A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays
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 curr...
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| Format: | Article |
| Language: | English |
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Kaunas University of Technology
2024-10-01
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| Series: | Elektronika ir Elektrotechnika |
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| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/38231 |
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| _version_ | 1846142252079906816 |
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| author | Haitao Li Weiqiong Song Le Zhao Shuai Guo Wei Song Li Huang |
| author_facet | Haitao Li Weiqiong Song Le Zhao Shuai Guo Wei Song Li Huang |
| author_sort | Haitao Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1fdd5ef29e284a14bdf6f61ce1e68c9a |
| institution | Kabale University |
| issn | 1392-1215 2029-5731 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj-art-1fdd5ef29e284a14bdf6f61ce1e68c9a2024-12-03T13:46:24ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312024-10-01305455610.5755/j02.eie.3823143485A Vine-Copula Method for Outlier Identification in Photovoltaic ArraysHaitao Li0Weiqiong Song1Le Zhao2Shuai Guo3Wei Song4Li Huang5China Electric Power Research Institute, Beijing, ChinaChina Electric Power Research Institute, Beijing, ChinaState Grid Beijing Electric Power Company, Beijing, ChinaChina Electric Power Research Institute, Beijing, ChinaChina Electric Power Research Institute, Beijing, ChinaSoutheast University, Nanjing, Jiangsu, ChinaTo 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.https://eejournal.ktu.lt/index.php/elt/article/view/38231photovoltaic arrayanomalous data identificationvine-copulaconfidence intervalinterdependent structure |
| spellingShingle | Haitao Li Weiqiong Song Le Zhao Shuai Guo Wei Song Li Huang A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays Elektronika ir Elektrotechnika photovoltaic array anomalous data identification vine-copula confidence interval interdependent structure |
| title | A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays |
| title_full | A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays |
| title_fullStr | A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays |
| title_full_unstemmed | A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays |
| title_short | A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays |
| title_sort | vine copula method for outlier identification in photovoltaic arrays |
| topic | photovoltaic array anomalous data identification vine-copula confidence interval interdependent structure |
| url | https://eejournal.ktu.lt/index.php/elt/article/view/38231 |
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