基于两步Pair-Copula的光伏阵列异常数据辨识方法

黄煜; 张潇潇; 胡松林; 窦春霞; 洪奕; 宋玮琼

doi:10.19912/j.0254-0096.tynxb.2023-2043

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 10-21. DOI: 10.19912/j.0254-0096.tynxb.2023-2043

基于两步Pair-Copula的光伏阵列异常数据辨识方法

黄煜¹, 张潇潇¹, 胡松林¹, 窦春霞¹, 洪奕², 宋玮琼³

作者信息 +

ANOMALY DATA DETECTION METHOD FOR PHOTOVOLTAIC ARRAYS BASED ON TWO-STEP PAIR-COPULA MODELING

Huang Yu¹, Zhang Xiaoxiao¹, Hu Songlin¹, Dou Chunxia¹, Hong Yi², Song Weiqiong³

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摘要

为有效进行光伏阵列的性能监控和功率预测等重要工作,如何提升光伏数据的质量便成为当下亟待解决的问题。提出一种基于两步Pair-Copula的光伏阵列异常数据识别方法。该方法分为两个步骤,第一步是对光伏阵列直流侧电流进行异常值识别;第二步以第一步为基础,对光伏直流侧电压进行异常值识别。具体而言,首先基于Pair-Copula对光伏电流、太阳辐照度和温度之间的相依结构进行建模,并采用赤池信息准则优化Copula函数。然后建立光伏电流的条件概率模型,并求解出条件概率置信区间。再以光伏电流的置信区间为主要判据,进行电流异常值的识别和剔除。最后,基于上一步得到的数据,重复上述步骤,对光伏电压值进行异常值的剔除。通过仿真实验结果看出,与其他异常识别方法相比,该文提出的方法在保持低识别错误率的同时,具有更高的识别准确率。

Abstract

To optimally monitor photovoltaic arrays and forecast their power production, improving the quality of photovoltaic data is an essential and urgent task. To this end, this paper introduces a method for the identification of anomalous data in photovoltaic arrays based on a two-step Pair-Copula approach. This method is divided into two stages： the first stage involves the identification of outliers in the direct current side of the photovoltaic array, while the second stage, building upon the first, involves the identification of outliers in the photovoltaic direct current side voltage. More specifically, the Pair-Copula is utilized to model the dependence structure between photovoltaic current, irradiance, and temperature, with Akaike information criterion employed to optimize the Copula function. Subsequently, a conditional probability model for the photovoltaic current is established, and the formula for calculating the confidence interval of the conditional probability is derived. The confidence interval of the photovoltaic current is then used as the primary criterion for identifying and eliminating current outliers. Finally, building upon the data obtained in the previous step, the aforementioned procedure is repeated to eliminate voltage outliers. The results of simulation experiments demonstrate that, compared with other outlier identification methods, the approach proposed in this paper maintains a low identification error rate while boasting a higher identification accuracy.

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Key words

photovoltaic arrays / anomaly detection / correlation theory / Pair-Copula theory / confidence interval

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黄煜, 张潇潇, 胡松林, 窦春霞, 洪奕, 宋玮琼. 基于两步Pair-Copula的光伏阵列异常数据辨识方法[J]. 太阳能学报. 2024, 45(12): 10-21 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2043

Huang Yu, Zhang Xiaoxiao, Hu Songlin, Dou Chunxia, Hong Yi, Song Weiqiong. ANOMALY DATA DETECTION METHOD FOR PHOTOVOLTAIC ARRAYS BASED ON TWO-STEP PAIR-COPULA MODELING[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 10-21 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2043

中图分类号： TK51

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