation indexes.
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