**Acknowledgements**

*Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*

around the eight-second a disturbance is presented. Also, in **Figure 11b** is shown a zoom of such specific area in which the disturbance is presented and, it can be appreciated that the disturbance has the specific characteristics that belong to the occurrence of sag. In this sense, it should be highlighted that the effectiveness of the proposed novelty detection methodology has been proved by analyzing real data acquired from a real scenario that includes the monitoring of a transformer.

*Voltage signal acquired during the monitoring of the electric transformer in which a novelty detection is detected. (a) Complete voltage signal, (b) zoom over the specific area in which the disturbance is detected and* 

This chapter proposes a novelty detection methodology based on Self-Organizing Maps to perform the monitoring of Power Quality. The obtained result proves the effectiveness of the proposed method for detecting the occurrence of unexpected and undesirable electric power disturbances such as sag, swell, and

Thus, two main important key points must be highlighted from this proposal. First, the characterization of the electric power signals through statistical timedomain based features leads to achieving a high-performance representation of the data distribution. Second, the modeling of the available data by means of SOM's neuron grids allows preserving the topology of the data, which is a key feature that leading the detection of novelty events. Additionally, the consideration of a collaborative SOM neuron structure based on the analysis of the mean quantization error effectively detects all novel electric power disturbances

**36**

**5. Conclusions**

**Figure 11.**

*identified as sag.*

fluctuations.

considered.

This research work has been partially supported by the FONDEC-UAQ-2019 under the registered project FIN202011.
