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PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast

机译：基于微转换器和日前预报的光伏组件故障诊断

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摘要

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.
机译：使用太阳能微转换器可以在单个模块级别更详细地监视光伏（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殊性，例如常规阴影。这样，可以实时将日前预测功率与实际功率进行比较，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。该方法基于与微型转换器相连的现有PV模块的输出功率的日前预测，以及相邻PV模块的结果。最后，鉴于提高的功率预测准确性以及对光伏组件性能和可能出现的故障进行实时和离线分析，本文还通过比较其有效性和即时理解力，提出了使用新的数学公式对最常见的错误定义进行分析的建议。 。

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