Power quality time series data mining using S-transform and fuzzy expert system

This paper presents new approach for time series data classification using Fuzzy Expert System (FES). In the proposed study, the power disturbance signals are considered as time series data for testing the designed FES. Initially the time series data are pre-processed through the advanced signal processing tool such as S-transform and various statistical features are extracted, which are used as inputs to the FES. The FES output is optimized i1sing Particle Swann Optimization (PSO) to bring the output to distinct classification level. Both Gaussian and trapezoidal membership functions are selected for designing the proposed FES arid the performance measure is derived by comparing the classification rates for the time series data without noise and with noise up to SNR 20 db. The proposed algorithm provides accurate classification rates even under noisy conditions compared to the existing techniques, which shows the efficacy and robustness of the proposed algorithm for time series data classification.