This paper deals with a new version of an incremental conductance algorithm for maximum power harvesting (MPH) from the solar photovoltaic array, which has inherent decision taking and self-adaptive ability. The working principle of a self-adaptive incremental conductance (SAInC) algorithm is based on three consecutive operating points on the power–voltage characteristic. These points smartly detect the dynamic condition, as well as under normal condition, search the maximum power peak (MPP) zone. Moreover, using triangular analogy, it decides the optimum operating position for next iteration, which is responsible for quick MPP tracking as well as good dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady-state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on an experimental system. Moreover, performance of an SAInC algorithm is compared with the popular and recent state-of-the-art methods. The satisfactory dynamic and steady-state performances with low complexity as well as low computational burden of the SAInC algorithm show the superiority over state-of-the-art methods.

中文翻译：

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自适应增量电导算法，可快速有效地从光伏阵列中获取无纹波的最大功率

本文研究了一种增量电导算法的新版本，该算法可用于太阳能光伏阵列的最大功率收集（MPH），具有固有的决策能力和自适应能力。自适应增量电导（SAInC）算法的工作原理基于电源电压特性上的三个连续工作点。这些点可以智能地检测动态条件，以及在正常条件下搜索最大功率峰值（MPP）区域。此外，使用三角类比，它可以确定下一次迭代的最佳操作位置，这有助于快速MPP跟踪以及良好的动态性能。在此，在每个新的迭代中，步长将比以前的步长减小90％，从而提供了无振荡的稳态性能。所提技术的有效性已通过MATLAB仿真验证并在实验系统上进行了测试。此外，将SAInC算法的性能与流行的和最新的方法进行了比较。SAInC算法具有令人满意的低复杂度和低计算量的动态和稳态性能，显示出优于最新方法的优越性。