Prediction of time series of overhead lines failure rate with chaotic indicators
The results of forecasting the failure rate (failure frequency) of overhead lines (OHL) 500 kV, presented in the form of a time series with signs of chaos, are presented. Predictive estimates are obtained using methods of singular spectrum analysis, neural and fuzzy neural networks. As an object of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2020-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/76/e3sconf_rses2020_01016.pdf |
| _version_ | 1828882690962620416 |
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| author | Zubov Nikolay Misrikhanov Misrikhan Ryabchenko Vladimir Shuntov Andrey |
| author_facet | Zubov Nikolay Misrikhanov Misrikhan Ryabchenko Vladimir Shuntov Andrey |
| author_sort | Zubov Nikolay |
| collection | DOAJ |
| description | The results of forecasting the failure rate (failure frequency) of overhead lines (OHL) 500 kV, presented in the form of a time series with signs of chaos, are presented. Predictive estimates are obtained using methods of singular spectrum analysis, neural and fuzzy neural networks. As an object of singular spectrum analysis, a delay matrix is used, which is formed on the basis of the time series of the failure rate. The prediction was carried out by means of one-step transformations of the initial data. For prediction using a neural network, a direct signal transmission network is used, trained by the backpropagation method. In order to achieve the minimum mean squared error, the training sample contained the maximum possible history. To predict the failure rate by the method of fuzzy neural networks, the Wang-Mendel network was chosen. In all prediction cases, within the framework of one prediction year, 10 thousand "training - prediction" cycles were performed, which ensured the stationarity property of the histograms of the failure rate distributions. |
| first_indexed | 2024-12-13T10:33:05Z |
| format | Article |
| id | doaj.art-e7b3fd8abb864344a1129ef8a21606d8 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-13T10:33:05Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-e7b3fd8abb864344a1129ef8a21606d82022-12-21T23:50:48ZengEDP SciencesE3S Web of Conferences2267-12422020-01-012160101610.1051/e3sconf/202021601016e3sconf_rses2020_01016Prediction of time series of overhead lines failure rate with chaotic indicatorsZubov Nikolay0Misrikhanov Misrikhan1Ryabchenko Vladimir2Shuntov Andrey3National research university “Bauman Moscow State Technical University”National research university "MPEI""JSC “R&D Center FGC UES”National research university "MPEI""The results of forecasting the failure rate (failure frequency) of overhead lines (OHL) 500 kV, presented in the form of a time series with signs of chaos, are presented. Predictive estimates are obtained using methods of singular spectrum analysis, neural and fuzzy neural networks. As an object of singular spectrum analysis, a delay matrix is used, which is formed on the basis of the time series of the failure rate. The prediction was carried out by means of one-step transformations of the initial data. For prediction using a neural network, a direct signal transmission network is used, trained by the backpropagation method. In order to achieve the minimum mean squared error, the training sample contained the maximum possible history. To predict the failure rate by the method of fuzzy neural networks, the Wang-Mendel network was chosen. In all prediction cases, within the framework of one prediction year, 10 thousand "training - prediction" cycles were performed, which ensured the stationarity property of the histograms of the failure rate distributions.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/76/e3sconf_rses2020_01016.pdf |
| spellingShingle | Zubov Nikolay Misrikhanov Misrikhan Ryabchenko Vladimir Shuntov Andrey Prediction of time series of overhead lines failure rate with chaotic indicators E3S Web of Conferences |
| title | Prediction of time series of overhead lines failure rate with chaotic indicators |
| title_full | Prediction of time series of overhead lines failure rate with chaotic indicators |
| title_fullStr | Prediction of time series of overhead lines failure rate with chaotic indicators |
| title_full_unstemmed | Prediction of time series of overhead lines failure rate with chaotic indicators |
| title_short | Prediction of time series of overhead lines failure rate with chaotic indicators |
| title_sort | prediction of time series of overhead lines failure rate with chaotic indicators |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/76/e3sconf_rses2020_01016.pdf |
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