A Novel Photovoltaic Array Outlier Cleaning Algorithm Based on Sliding Standard Deviation Mutation
| Autor: | Qian Sun, Ning Zhou, Hao Liu, Zhan'ao Tan, Aoyu Hu, Honglu Zhu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
PV array
power curve raw operation data data cleaning cleaning algorithm Control and Optimization Power station Renewable Energy Sustainability and the Environment Computer science 020209 energy 05 social sciences Photovoltaic system Energy Engineering and Power Technology 02 engineering and technology Fault (power engineering) Standard deviation Identification (information) 0502 economics and business Mutation (genetic algorithm) Outlier 0202 electrical engineering electronic engineering information engineering Statistical dispersion Electrical and Electronic Engineering Engineering (miscellaneous) Algorithm 050203 business & management Energy (miscellaneous) |
| Zdroj: | Energies; Volume 12; Issue 22; Pages: 4316 |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en12224316 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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