A simple pattern recognition-based method for Power Quality disturbance detection

Abstract

Voltage disturbances are the most frequent cause of a large range of disruption in industrial, commercial, and residential power supply systems. These disturbances are often referred to as power quality problems and affect the Power Systems causing substantial losses. To avoid the storage of a large amount of data, the first task in monitoring the power quality is the realtime detection of disturbances, which must be performed by an accurate and low-complexity system. This paper proposes a low-complexity system for power quality disturbance detection. The method makes innovative use of simple features extracted from reduced segments of the monitored voltage waveform. The extract features (the mean value, variance, energy, and the maximum and minimum values of the filtered voltage signals) require low computational effort and allow a considerable dimensional reduction of the signals, leading to simple detection algorithms. The proposed method achieves high detection rates on both simulated and real signals.