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首页> 外文期刊>Indian Journal of Science and Technology >Investigation of ANN-GA and Modified Perturb and Observe MPPT Techniques for Photovoltaic System in the Grid Connected Mode
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Investigation of ANN-GA and Modified Perturb and Observe MPPT Techniques for Photovoltaic System in the Grid Connected Mode

机译：并网模式下光伏系统的ANN-GA研究和改进的扰动并遵守MPPT技术

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摘要

The output characteristics of Photovoltaic (PV) arrays are nonlinear and change with the cell's temperature and solar radiation. Maximum Power Point Tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). This paper presents an integrated offline Genetic Algorithm (GA) and artificial neural network (ANN) to track the solar power optimally based on various operation conditions due to the uncertain climate change. Data are optimized by GA and then these optimum values are used in neural network training. The obtained results show minimal error of MPP, optimal voltage (Vmpp) and superior capability of the suggested method in the MPPT. The simulation results are presented by using Matlab/Simulink and show that ANN-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point; also, this method has well regulated PV output power and it produces extra power rather than Modified Perturb&Observe (MP&O) method for different conditions. Moreover, to control both line voltage and current, a grid side P-Q controller has been applied.
机译：光伏（PV）阵列的输出特性是非线性的，并随电池的温度和太阳辐射而变化。最大功率点跟踪（MPPT）方法用于通过连续跟踪最大功率点（MPP）来最大化PV阵列输出功率。本文提出了一种集成的离线遗传算法（GA）和人工神经网络（ANN），以在不确定的气候变化条件下根据各种运行条件优化跟踪太阳能。通过GA优化数据，然后将这些最佳值用于神经网络训练。获得的结果显示了MPPT的最小误差，最佳电压（Vmpp）和MPPT中建议方法的出色功能。用Matlab / Simulink给出了仿真结果，表明并网模式的ANN-GA控制器可以轻松满足负载需求，并且最大功率点附近的波动较小。而且，该方法具有良好的PV输出功率调节能力，并且可以产生额外的功率，而不是针对不同条件的改良扰动和观察（MP＆O）方法。此外，为了控制线路电压和电流，已经应用了电网侧P-Q控制器。

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