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A new real-time reconfiguration approach based on neural network in partial shading for PV arrays

机译:基于神经网络的光伏阵列局部阴影实时重配置新方法

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Reconfiguration process in photovoltaic (PV) arrays is very important to improve power-voltage characteristics of the system. In this paper, a new reconfiguration method based on neural network is proposed for PV arrays under partial shadow conditions. A new connection control algorithm based on artificial neural network is presented by the proposed method. This method includes fixed part and adaptive part and uses short circuit currents of PV panel group in every rows of adaptive and fixed part in array. A neural network used for reconfiguration strategy finds new configuration scheme of PV array. Then, adaptive parts are connected to rows of fixed part according to this configuration with switching matrix. Proposed approach has been verified with experimental results obtained using Beagle Board XM microprocessor board in real time for 3×4 array. As shown in results, many contributions such as an improvement in the output power of the PV array, an efficient reconfiguration strategy, real-time applicability, easy measurable parameters, and independence from panel types have been obtained with proposed method.
机译:光伏(PV)阵列中的重新配置过程对于提高系统的电源电压特性非常重要。本文针对部分阴影条件下的光伏阵列提出了一种新的基于神经网络的重构方法。该方法提出了一种新的基于人工神经网络的连接控制算法。该方法包括固定部分和自适应部分,并在阵列的自适应部分和固定部分的每一行中使用PV面板组的短路电流。用于重新配置策略的神经网络找到了光伏阵列的新配置方案。然后,根据该配置,利用切换矩阵将自适应部分连接到固定部分的行。使用Beagle Board XM微处理器板针对3×4阵列实时获得的实验结果已验证了所提出的方法。如结果所示,通过所提出的方法已经获得了许多贡献,例如提高了PV阵列的输出功率,有效的重新配置策略,实时适用性,易于测量的参数以及与面板类型的独立性。

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