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Performance prediction of distributed PV generation systems using Artificial Neural Networks (ANN) and Mesh Networks

机译:使用人工神经网络(ANN)和网格网络的分布式光伏发电系统的性能预测

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This paper demonstrates a mechanism of modeling the performance of inverters using performance data along with climatological parameters. The proposed methodology is to integrate PV generation data at inverter level from different generation sources in a single platform. A robust network architecture along with data communication devices is used for fetching the inverter level data. This data is appended with real time climatological parameters. A model is then developed for futuristic prediction of PV installation performance data with respect to climatological parameters. Artificial Neural Network (ANN) architecture is used in the process for correlating the climatological parameters with respect to each technology of solar panel for predicting DC current output of inverter. An accuracy of 93.9% is achieved through this model for predicting the DC output of a PV system.
机译:本文演示了一种使用性能数据以及气候参数对逆变器性能进行建模的机制。拟议的方法是将来自不同发电源的逆变器级别的光伏发电数据集成到一个平台中。强大的网络体系结构与数据通信设备一起用于获取逆变器级别的数据。该数据附有实时气候参数。然后,针对气候参数开发了一个模型,用于对光伏安装性能数据进行未来的预测。人工神经网络(ANN)体系结构用于该过程中,以针对太阳能电池板的每种技术将气候参数关联起来,以预测逆变器的直流电流输出。通过该模型预测光伏系统的直流输出,可以达到93.9%的精度。

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