The invention discloses an
intelligent decision-making method for a power
system based on case reasoning, and solves the problems of strong uncertainty of a
power grid under the access of high-proportion
renewable energy sources and the like. A quintuple
case model is constructed, including
system states, effect scores and the like, and
power grid state characterization is realized through multi-
modal feature fusion and a spatial-temporal
feature extraction network; a three-level retrieval framework is adopted, and the retrieval efficiency is improved by combining mixed similarity calculation and constraint
pruning; designing a correction mechanism with physical constraint compensation, and dynamically adjusting a compensation coefficient by means of
fuzzy logic; and an incremental and density peak clustering case
library optimization strategy is provided. The
system adopts data and an
algorithm, applies a three-level distributed architecture, and supports
mass data storage and real-time reasoning. The provincial
power grid application shows that the decision
generation time is reduced from 87.2 + / -12.5 ms to 14.8 + / -3.2 ms, the
voltage recovery success rate is increased to 94.3%, the constraint violation rate is reduced to 2.1%, and the decision efficiency and safety of the
new energy power grid are improved.