Abstract
In order to effectively compress the power system data and reduce the space occupied by data storage and transmission, this paper proposes a data compression method based on Latent Segmented ODE (LatSegODE) for the large-scale steady-state data generated during the working process of the power system. By training the ODE model using a large amount of steady-state data, a latent ODE model is generated to characterize the steady-state data of the power system, thereby significantly reducing the storage space occupied by the data. During the experimental stage, compression testing is carried out on the static data of the power system of a power grid company. According to the quantitative evaluation results of the two indices of normalized mean square error and data compression ratio, the proposed method has obvious compression advantages, and the compressed signals have a high degree of fitting to the actual sampled signal waveforms.
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