Abstract: A method and system for online decision making of generator start-up, determining the lines to be restored step by step based on the real-time power system data to guide the initial stage. The black-start unit is determined, and the units waiting to be restored are selected from all units. The training set including labeled samples to establish a value network. During generator start-up, the blackout area is judged, the state of power system is identified, the availability of equipment is judged, and the characteristics of generators are collected. The total generation capability is used as search objective for Monte Carlo tree search. Based on the value network, Monte Carlo is applied to search the next line to be restored during generator start-up. Parallel computation adopted to check the constraints for the restoration of alternative lines. The results of Monte Carlo are summarized to determine the line to be restored next.

Abstract: A method and system for online decision making of generator start-up, determining the lines to be restored step by step based on the real-time power system data to guide the initial stage. The black-start unit is determined, and the units waiting to be restored are selected from all units. The training set including labeled samples to establish a value network. During generator start-up, the blackout area is judged, the state of power system is identified, the availability of equipment is judged, and the characteristics of generators are collected. The total generation capability is used as search objective for Monte Carlo tree search. Based on the value network, Monte Carlo is applied to search the next line to be restored during generator start-up. Parallel computation adopted to check the constraints for the restoration of alternative lines. The results of Monte Carlo are summarized to determine the line to be restored next.