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A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric conditions

机译：在快速变化的大气条件下用于并网光伏系统的混合MPPT方法

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摘要

The modest changes in operating current and voltage of photovoltaic (PV) panel due to the temperature and radiation fluctuation constitute visible variations in the output power. In this paper, a hybrid method to optimize the performance of the maximum power point tracking (MPPT) controller for mitigating these variations and forcing the system to operate on maximum power point (MPP) is developed. The presented Hybrid MPPT method consists of two loops: (i) artificial neural network (ANN) based reference point setting loop and (ii) perturbation and observation (P&O) based fine tuning loop. To assess robustness of the proposed method, a comparison is performed using the conventional P&O, incremental conductance (INC) and ANN based MPPT methods under both rapidly changing radiation and partially shaded conditions by using PSCAD/EMTDC program. The results obtained from the test cases explicitly demonstrate that the presented MPPT method not only achieves an increase in speed of MPP tracking, but also reduces the steady state oscillations and prevents the possibility of the algorithm from confusing its perturbation direction. The system efficiency more than 98.26%, 120 ms improvement in convergence speed and 1.16 V decrease in the rate of overshoot are obtained with proposed Hybrid MPPT method under the rapidly changing environmental conditions. (C) 2017 Elsevier B.V. All rights reserved.
机译：由于温度和辐射波动，光伏（PV）面板的工作电流和电压的适度变化构成了输出功率的可见变化。在本文中，开发了一种混合方法来优化最大功率点跟踪（MPPT）控制器的性能，以缓解这些变化并强制系统在最大功率点（MPP）上运行。提出的混合MPPT方法由两个回路组成：（i）基于人工神经网络（ANN）的参考点设置回路和（ii）基于摄动和观测（P＆O）的微调回路。为了评估所提出方法的鲁棒性，使用PSCAD / EMTDC程序在快速变化的辐射和部分阴影条件下，使用常规P＆O，增量电导（INC）和基于ANN的MPPT方法进行了比较。从测试案例中获得的结果清楚地表明，所提出的MPPT方法不仅可以提高MPP跟踪的速度，而且可以减少稳态振荡，并防止算法混淆其扰动方向的可能性。在快速变化的环境条件下，提出的Hybrid MPPT方法获得的系统效率超过98.26％，收敛速度提高了120 ms，过冲率降低了1.16V。 （C）2017 Elsevier B.V.保留所有权利。

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