Grid-tied Two PV LLC Converter with Dual MPPT Algorithm based on the Adaptive Neuro Fuzzy Interface System (ANFIS)
Grid-tied Two PV LLC Converter with Dual MPPT Algorithm based on the Adaptive Neuro Fuzzy Interface System (ANFIS)
复制标题
具有基于自适应神经模糊接口系统 (ANFIS) 的双 MPPT 算法的并网两个 PV LLC 转换器
DOI：
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发表时间：
2021
期刊：
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通讯作者：
I. Batarseh
中科院分区：
文献类型：
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作者：
Sumana Ghosh;I. Batarseh
提出了一种基于自适应神经模糊接口系统(ANFIS)的双最大功率点跟踪(MPPT)算法，以获取并网移相双输入LLC变流器的最大功率。该算法可以同时从两个光伏电池板提取最大功率，即使在动态天气条件和局部遮阳的情况下也是如此。双MPPT算法利用脉宽调制(PWM)和移相调制(PSM)相结合的技术产生占空比和相移来调节潮流。所有一次开关的零电压开关(ZVS)和二次二极管的零电流开关(ZCS)在整个输入和负载条件范围内均可实现。所提出的ANFIS模型使用每个光伏电池板的LLC转换器的输入输出数据集来训练神经网络，而模糊规则利用隶属度函数来确保最优输出。另一方面，电网控制器维护固定的直流链路，并确保电网注入电力。文中推导了双MPPT算法，并对所提出的闭环系统进行了验证。
This paper proposes a dual Maximum Power Point Tracking (MPPT) algorithm using the Adaptive Neuro Fuzzy Interface System (ANFIS) to harvest maximum power from a grid-tied phase-shifted dual-input LLC converter. The algorithm can simultaneously extract maximum power from two Photovoltaic (PV) panels even under dynamic weather conditions and partial shading. The dual MPPT algorithm generates duty cycle and phase shift to regulate the power flow using combined pulse width modulation (PWM) and phase-shift modulation (PSM) techniques. Zero Voltage Switching (ZVS) in all primary switches and Zero Current Switching (ZCS) in the secondary diodes are achieved over the entire range of input and load conditions. The proposed ANFIS model uses the LLC converter’s input-output data set for each PV panel to train the neural network while the fuzzy rules ensure the optimum output using membership functions. On the other hand, a grid controller maintains the fixed DC link as well as ensures grid power injection. Derivation of this dual-MPPT algorithm followed by the verification of the proposed closed-loop system is presented here.