A Novel Photovoltaic Array Outlier Cleaning Algorithm Based on Sliding Standard Deviation Mutation

Aoyu Hu, Qian Sun, Hao Liu, Ning Zhou, Zhan’ao Tan and Honglu Zhu
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Aoyu Hu: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Qian Sun: State Grid HeNan Electric Power Company Research Institute, Zhengzhou 450000, China
Hao Liu: State Grid HeNan Electric Power Company Research Institute, Zhengzhou 450000, China
Ning Zhou: State Grid HeNan Electric Power Company Research Institute, Zhengzhou 450000, China
Zhan’ao Tan: Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Honglu Zhu: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

Energies, 2019, vol. 12, issue 22, 1-16

Abstract: There is a large number of outliers in the operation data of photovoltaic (PV) array, which is caused by array abnormalities and faults, communication issues, sensor failure, and array shutdown during PV power plant operation. The outlier will reduce the accuracy of PV system performance analysis and modeling, and make it difficult for fault diagnosis of PV power plant. The conventional data cleaning method is affected by the outlier data distribution. In order to solve the above problems, this paper presents a method for identifying PV array outliers based on sliding standard deviation mutation. Considering the PV array output characteristics under actual environmental conditions, the distribution of array outliers is analyzed. Then, an outlier identification method is established based on sliding standard deviation calculation. This method can identify outliers by analyzing the degree of dispersion of the operational data. The verification part is illustrated by case study and algorithm comparison. In the case study, multiple sets of actual operating data of different inverters are cleaned, which is selected from a large grid-connected power station. The cleaning results illustrate the availability of the algorithm. Then, the comparison against the quantile-algorithm-based outlier identification method explains the effectiveness of the proposed algorithm.

Keywords: PV array; power curve; raw operation data; data cleaning; cleaning algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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