The employment of solar microconverter allows a more detailed monitoring of the photovoltaic (PV) output power at the single module level; thus, machine learning techniques are capable to track the peculiarities of modules in the PV plants, such as regular shadings. In this way, it is possible to compare in real time the day-ahead forecast power with the actual one in order to better evaluate faults or anomalous trends that might have occurred in the PV plant. This paper presents a method for an effective fault diagnosis; this method is based on the day-ahead forecast of the output power from an existing PV module, linked to a microconverter, and on the outcome of the neighbor PV modules. Finally, this paper also proposes the analysis of the most common error definitions with new mathematical formulations, by comparing their effectiveness and immediate comprehension, in view of increasing power forecasting accuracy and performing both real-time and offline analysis of PV modules performance and possible faults.

中文翻译：

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基于微变流器和日前预报的光伏组件故障诊断

太阳能微型转换器的使用允许在单个模块级别更详细地监控光伏 (PV) 输出功率；因此，机器学习技术能够跟踪光伏电站中模块的特性，例如常规阴影。通过这种方式，可以将日前预测的功率与实际功率进行实时比较，以便更好地评估光伏电站中可能发生的故障或异常趋势。本文提出了一种有效的故障诊断方法；该方法基于对连接到微转换器的现有光伏模块输出功率的日前预测，以及相邻光伏模块的结果。最后，本文还提出用新的数学公式分析最常见的错误定义，