Short-term irradiance variability because of the passing clouds of unknown size, direction, and speed is a key issue for power grid planners because of the unexpected fluctuation in the generated power of photovoltaic (PV) systems. In order to handle this issue, several models have been presented in the literature to estimate the variability of the PV systems output power during cloudy and partial shading events. However, the estimation error of all presented models in the literature is relatively high. To comply with the utility guidelines of limiting the PV systems generated power variability to a level less than 10% of the PV systems capacity per minute, a more accurate power estimation model is essential to precisely calculate the required energy storage backup. In this article, a new model to estimate the output power of a group of rooftop PV systems during cloudy events using only one sensor is proposed. In this regard, two new strategies are adopted. First, day time is divided into three-time segments in which each segment exhibits almost the same weather conditions. Second, the data of each time series are analyzed using the wavelet transform to divide them into high and low-frequency modes. The frequency modes are then used to train a gene expression programming model to estimate the entire generated power of the PV systems. The proposed model features higher accuracy than other models presented in the literature. A sensitivity analysis is performed to quantify the effect of various parameters on the accuracy of the proposed model whose robustness is validated through practical data collected from a group of distributed rooftop PV systems in Brisbane, QLD, Australia. Results reveal that the maximum average mean absolute error of the proposed model is 7.49%, whereas it is 12.12% for the existing models in the literature.

中文翻译：

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使用基因表达编程估算阴天光伏系统的发电量

由于光伏 (PV) 系统产生的功率出现意外波动，因此由于大小、方向和速度未知的云流过而导致的短期辐照度变化是电网规划者的关键问题。为了解决这个问题，文献中提出了几种模型来估计多云和部分阴影事件期间光伏系统输出功率的可变性。然而，文献中所有提出的模型的估计误差都相对较高。为了遵守将光伏系统产生的功率变化限制在每分钟光伏系统容量的 10% 以下的公用事业指南，更准确的功率估计模型对于精确计算所需的备用储能至关重要。在本文中，提出了一种仅使用一个传感器在多云事件期间估计一组屋顶光伏系统输出功率的新模型。在这方面，采用了两种新策略。首先，白天时间被分为三个时间段，其中每个段都表现出几乎相同的天气条件。其次，利用小波变换对每个时间序列的数据进行分析，将它们分为高频和低频模式。然后使用频率模式来训练基因表达编程模型，以估计光伏系统的整个发电量。所提出的模型具有比文献中提出的其他模型更高的精度。执行敏感性分析以量化各种参数对所提出模型准确性的影响，该模型的稳健性通过从澳大利亚昆士兰州布里斯班的一组分布式屋顶光伏系统收集的实际数据得到验证。结果表明，所提出模型的最大平均平均绝对误差为 7.49%，而文献中现有模型的最大平均绝对误差为 12.12%。