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Power quality has become a great concern to all electricity consumers. Poor power quality can cause equipment failure, data and economical losses. An automated monitoring system is needed to ensure signal quality, reduce diagnostic time and rectify failures. This paper presents the detection and classification of power quality signals using bilinear time-frequency distribution which is smooth-windowed Wigner-Ville distribution (SWWVD). The power quality signals focused are swell, sag, interruption, harmonic, interharmonic and transient based on IEEE Std. 1159-2009. The technique represents signal jointly in time-frequency representation (TFR) with high frequency and time resolution. Thus, it is very appropriate to analyze the signals that consist of multi-frequency components and magnitude variations. From the TFR, signal characteristics are calculated and used as input for signal classifier. To verify the system performance, 100 signals with various characteristics for each type of power quality signal are generated and classified at SNR from 0 to 40 dB by simulation using MATLAB. It shows that the system gives 100% percent correct classification at 36dB of SNR for all power quality signals.