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Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 190卷 106424-106424页

作者： Zhao, Long Zhao, Xinbo Zhou, Hanmi Wang, Xianlong Xing, Xuguang Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Shaanxi Peoples R China

Accurately estimating reference crop evapotranspiration (ETo) is crucial for water resource management and precise irrigation. The FAO-56 Penman-Monteith model has the highest accuracy in terms of ETo prediction, but its application is restricted by a lack of complete meteorological data. To develop a high accuracy prediction model for ETo with incomplete meteorological data, this study used four principal component analysis methods (i.e. path analysis, stepwise linear regression analysis, logistic regression analysis and factor analysis) to determine few factors that strongly affect ETo as the input to established the ETo model with machine learning (ML). The prediction model for ETo was established using support vector machine (SVM), gradient boosting decision tree (GBDT), particle swarm optimisation (PSO)- SVM, and PSO-GBDT algorithms. This study demonstrated that SVM, GBDT, PSO-SVM and PSO-GBDT models can maintain the highest accuracy by using the five most important affect factors extracted on the basis of path analysis (PA) as input, for various models, the precision ranges of NSE, R2, RMSE, MAE, and GPI were approximately 0.53-0.70, 0.63-0.84, 0.27-0.47, 0.27-0.69, and 0.72-1.01 respectively. The accuracy of SVM and GBDT significantly improved through optimisation using PSO. When inputting the top five factors of importance extracted by PA algorithm in each model, the SVM and GBDT model optimized by PSO algorithm increased the average value of GPI by 17.74% and 46.71% respectively. When inputting the top five factors extracted by the PA algorithm in PSO-GBDT, the average value of NSE, R2, RMSE and MAE is 22.37%, 2.83%, 9.05% and 6.84% higher than that of PSO-SVM, and the GPI is 8.30% higher than that of PSO-SVM. This study shows that the ETo prediction model constructed by the PSO-GBDT algorithm can obtain the highest accuracy when the top five factors extracted by the PA algorithm are used as input. This provides a reference for ETo estimation in Southwest C

关键词： Reference crop evapotranspiration Principal component analysis particle swarm optimisation algorithm Support vector machine Gradient boosting decision tree

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Backstepping fractional sliding mode voltage control of an islanded microgrid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第12期13卷 2464-2473页

作者： Delavari, Hadi Naderian, Sina Hamedan Univ Technol Dept Elect Engn Hamadan 65155 Iran

Distribution networks include thousands of sensors and a large number of controllable devices such as flexible loads, batteries and distributed generators with an arbitrary configuration that can be parametrically uncertain or topologically unknown. In this study, a new robust non-linear voltage-control strategy based on backstepping and fractional-order (FO) sliding mode control (SMC) is presented for an islanded microgrid (MG). Then for the problem of determining the controller parameters, particle swarm optimisation algorithm is used. The primary objective in this study is to improve robust performance and disturbance rejection of the closed-loop control system. A FO backstepping SMC is proposed in this study. The proposed controller robustly regulates the MG voltages in the presence of parametric uncertainties, unmodelled dynamics, imbalanced and non-linear loads with harmonic. Finally, the performance of the proposed technique is investigated under disturbance in an islanded MG. An in-depth comparative study is done to highlight the merits of the proposed controller. Simulation results revealed that the total harmonic distortion and the steady-state error of the output voltage are successfully decreased and the dynamic performance and the capability of perturbation rejection are efficiently improved.

关键词： power generation control distributed power generation harmonic distortion nonlinear control systems uncertain systems closed loop systems particle swarm optimisation variable structure systems robust control voltage control control nonlinearities backstepping fractional sliding mode voltage control islanded MG MG voltages FO backstepping SMC closed-loop control system particle swarm optimisation algorithm islanded microgrid robust nonlinear voltage-control strategy distribution networks

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Synthesis of flat-top beam pattern of linear antenna arrays with restricted side lobe level, VSWR and independent nulls using Flower Pollination algorithm

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INTERNATIONAL JOURNAL OF ELECTRONICS 2019年第12期106卷 1964-1977页

