ABSTRACT
Uninterruptible power supply is the main objective of power utility companies that identify and locate different types of fault as quickly as possible to protect the power system from complete blackouts using intelligent techniques. Therefore, this study presents a novel method for the detection of fault disturbances based on Wavelet Transform (WT) and Independent Component Analysis (ICA). The voltage signal is taken offline under fault conditions and is processed using wavelet and ICA for analysis. The time–frequency resolution of WT detects the fault initiation event in the signal. Again, a performance index is calculated from the ICA under fault conditions to detect fault disturbances in the voltage signal. The proposed approach is tested to be robust enough under various operating scenarios such as without noise, with 20-dB noise, and under frequency variation conditions. Furthermore, the detection study is carried out using a performance index, energy content, by applying the existing Fourier Transform (FT), Short-Time Fourier Transform (STFT), and the proposed wavelet transform. Fault disturbances are detected if the energy calculated in each scenario is higher than the corresponding threshold value. The study of fault detection is simulated in MATLAB/Simulink in a typical power system.
