E3S Web Conf.
Volume 216, 2020Rudenko International Conference “Methodological problems in reliability study of large energy systems” (RSES 2020)
|Number of page(s)||5|
|Published online||14 December 2020|
Prediction of time series of overhead lines failure rate with chaotic indicators
1 National research university “Bauman Moscow State Technical University”, Moscow, Russia
2 National research university "MPEI"", Moscow, Russia
3 JSC “R&D Center FGC UES”, Moscow, Russia
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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