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Optimal operation of photovoltaic/utility grid interconnected electrical power system using neural network

机译：基于神经网络的光伏/实用电网互连电力系统的最佳运行

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The performance of photovoltaic power system interconnected with utility grid can be improved through an application of advanced control method. This paper introduces an application of an artificial neural network on the operation control of the photovoltaic/utility grid to improve system efficiency and reliability. The generated power from photovoltaic solar cells array has been calculated by a computer program under known insolation and temperature. The computer program which proposed here and applied to carry out these calculations is based on the minimization of the power purchase from grid. This paper focus on a hybrid system consists of photovoltaic system accompanied with battery storage interconnected with utility grid taking into account the variation of solar radiation and load demand during the day. Different feed forward neural network architectures are trained and tested with data containing a variety of operation patterns. A simulation is carried out over one year using the hourly data of the load demand, insolation and temperature at El'Zafranna site, Egypt as a case study. The results show that the selected neural network architecture (5+9+4) gives reasonably accurate operation of photovoltaic/ utility grid accompanied with battery storage.
机译：通过应用先进的控制方法，可以提高与实用电网相互连接的光伏电力系统的性能。本文介绍了人工神经网络对光伏/实用电网操作控制的应用，以提高系统效率和可靠性。来自光伏太阳能电池阵列的产生功率已经通过已知的剥离和温度下的计算机程序计算。这里提出并应用这些计算的计算机程序基于从电网购买的功率最小化。本文侧重于混合动力车系统由光伏系统组成，伴随着与实用电网相互连接的电池存储，考虑到当天的太阳辐射和负荷需求的变化。不同的馈送前向神经网络架构培训并使用包含各种操作模式的数据进行测试。埃及El'Zafranna网站的每小时数据，埃及以e埃及，埃及为例研究，使用每小时数据进行仿真。结果表明，所选择的神经网络架构（5 + 9 + 4）可提供光伏/公用电网的合理精确运行，伴随电池存储。

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