作者： Patidar, Hemant Mahanti, Gautam Kumar Muralidharan, R. Oriental Univ Dept Elect & Elect Engn Indore Madhya Pradesh India Natl Inst Technol Dept Elect & Commun Engn Durgapur India NUST Coll Engn Dept Elect & Comp Engn Muscat Oman

Inspired by the biological evolution and the natural selection, evolutionary algorithms have already shown their success in obtaining the solutions for many complex optimisation problems. The aim of this paper is to study the performance of Flower Pollination algorithm for the synthesis of the flat-top beam pattern in linear antenna arrays with restrictions on the peak side lobe level, ripple in the flat-top portion, and maximum Voltage Standing Wave Ratio along with the two independent nulls. Mutual coupling is considered between the parallel dipole antennas. This synthesis is obtained by the generation of current amplitudes with binary phase shifters of 0 and 180 degrees for the antenna elements. Matlab software is used for the simulations. The values of the different antenna parameters as well as the statistical parameters such as the global best fitness value, worst fitness value, mean and standard deviation from the optimisation process generated by the simulations are obtained. Two examples are presented in this paper with different number of antenna elements. The obtained results show the superiority of the Flower Pollination algorithm over particle swarm optimisation algorithm. Moreover, the results obtained from simulations are validated by using FEKO software.

关键词： Antenna array flat-top beam pattern Flower Pollination algorithm ripple side lobe level particle swarm optimisation algorithm voltage standing wave ratio

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Optimised operation of power sources of a PV/battery/hydrogen-powered hybrid charging station for electric and fuel cell vehicles

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IET RENEWABLE POWER GENERATION 2019年第16期13卷 3022-3032页

作者： Garcia-Trivino, Pablo Torreglosa, Juan P. Jurado, Francisco Fernandez Ramirez, Luis M. Univ Cadiz Dept Elect Engn Res Grp Elect Technol Sustainable & Renewable Ene EPS Algeciras Avda Ramon Puyol S-N Cadiz 11202 Spain Univ Huelva Elect Engn Dept Carretera Palos Huelva S-N Huelva 21071 Spain Univ Jaen Dept Elect Engn Res Grp Res & Elect Technol PAIDI TEP 152 EPS Linares Ave Univ Jaen 23700 Spain

This study presents a new energy management system (EMS) for the optimised operation of power sources of a hybrid charging station for electric vehicles and fuel cell vehicles. It is composed of a photovoltaic (PV) system, a battery and a hydrogen system as energy storage systems (ESSs), a grid connection, six fast charging units and a hydrogen supplier. The proposed EMS is designed to reduce the utilisation costs of the ESS and make them work, as much as possible, around their maximum efficiency points. The optimisation function depends on a cost prediction system that calculates the net present cost of the components from their previous performance and a fuzzy logic system designed for improving their efficiency. Finally, a particle swarm optimisation algorithm is used to solve the optimisation function and obtain the required power for each ESS. The proposed EMS is checked under Simulink environment for long-term simulations (25 years). By comparing the EMS with a simpler one that optimises only the costs, it is proved that the proposed EMS achieves better efficiency of the charging station (+7.35%) and a notable reduction in the loss of power supply probability (-57.32%) without compromising excessively its average utilisation cost (+1.81%).

关键词： electric vehicles particle swarm optimisation fuel cell vehicles energy storage battery powered vehicles power grids energy management systems optimisation distributed power generation battery storage plants hybrid power systems photovoltaic power systems energy storage systems local grid fast charging units rated power hydrogen supplier EMS utilisation costs ESS maximum efficiency points optimisation function cost prediction system net present cost fuzzy logic system particle swarm optimisation algorithm hybrid charging station power supply probability average utilisation cost optimised operation power sources cell vehicles energy management system electric vehicles photovoltaic system hydrogen system power 300 0 kW time 25 0 year

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Optimal sizing and siting of smart microgrid components under high renewables penetration considering demand response

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IET RENEWABLE POWER GENERATION 2019年第10期13卷 1809-1822页

作者： Hakimi, Seyed Mehdi Hasankhani, Arezoo Shafie-khah, Miadreza Catalao, Joao P. S. Islamic Azad Univ Dept Elect Engn Damavand Branch Damavand Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

