Big Data-Driven Active Optimization Algorithm for Grid Fault Disposal Plan Assisted Decision Logic

Abstract

In the current active optimisation of the grid fault disposal plan auxiliary decision logic, the logical relationships are more ambiguous, resulting in a low accuracy of the active optimisation results. To this end, a big data-driven active optimisation algorithm is proposed for the auxiliary decision logic of grid fault handling plans. Deep level access to grid fault information. Integration of fault type disposal plans. Develop auxiliary decision logic based on big data, extract keywords and correspond to fault states logically. Generate an active optimisation algorithm for the auxiliary decision logic. The experiments show that the average detection rate of the method is 95.14%, which is a substantial improvement and has high application value.