Solutions for detection of non-technical losses in the electricity grid: a review

This paper is a review of literature with an analysis on a selection of scienti c studies for detection of non-technical losses. Non-technical losses occurring in the electric grid at level of transmission or of distribution have negative impact on economies, affecting utilities, paying consumers and states. The paper is concerned with the lines of research pursued, the main techniques used and the limitations on current solutions. Also, a typology for the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identifi ed. The selected literature covers a wide range of solutions associated with non-technical losses. Of the 103 selected studies, 6 are theoretical, 25 propose hardware solutions and 72 propose non-hardware solutions. Data based classi cation models and data from consumption with high resolution are respectively required in about 47% and 35% of the reported solutions. Available solutions cover a wide range of cases, with the main limitation found being the lack of an uni ed solution, which enables the detection of all kinds of non-technical losses

Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid

The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices interconnected via communication networks expose unprecedented vulnerabilities, thereby increasing the potential attack surface. This dissertation is aimed at developing novel game-theoretic and machine-learning techniques for addressing the reliability and security issues residing at multiple layers of the smart grid, including power distribution system reliability forecasting, risk assessment of cyber-physical attacks targeted at the grid, and cyber attack detection in the Advanced Metering Infrastructure (AMI) and renewable resources. This dissertation first comprehensively investigates the combined effect of various weather parameters on the reliability performance of the smart grid, and proposes a multilayer perceptron (MLP)-based framework to forecast the daily number of power interruptions in the distribution system using time series of common weather data. Regarding evaluating the risk of cyber-physical attacks faced by the smart grid, a stochastic budget allocation game is proposed to analyze the strategic interactions between a malicious attacker and the grid defender. A reinforcement learning algorithm is developed to enable the two players to reach a game equilibrium, where the optimal budget allocation strategies of the two players, in terms of attacking/protecting the critical elements of the grid, can be obtained. In addition, the risk of the cyber-physical attack can be derived based on the successful attack probability to various grid elements. Furthermore, this dissertation develops a multimodal data-driven framework for the cyber attack detection in the power distribution system integrated with renewable resources. This approach introduces the spare feature learning into an ensemble classifier for improving the detection efficiency, and implements the spatiotemporal correlation analysis for differentiating the attacked renewable energy measurements from fault scenarios. Numerical results based on the IEEE 34-bus system show that the proposed framework achieves the most accurate detection of cyber attacks reported in the literature. To address the electricity theft in the AMI, a Distributed Intelligent Framework for Electricity Theft Detection (DIFETD) is proposed, which is equipped with Benford’s analysis for initial diagnostics on large smart meter data. A Stackelberg game between utility and multiple electricity thieves is then formulated to model the electricity theft actions. Finally, a Likelihood Ratio Test (LRT) is utilized to detect potentially fraudulent meters

Fake Currency Detection using Image Processing

In recent years, a lot of illegal counterfeiting rings manufacture and sell fake coins and at the same time fake note currency is printed as well, which have caused great loss and damage to the society. Thus it is imperative to be able to detect fake currency. We propose a new approach to detect fake Indian notes using their images. A currency image is represented in the dissimilarity space, which is a vector space constructed by comparing the image with a set of prototypes. Each dimension measures the dissimilarity between the image under consideration and a prototype. In order to obtain the dissimilarity between two images, the local key points on each image are detected and described. Based on the characteristics of the currency, the matched key points between the two images can be identified in an efficient manner. A post processing procedure is further proposed to remove mismatched key points. Due to the limited number of fake currency in real life, SVM is conducted for fake currency detection, so only genuine currency are needed to train the classifier

Analysis of human-computer interaction time series using Deep Learning

Dissertação de mestrado integrado em Engenharia InformáticaThe collection and use of data resulting from human-computer interaction are becoming more and more common. These have been allowing for the birth of intelligent systems that extract powerful knowledge, potentially improving the user experience or even originating various digital services. With the rapid scientific advancements that have been taking place in the field of Deep Learning, it is convenient to review the underlying techniques currently used in these systems. In this work, we propose an approach to the general task of analyzing such interactions in the form of time series, using Deep Learning. We then rely on this approach to develop an anti-cheating system for video games using only keyboard and mouse input data. This system can work with any video game, and with minor adjustments, it can be easily adapted to new platforms (such as mobile and gaming consoles). Experiments suggest that analyzing HCI time series data with deep learning yields better results while providing solutions that do not rely highly on domain knowledge as traditional systems.A recolha e a utilização de dados resultantes da interação humano-computador estão a tornar-se cada vez mais comuns. Estas têm permitido o surgimento de sistemas inteligentes capazes de extrair conhecimento ex tremamente útil, potencialmente melhorando a experiência do utilizador ou mesmo originando diversos serviços digitais. Com os acelerados avanços científicos na área do Deep Learning, torna-se conveniente rever as técni cas subjacentes a estes sistemas. Neste trabalho, propomos uma abordagem ao problema geral de analisar tais interações na forma de séries temporais, utilizando Deep Learning. Apoiamo-nos então nesta abordagem para desenvolver um sistema de anti-cheating para videojogos, utilizando apenas dados de input de rato e teclado. Este sistema funciona com qualquer jogo e pode, com pequenos ajustes, ser adaptado para novas plataformas (como dispositivos móveis ou consolas). As experiências sugerem que analisar dados de séries temporais de interação humano-computador pro duz melhores resultados, disponibilizando soluções que não são altamente dependentes de conhecimento de domínio como sistemas tradicionais