The purpose of this article is to determine the size and place of different components in microgrids (MGs) including renewable energy resources (RERs). Various factors like reliability, the uncertainty of wind speed, solar irradiance, load, and load growth are considered. The Ekbatan residential complex is studied as the pilot case study placed in Tehran, Iran. Ekbatan complex has three separate sets of buildings called phase 1, 2, and 3 considered as smart MGs. The multi-objective optimisation problem is solved considering RERs uncertainties, improving reliability and power quality and minimizing power loss by particle swarm optimisation algorithm. Different constraints in terms of voltage, frequency, resources, and energy storage systems (ESSs) capacity are taken into consideration. The effect of load growth, photovoltaic (PV) and ESSs placement, changing the capital cost of RERs, and demand response of controllable loads are studied on optimal sizing and siting. The proposed method is tested on a wind turbine/PV/fuel cell (FC)/hydrogen tank MGs system and the optimal sizing and siting of mentioned sources could decelerate the rate of increase in the total cost of MG considering the load growth.

关键词： energy storage wind turbines smart power grids renewable energy sources battery storage plants photovoltaic power systems optimisation distributed power generation hybrid power systems particle swarm optimisation energy management systems smart microgrid components high renewables penetration demand response size place different components microgrids including renewable energy resources wind speed solar irradiance load growth Ekbatan residential complex pilot case study Ekbatan complex smart MGs multiobjective optimisation problem RERs uncertainties minimizing power loss particle swarm optimisation algorithm different constraints energy storage systems capacity controllable loads wind turbine PV fuel cell /hydrogen tank MGs system

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Improved grey wolf optimisation algorithms

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JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 615-619页

作者： Gao, Zheng-ming Zhao, Juan Hu, Yu-rong Jingchu Univ Technol Sch Comp Engn Jingmen Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen Peoples R China Jingchu Univ Technol Dept Sci & Technol Jingmen Peoples R China

The grey wolf optimisation (GWO) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nature. However, it still has its capability to be improved in the convergence ratio, stability, and reduce the errors. And it is also easily trapped in local optimum and converged slowly approaching the end, which is just the same defect appearing in other meta-heuristic algorithms such as the bat algorithm (BA), the particle swarm optimisation (PSO) algorithm, and the genetic algorithm (GA). Lots of improvements have been proposed before. In this paper, we propose an improved GWO algorithm inspired by the PSO algorithm to fasten the convergence ratio and reduce the errors. Empirical work and verifications are carried out;and results show its better performance than the standard GWO algorithm and other well-known meta-heuristic algorithms

关键词： grey systems particle swarm optimisation genetic algorithms meta-heuristic algorithms bat algorithm particle swarm optimisation algorithm genetic algorithm improved GWO algorithm PSO algorithm convergence ratio standard GWO algorithm improved grey wolf optimisation algorithm outstanding capabilities

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Hybrid particle swarm optimisation algorithm for image segmentation

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2011年第4期14卷 317-323页

作者： Zhang, Jian-de Lu, Jin-gui Li, Hong-liang Nanjing Univ Technol CAD Ctr Nanjing 210009 Jiangsu Peoples R China

Hybrid particle swarm optimisation algorithm is applied to image segmentation problem to determine the threshold in this paper. Based on the analysis of basic particle swarm optimisation algorithm, a hybrid particle swarm optimisation algorithm which combines the traditional particle swarm algorithm and K-means cluster algorithm is introduced to segment images, and the detailed computer implementation procedure is given for image segmentation. Numerical experiments have been performed to evaluate the efficiency of hybrid algorithm for optimisation problem of image segmentation.

关键词： particle swarm optimisation algorithm image segmentation threshold optimisation

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Test cases generation for multiple paths based on PSO algorithm with metamorphic relations

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IET SOFTWARE 2018年第4期12卷 306-317页

作者： Lv, Xue-Wei Huang, Song Hui, Zhan-Wei Ji, Hai-Jin Army Engn Univ PLA Command Informat Inst Software Engn Dept Nanjing Jiangsu Peoples R China Huaiyin Normal Univ Sch Comp Sci & Technol Huaian Peoples R China PLA Mil Training Software Testing & Evaluat Ctr Test Management Dept Nanjing Jiangsu Peoples R China

