| Title |
Save gydančio tinklo sistemos (flir) įtaka saidi saifi rodikliams skirstomajame tinkle |
| Translation of Title |
Impact of self-healing network system (flir) over saidi saifi indicators in distribution network. |
| Authors |
Kryžanauskas, Ruslanas |
| Full Text |
|
| Pages |
70 |
| Keywords [eng] |
artificial intelligence ; self healing network ; saidi saifi ; distribution network |
| Abstract [eng] |
This project analyses a self-healing network, its operating principles. SAIDI and SAIFI indices are determined on the lines before and after the implementation of the self-healing network. An analysis of historical weather data over a 10-year period, analysis of distribution network failures over a 1.5-year period, and analysis of the operations of the self-healing network over a 1.5-year period that trigger the main variables for recovery were performed. Linear regression, simple neural network, and complex neural network artificial intelligence methods were used to predict the SAIDI and SAIFI indices. Hyperparameter optimization was performed for the best predictive model. The research revealed that the installation of a self-healing network in the distribution grid positively impacts SAIDI indices, as it significantly reduces the number and duration of customer outages on the lines where it is installed. |
| Dissertation Institution |
Kauno technologijos universitetas. |
| Type |
Master thesis |
| Language |
Lithuanian |
| Publication date |
2023 |
