The Concept of the Deep Learning-Based System "Artificial Dispatcher" to Power System Control and Dispatch
This addresses the risk of dangerous conditions in power systems for operators and infrastructure, but it is incremental as it builds on existing deep learning and game theory approaches.
The paper tackles the problem of insufficient effectiveness in controlling normal and emergency conditions in modern power systems by proposing an 'Artificial Dispatcher' system that combines human intelligence with the speed of automatic devices using deep learning and game theory. The result is a system designed to bring power systems into normal or post-emergency states through required control actions.
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 dispatcher 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 dispatcher's 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.