Abstract
Power quality issues can affect the performance of devices powered by the grid and can, in severe cases, permanently damage connected devices. Events that affect power quality include sags, swells, waveform distortions and transients. Transients are one of the most common power quality disturbances and are caused by lightning strikes or switching activities among power-grid-connected systems and devices. Transients can reach very high magnitudes, and their duration spans from nanoseconds to milliseconds. This study proposed a deep-learning-based technique that was supported by convolutional neural networks and a bidirectional long short-term memory approach in order to detect and characterize power-quality transients. The method was validated (i.e., benchmarked) using an alternative algorithm that had been previously validated according to a digital high-pass filter and a morphological closing operation. The training and performance assessments were carried out using actual power-grid-measured data and events.