Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data
Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data
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DOI：
10.3390/electronics8080858
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发表时间：
2019-08-01
期刊：
影响因子：
2.9
通讯作者：
Al-Raweshidy, Hamed S.
中科院分区：
文献类型：
--
作者：
Al-Majidi, Sadeq D.;Abbod, Maysam E.;Al-Raweshidy, Hamed S.
最大功率点跟踪(MPPT)技术是提高光伏阵列发电输出功率的光伏系统设计的重要组成部分。虽然已经提出了各种不同的技术，但自适应神经-模糊推理系统(ANFIS)因其响应快、振荡小而成为最大功率点跟踪的最有效方法。然而，准确的训练数据是设计有效的ANFIS-MPPT的一大挑战。本文设计了一种基于大量实验训练数据的ANFIS-MPPT方法，以避免系统出现较大的训练误差。这些数据是在2018年全年从安装在英国伦敦布鲁内尔大学的光伏阵列的实验测试中收集的。通常，来自实验测试的数据包含误差，因此使用曲线拟合技术来分析以优化ANFIS模型的整定。为了评估该方法的性能，利用光伏系统的MATLAB/SIMULINK模型对该方法进行了仿真。通过一个半阴天的实际测量试验，计算了该方法在不同气候条件下的平均效率。实验结果表明，该方法能准确跟踪最大功率点，效率可达99.3%以上。
Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.