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| 1 | Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances显示文摘在最近的年里,许多不同技术被使用以便拉最大的力量从光电(PV ) 为改变太阳的发光和温度条件的模块。通常, PV 系统的输出发电取决于断断续续的太阳的曝晒,房间温度, PV 面板的效率和它的输出电压水平。因而,追踪 PV 系统的产生力量并且利用是必要的最佳地收集了太阳能。这份报纸的目的是由使用一个适应 neuro 模糊的推理系统(ANFIS ) 和基因算法(GA ) 模仿并且控制连接格子的 PV 来源控制器。数据被 GA 优化然后,这些最佳值在网络训练被使用。模拟结果显示 ANFIS-GA 控制器能在最大的力量点(MPP ) 附近与更少的变化容易满足负担的需要并且能增加集中速度完成 MPP 而非常规方法。而且,控制线电压和电流,一个格子方面 P/Q 控制器被使用了。动态建模, PV 系统的控制和模拟学习用 Matlab/Simulink 程序被执行。 | Saeed VAFAEI Alireza REZVANI Majid GANDOMKAR Maziar IZADBAKHSH | 2015 | Frontiers in Energy2015，9，3： | 3 |
| 2 | Comparison of modeling methods for wind power prediction:a critical study显示文摘Prediction of power generation of a wind turbine is crucial,which calls for accurate and reliable models.In this work,six different models have been developed based on wind power equation,concept of power curve,response surface methodology(RSM)and artificial neural network(ANN),and the results have been compared.To develop the models based on the concept of power curve,the manufacturer’s power curve,and to develop RSM as well as ANN models,the data collected from supervisory control and data acquisition(SCADA)of a 1.5 MW turbine have been used.In addition to wind speed,the air density,blade pitch angle,rotor speed and wind direction have been considered as input variables for RSM and ANN models.Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods. | Rashmi P.SHETTY A.SATHYABHAMA P.Srinivasa PAI | 2020 | Frontiers in Energy2020，14，2： | 1 |
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