ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid

Desarrollo e implementación de una herramienta de análisis de secuencias de acciones

Este documento presenta el Trabajo de Fin de Grado que he realizado en la Universidad Carlos III de Madrid, en la Escuela Politécnica de Leganés. El proyecto que se ha desarrollado tiene como objetivo la automatización de la creación de una estructura arbórea (trie) que permite almacenar y analizar secuencias de acciones en dominio de datos secuenciales. En el documento se describe toda la realización y estructura del software implementado, así como la experimentación que se ha realizado como demostración del funcionamiento del mismo. Gracias a este software se permite de forma automática crear tries y analizarlos, con un enfoque gráfico y estadístico. La utilización del trie como estructura de representación de datos permite utilizar un enfoque estadístico para el análisis de secuencias de datos y la extracción de patrones. El software diseñado es genérico, por lo que puede ser utilizado en cualquier dominio, facilitando enormemente la labor de los investigadores.This document presents the Final Degree Work that I have done for the University Carlos III de Madrid, in the Polytechnic School of Leganés. The developed project is aimed to the automatic creation of a tree structure (trie) that allows to store and to analyze sequences of actions in a sequential data domain. In the document is described all of the work done and the structure of the implemented software, as well as the performed experimentation as demonstration of the functionality of the software system. Thanks to this software, automatic creation and analysis of tries are done with a statistical and graphical approach. The usage of tries as structure to represent data allows to create a statistical approach to analyze data sequences and to perform pattern recognition. The design software is generic, so it can be used in any domain, facilitating the work of researchers.Ingeniería Informátic