The generation of multiple-path test cases can greatly enhance the efficiency of path-wise testing. Various methods adopting meta-heuristic algorithm to generate multiple-path test cases have been proposed, but existing methods focus on improving the meta-heuristic algorithm to get better test case generation efficiency, and test cases covering each path needs to be generated by meta-heuristic algorithm searching. To improve efficiency, a test case generation method for multiple-path coverage is proposed in this study, which combines a particle swarm optimisation (PSO) algorithm with metamorphic relations (MRs). The method first generates a test case using the PSO algorithm, and then generates new test cases by repeatedly using MRs between test cases. This method reduces evolving numbers of PSO algorithm. The experimental results show that the proposed method can significantly enhance the efficiency in terms of fitness evaluations and time consumption.

关键词： particle swarm optimisation program testing search problems metamorphic relations PSO algorithm test cases generation multiple-path test cases path-wise testing test case generation efficiency test case generation method multiple-path coverage particle swarm optimisation algorithm metaheuristic algorithm searching

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Cost-based optimal siting and sizing of electric vehicle charging stations considering demand response programmes

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第8期12卷 1712-1720页

作者： Simorgh, Hamid Doagou-Mojarrad, Hasan Razmi, Hadi Gharehpetian, Gevork B. Microelect Res & Dev Ctr Iran MERDCI Tehran Iran Islamic Azad Univ East Tehran Branch Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran Iran

Here, the optimal placement and sizing of electric vehicle charging stations (EVCSs) are presented. High penetration of electric vehicles (EVs) and resulted losses in network would consequently impose more complexity to solution of application problem of EVCSs. To overcome this problem, the model would consider the incentive-based demand response programmes (DRPs), which is handled by particle swarm optimisation algorithm. Minimising investment cost, connection cost, total cost of losses, and demand response (DR) cost are the objective functions of this problem here. Finally, the proposed model is applied to a test system and results are discussed. By comparing the results obtained through different scenarios, it is concluded that the application of DRP results in a distinct reduction in grid losses and total costs.

关键词： electric vehicle charging secondary cells particle swarm optimisation cost-based optimal siting optimal sizing electric vehicle charging stations EVC incentive-based demand response programmes particle swarm optimisation algorithm test system DRP

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Side lobe level reduction and gain enhancement of a pyramidal horn antenna in the presence of metasurfaces

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IET MICROWAVES ANTENNAS & PROPAGATION 2018年第3期12卷 295-301页

作者： Moradi, Anis Mohajeri, Farzad Shiraz Univ Dept Commun & Elect Zand Blvd Shiraz Iran

In this study, the design method and experimental presentation of low-refractive-index metasurfaces have been reported. These metasurfaces are employed in the interior E-plane walls of a pyramidal horn antenna. The target is a linearly polarised horn antenna with low side lobe levels (SLLs) and symmetric radiation patterns in the K-u band (13-18GHz) for satellite communications. particle swarm optimisation algorithm has been applied to the design procedure to achieve low-loss dispersion-engineered metasurfaces with desirable surface impedance characteristics. The metasurfaces were fabricated using precise low-cost printed-circuit board technique. There was perfect agreement between the measurement and simulation results. The fabricated horn showed low SLLs and an increase in the gain across the entire bandwidth. The SLLs are reduced by 14-29dB in the E-planes and the gain is improved by 1.2-2.3dB compared with a corresponding conventional horn. The far field radiation patterns of the metahorn verify the metasurface design approach. Furthermore, this method assures a lighter horn with an easier manufacturing technique in comparison with the other conventional horns, such as corrugated horns.

关键词： printed circuits surface impedance particle swarm optimisation satellite antennas antenna radiation patterns metamaterial antennas horn antennas side lobe level reduction pyramidal horn antenna gain enhancement low-refractive-index metasurface design method pyramidal horn antenna interior E-plane wall SLL reduction symmetric radiation patterns Ku band satellite communications particle swarm optimisation algorithm low-loss dispersion-engineered metasurfaces surface impedance characteristics low-cost printed circuit board technique metahorn field radiation patterns metasurface design approach frequency 13 GHz to 18 GHz gain 1 2 dB to 2 3 dB

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