Parametric compression algorithm for power system steady data

A parametric compression algorithm based on adaptive-network-based fuzzy inference system (ANFIS) model was proposed for the vast amounts of steady data in power system. The equiphase acquisition devices improve the steady-state data cycle sliding step phenomenon effectively. A mathematical model of equiphase data sampling was established to provide conditions for the parametric compression algorithm. The algorithm structure was designed and detailed process was described. ANFIS is employed to create the data model in the algorithm, which is used to reconstruct sampling data, and cubic spline interpolation is employed to establish reference phase model and then to calculate phase difference of each cycle. Finally, simulating and experimental data show the impacts of different factors on compression ratio (CR) and signal noise ratio (SNR) in order to provide references for the parameters selected. It proves that this algorithm can greatly improve the compression ratio and de-noising effect. This paper demonstrates a new approach for data compression in power system.