Sensor Fault-detection Algorithm on a AC/DC Converters for Microgrids based on Principal Component Analysis

The present academic research work proposes a method based on Principal Component Analysis (PCA) for the detection of failures in sensors related to local control of AC/ DC voltage converters in a micro-grid (MR) with the possibility of coupling to the net. In order to achieve the aforementioned, a simulation of a micro-grid is carried out with a variety of loads (linear and non-linear) and both conventional and renewable generators, as well as storage elements that together have DC and AC systems with their respective converters. The researchers propose two failure scenarios and a normal operating scenario that serves as a reference to carry out the analyzes. As a result of the, an algorithm has been implemented that, based on the main components of the mentioned cases, calculates differences between the spaces obtained from a total of 195 variables collected in all the bars of the system, in addition to the sensing for local control. The observations obtained are around 145000 value takes.