A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays

Authors

  • Haitao Li China Electric Power Research Institute, Beijing, China
  • Weiqiong Song China Electric Power Research Institute, Beijing, China
  • Le Zhao State Grid Beijing Electric Power Company, Beijing, China
  • Shuai Guo China Electric Power Research Institute, Beijing, China
  • Wei Song China Electric Power Research Institute, Beijing, China
  • Li Huang Southeast University, Nanjing, Jiangsu, China

DOI:

https://doi.org/10.5755/j02.eie.38231

Keywords:

Photovoltaic array, Anomalous data identification, Vine-Copula, Confidence interval, Interdependent structure

Abstract

To improve the operational efficiency and reliability of photovoltaic power stations, this paper introduces a novel approach to detect outliers in photovoltaic arrays using a Vine-Copula method. The procedure is divided into two distinct phases. Initially, it identifies deviations in the direct current (DC) component of the photovoltaic (PV) system. The following phase extends this by pinpointing irregularities in the DC voltage of the array. To model the interconnection between the PV current, irradiance, and temperature, the Vine-Copula is employed in this process. The optimisation of this function is based on the Akaike information criterion. Subsequently, a conditional probability model for the PV current is developed along with a formula to determine the quantile of this probability. This interval is then employed as the primary metric for detecting and eliminating current deviations. After refining the current data, a similar approach is taken to address voltage irregularities. The results of the simulation tests indicate that this proposed method is more effective, showing lower error rates and higher accuracy in detecting outliers, compared to other methods.

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Published

2024-10-22

How to Cite

Li, H., Song, W., Zhao, L., Guo, S., Song, W., & Huang, L. (2024). A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays. Elektronika Ir Elektrotechnika, 30(5), 45-56. https://doi.org/10.5755/j02.eie.38231

Issue

Section

RENEWABLE ENERGY

Funding data