Operation, Monitoring, and Protection of Future Power Systems: Advanced Congestion Forecast and Dynamic State Estimation Applications

The electrical power systems are undergoing drastic changes such as increasing levels of renewable energy sources, energy storage, electrification of energy-efficient loads such as heat pumps and electric vehicles, demand-side resources, etc., in the last decade, and more changes will be followed in the near future. The emergence of digitalization and advanced communication in the case of distribution systems to enhance the performance of the electricity infrastructure also adds further complexities. These changes pose challenges such as increased levels of network congestion, voltage variations, protection mis-operations, increased needs for real-time monitoring, and improved planning practices of the system operators. These challenges will require the development of new paradigms to operate the power grids securely, safely, and economically. This thesis attempted to address those challenges and had the following main contributions:First, the thesis started by presenting a comprehensive assessment framework to address the distribution system operators’ future-readiness and help the distribution system operators to determine the current status of their network infrastructures, business models, and policies and thus identify the pathways for the required developments for the smooth transition towards future intelligent distribution grids.Second, the thesis presents an advanced congestion forecast tool that would support the distribution system operators to forecast and visualize network congestion and voltage variations issues for multiple forecasting horizons ranging from close-to-real time to a day-ahead. The tool is based on a probabilistic power flow that incorporates forecasts of solar photovoltaic production and electricity demand, combined with advanced load models and different operating modes of solar photovoltaic inverters. The tool has been integrated to an existing industrial graded distribution management system via an IoT platform Codex Smart Edge of Atos Worldgrid. The results from case studies demonstrated that the tool performs satisfactorily for both small and large networks and can visualise the cumulative probabilities of network congestion and voltage variations for a variety of forecast horizons as desired by the distribution system operator.Third, a dynamic state estimation-based protection scheme for the transmission lines which does not require complicated relay settings and coordination has been demonstrated using an experimental setup at Chalmers power system laboratory. The scheme makes use of the real-time measurements provided by advanced sensors which are developed by Smart State Technology, The Netherlands. The experimental validations of the scheme have been performed under different fault types and conditions, e.g., unbalanced faults, three-phase faults, high impedance faults, hidden failures, inductive load conditions, etc. The results have shown that the scheme performs adequately in both normal and fault conditions and thus the scheme would work for transmission line protection by avoiding relay coordination and settings issues.Finally, the thesis presents a decentralized dynamic state estimation method for estimating the dynamic states of a transmission line in real-time. This method utilizes the sampled measurements from the local end of a transmission line, and thereafter dynamic state estimation is performed by employing an unscented Kalman filter. The advantage of the method is that the remote end state variables of a transmission line can be estimated using only the local end variables and, hence, the need for communication infrastructure is eliminated. Furthermore, an exact nonlinear model of the transmission line is utilized and the dynamic state estimation of one transmission line is independent of the other lines. These features in turn result in reduced complexity, higher accuracy, and easier implementation of the decentralized estimator. The method is envisioned to have potential applications in transmission line monitoring, control, and protection

Named Data Networking in Vehicular Ad hoc Networks: State-of-the-Art and Challenges

International audienceInformation-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community

Development of a Framework for Preliminary Risk Analysis in Transportation Projects

Over the years, risk analysis methodologies have been developed and implemented by many industries. NASA has implemented a cost efficient Continuous Risk Analysis methodology with good results. The U. S. Department of Transportation also states that a continuous risk analysis is the key in identifying, addressing, and handling risks before they become threats to success. However, current practices seldom incorporate this concept into real transportation projects. In general, risk is simply disregarded in feasibility studies. One of primary reasons is the lack of a feasible and effective risk analysis approach to guide efficient implementation in real projects. This thesis reviews current risk analysis practices used in public transportation projects. Using a case study, it also explores potential obstacles encountered in the implementation of systematic risk analysis. Finally, this thesis presents a preliminary risk analysis framework developed through the case study and enriched subsequently by incorporating material documented in the literature. The proposed risk analysis approach is to help achieve continuous risk analysis in transportation projects by enabling early start, frequent implementation, extensive application and flexible adoption

Evaluation of topic-based adaptation and student modeling in QuizGuide

This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning

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