Voltage quality problems such as voltage sag, swell, flicker, undervoltage, and overvoltage have been of great concern for both utilities and customers over the last decade. In this paper, a new approach based on the $H_{infty}$ algorithm to monitor voltage disturbances is presented. The key idea of this approach is to estimate the amplitude of the fundamental component of distorted and noisy voltage waveform instantaneously, and then the information can be extracted from the estimated envelope to identify and classify different voltage related power quality problems. The $H_{infty}$ algorithm is characterized by a fast tracking, unlike that of existing techniques. The $H_{infty}$ algorithm outperforms the Kalman Filter (KF) by its fast convergence and robust tracking performance against non-Gaussian noise. The paper investigates the effects of various types of noise on the performance of the $H_{infty}$ algorithm. Digital simulation results confirm the validity and accuracy of the proposed method. The proposed $H_{infty}$ algorithm is examined by tracking the flicker produced by a resistance welder simulated in the PSCAD/EMTDC package.