Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We firstly propose a distributed state estimator assuming regular system operation, that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to (i) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, (ii) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and (iii) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Increasing the robustness of autonomous systems to hardware degradation using machine learning

Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders’ requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9)

A dependability framework for WSN-based aquatic monitoring systems

Wireless Sensor Networks (WSN) are being progressively used in several application areas, particularly to collect data and monitor physical processes. Moreover, sensor nodes used in environmental monitoring applications, such as the aquatic sensor networks, are often subject to harsh environmental conditions while monitoring complex phenomena. Non-functional requirements, like reliability, security or availability, are increasingly important and must be accounted for in the application development. For that purpose, there is a large body of knowledge on dependability techniques for distributed systems, which provides a good basis to understand how to satisfy these non-functional requirements of WSN-based monitoring applications. Given the data-centric nature of monitoring applications, it is of particular importance to ensure that data is reliable or, more generically, that it has the necessary quality. The problem of ensuring the desired quality of data for dependable monitoring using WSNs is studied herein. With a dependability-oriented perspective, it is reviewed the possible impairments to dependability and the prominent existing solutions to solve or mitigate these impairments. Despite the variety of components that may form a WSN-based monitoring system, it is given particular attention to understanding which faults can affect sensors, how they can affect the quality of the information, and how this quality can be improved and quantified. Open research issues for the specific case of aquatic monitoring applications are also discussed. One of the challenges in achieving a dependable system behavior is to overcome the external disturbances affecting sensor measurements and detect the failure patterns in sensor data. This is a particular problem in environmental monitoring, due to the difficulty in distinguishing a faulty behavior from the representation of a natural phenomenon. Existing solutions for failure detection assume that physical processes can be accurately modeled, or that there are large deviations that may be detected using coarse techniques, or more commonly that it is a high-density sensor network with value redundant sensors. This thesis aims at defining a new methodology for dependable data quality in environmental monitoring systems, aiming to detect faulty measurements and increase the sensors data quality. The framework of the methodology is overviewed through a generically applicable design, which can be employed to any environment sensor network dataset. The methodology is evaluated in various datasets of different WSNs, where it is used machine learning to model each sensor behavior, exploiting the existence of correlated data provided by neighbor sensors. It is intended to explore the data fusion strategies in order to effectively detect potential failures for each sensor and, simultaneously, distinguish truly abnormal measurements from deviations due to natural phenomena. This is accomplished with the successful application of the methodology to detect and correct outliers, offset and drifting failures in real monitoring networks datasets. In the future, the methodology can be applied to optimize the data quality control processes of new and already operating monitoring networks, and assist in the networks maintenance operations.As redes de sensores sem fios (RSSF) têm vindo cada vez mais a serem utilizadas em diversas áreas de aplicação, em especial para monitorizar e capturar informação de processos físicos em meios naturais. Neste contexto, os sensores que estão em contacto direto com o respectivo meio ambiente, como por exemplo os sensores em meios aquáticos, estão sujeitos a condições adversas e complexas durante o seu funcionamento. Esta complexidade conduz à necessidade de considerarmos, durante o desenvolvimento destas redes, os requisitos não funcionais da confiabilidade, da segurança ou da disponibilidade elevada. Para percebermos como satisfazer estes requisitos da monitorização com base em RSSF para aplicações ambientais, já existe uma boa base de conhecimento sobre técnicas de confiabilidade em sistemas distribuídos. Devido ao foco na obtenção de dados deste tipo de aplicações de RSSF, é particularmente importante garantir que os dados obtidos na monitorização sejam confiáveis ou, de uma forma mais geral, que tenham a qualidade necessária para o objetivo pretendido. Esta tese estuda o problema de garantir a qualidade de dados necessária para uma monitorização confiável usando RSSF. Com o foco na confiabilidade, revemos os possíveis impedimentos à obtenção de dados confiáveis e as soluções existentes capazes de corrigir ou mitigar esses impedimentos. Apesar de existir uma grande variedade de componentes que formam ou podem formar um sistema de monitorização com base em RSSF, prestamos particular atenção à compreensão das possíveis faltas que podem afetar os sensores, a como estas faltas afetam a qualidade dos dados recolhidos pelos sensores e a como podemos melhorar os dados e quantificar a sua qualidade. Tendo em conta o caso específico dos sistemas de monitorização em meios aquáticos, discutimos ainda as várias linhas de investigação em aberto neste tópico. Um dos desafios para se atingir um sistema de monitorização confiável é a deteção da influência de fatores externos relacionados com o ambiente monitorizado, que afetam as medições obtidas pelos sensores, bem como a deteção de comportamentos de falha nas medições. Este desafio é um problema particular na monitorização em ambientes naturais adversos devido à dificuldade da distinção entre os comportamentos associados às falhas nos sensores e os comportamentos dos sensores afetados pela à influência de um evento natural. As soluções existentes para este problema, relacionadas com deteção de faltas, assumem que os processos físicos a monitorizar podem ser modelados de forma eficaz, ou que os comportamentos de falha são caraterizados por desvios elevados do comportamento expectável de forma a serem facilmente detetáveis. Mais frequentemente, as soluções assumem que as redes de sensores contêm um número suficientemente elevado de sensores na área monitorizada e, consequentemente, que existem sensores redundantes relativamente à medição. Esta tese tem como objetivo a definição de uma nova metodologia para a obtenção de qualidade de dados confiável em sistemas de monitorização ambientais, com o intuito de detetar a presença de faltas nas medições e aumentar a qualidade dos dados dos sensores. Esta metodologia tem uma estrutura genérica de forma a ser aplicada a uma qualquer rede de sensores ambiental ou ao respectivo conjunto de dados obtido pelos sensores desta. A metodologia é avaliada através de vários conjuntos de dados de diferentes RSSF, em que aplicámos técnicas de aprendizagem automática para modelar o comportamento de cada sensor, com base na exploração das correlações existentes entre os dados obtidos pelos sensores da rede. O objetivo é a aplicação de estratégias de fusão de dados para a deteção de potenciais falhas em cada sensor e, simultaneamente, a distinção de medições verdadeiramente defeituosas de desvios derivados de eventos naturais. Este objectivo é cumprido através da aplicação bem sucedida da metodologia para detetar e corrigir outliers, offsets e drifts em conjuntos de dados reais obtidos por redes de sensores. No futuro, a metodologia pode ser aplicada para otimizar os processos de controlo da qualidade de dados quer de novos sistemas de monitorização, quer de redes de sensores já em funcionamento, bem como para auxiliar operações de manutenção das redes.Laboratório Nacional de Engenharia Civi