Name
Title | ||
|---|---|---|
The Concept of the Deep Learning-Based System "Artificial Dispatcher" to Power System Control and Dispatch. |
Abstract | ||
|---|---|---|
Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the and work of special automatic devices proves to be insufficiently effective more and more frequently, which raises risks of dangerous and emergency conditions in power systems. The paper is aimed at compensating for the shortcomings of man (a cognitive barrier, exposure to stresses and so on) and automatic devices by combining their strong points, i.e. the dispatcheru0027s intelligence and the speed of automatic devices by virtue of development of the intelligent system Artificial dispatcher on the basis of deep machine learning technology. For realization of the system Artificial dispatcher in addition to deep learning it is planned to attract the game theory approaches to formalize work of the up-to-date power system as a game problem. The gain for Artificial dispatcher will consist in bringing in a power system in the normal steady-state or post-emergency conditions by means of the required control actions. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computers and Society | Computer science,Electric power system,Knowledge management,Human–computer interaction,Game theory,Artificial intelligence,Control system,Deep learning,Cognition |
DocType | Volume | Citations |
Journal | abs/1805.05408 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikita V. Tomin | 1 | 7 | 4.25 |
| Victor G. Kurbatsky | 2 | 5 | 3.69 |
| Michael Negnevitsky | 3 | 46 | 16.72 |