Anticipating failures is vital for maintaining a reliable power supply. Advanced measurement devices in the grid generate vast data that contains valuable information on grid operations. Initial signatures of an incipient failure are often reflected in this data in the form of electrical waveform distortions. Conventional protection schemes are not equipped to analyze these distortions and anticipate failures. There is a considerable research gap for a simple yet robust and universal failure anticipation and diagnosis scheme. This paper proposes a universal Failure Anticipation and Diagnosis Scheme (FADS) to detect incipient failures in AC distribution grids. The method comprises three short stages, helping the operator make an informed decision. In the first stage, the FADS scheme leverages the fundamental properties of electrical sinusoid waveforms to detect distortions. In the second stage, the distortion data is processed through pre-determined thresholds set in accordance with the system's regular operation. In the third stage, depending on the system, the FADS uses the extent of the violations of these thresholds and ranks the severity of the danger posed to grid operations. The classification helps determine if the waveform distortions are the signature of an incipient failure. The proposed FADS method's reliability, robustness and effectiveness are evaluated in incipient failure conditions of field events modelled in real-time simulations on standardized IEEE distribution feeders. The FADS is a high-speed distortion detector, is quite sensitive, and the method has high selectivity because of its nature.@en

Continuous and rapid technological advancements have transformed the modern day power system. Increased global inter-connectivity has made reliable power supply a critical requirement. A small outage can cascade into a blackout causing great inconvenience and significant monetary damage. These concerns highlight the need of an additional layer of proactive approach in conventional protection schemes. The focus of such an approach would be to shift from reacting to a failure to anticipating a failure. Anticipating a failure gives time to better prepare and mitigate the failure by efficient allocation of resources in order to limit the negative consequences. Starting from the inception of the event causing the failure to the final occurrence of the failure, the time-period in between is termed as the pre-failure period where the signatures of the incipient failure can be observed. The availability of high-resolution devices has improved monitoring of grid operations during this pre-failure period. Improved monitoring enhances situational awareness leading to easier detection of incipient failure signatures. Research conducted in this field has led to development of few failure anticipation techniques but the application potential of some are restricted to specific equipment or phenomena while that of others are restricted by resource requirements. There is a need of addressing the research gap of a comprehensive failure anticipation technique that fulfills three major criteria of low computational burden, wide applicability in different scenarios and installation compatibility with existing grid monitoring devices for economical implementation. The research conducted in this thesis aims to address this research gap by developing a comprehensive failure anticipation technology titled Failure Anticipation and Diagnosis Scheme (FADS) for AC distribution systems. FADS implementation broadly comprises of three functionalities. The first functionality is concerned with quick and accurate identification of incipient failure signatures. Almost all failure anticipation techniques rely on cross-referencing historical databases or identifying specific patterns in order to detect incipient failure signatures. However, incipient failure signatures seldom manifest in same patterns. Hence, FADS relies on the fundamental aspect that pure AC sinusoids are complex exponentials. Incipient failure signatures would invariably violate certain properties of complex exponentials and manifest as waveform distortions, which would be leveraged by FADS to detect the signatures. The second functionality involves the data processing of the distortion data. Several novel parameters are introduced in the second functionality that helps in processing the data obtained from the first functionality. The use of novel parameters helps in accurate assessment of the stress experienced by the grid operations due to the event causing distortions. Such an assessment help FADS to be robust to false positives or false negatives. Finally, the third functionality involves interpretation of the information obtained through data processing. The interpretation provides metrics to rank the severity of the damage the event can inflict on grid operations along with specific inputs on the event location. This interpreted and refined data helps to provide means to the Distribution System Operator (DSO) for informed decision-making and time-efficient resource allocation for failure mitigation purposes. The different FADS functionalities work in unison to detect incipient failure signatures and extract valuable information, which can be then used to plan mitigation strategies. The different functionalities of FADS are designed to be installed in a manner such that the incremental costs of widespread FADS implementation are minimal. The evaluation of FADS in this thesis is conducted through a series of stringent and realistic test cases. The test cases are simulated on the standard IEEE-13 and IEEE-34 node test feeders. The first set of simulation studies focusses on detecting High Impedance Faults (HIF) as conventional protection schemes mostly fail to detect it. The test cases comprise of several novel stringent scenarios to evaluate the capability of FADS to accurately distinguish and detect HIF events among multiple switching events and normal grid actions at different sections of the grid. The second set of simulation studies involves recreating transient behavior generated due to real life incipient equipment failure conditions in laboratory based simulations. Simulations are used to evaluate the ability of FADS to detect and assess the incipient failure before the equipment breakdown occurs. The next set of studies is focused on analyzing how the FADS performance in previous simulation studies could be translated to assess the improvement in major reliability indices, mainly System Average Interruption Duration Index (SAIDI). Improvement in reliability indices are a major area of concern for utilities and the results obtained from FADS implementation are further quantified to provide a range of possible improvement in SAIDI value in percentage terms. Finally, the proposed benefit of FADS is illustrated through implementation on real field data provided by the Dutch DSO, Stedin B.V. In the course of FADS implementation, few shortcomings were noticed and possibilities of further improvements were identified. The final chapters of this thesis discuss the shortcomings and recommend improvements for future research studies. The functionalities of FADS are flexible and mostly user-dependent and can be systematically improved over time to make FADS a global standard for industrial and research applications for failure anticipation in AC distribution systems.@en

This chapter provides a brief overview of modelling and simulation approaches for smart grid systems. A special focus is put onto the coupling of simulation environments; i.e., the co-simulation of power systems and its components. Furthermore, selected implementations of standardized interfaces for domain simulators covering the domains of power systems and ICT are presented.@en

Reliable power supply to the consumers is the key goal for power utilities. The two key reliability indices for power utilities are SAIDI and SAIFI. These indices indicate the average duration and frequency of supply interruptions for the consumer. A low value on these indices reflects a reliable and mostly uninterrupted power supply. Reliability of the power supply is most threatened by events such as incipient failures that often go undetected by conventional protection schemes and can significantly increase average outage duration. However, such events initially manifest in terms of distortions in current and voltage waveforms. In this paper, waveform distortions are leveraged to detect and locate such events before they cascade to cause further damage. Early detection and localization will help in planning a swift response and mitigation of risk. Such a timely action will reduce the outage duration and interruption frequency, eventually leading to improvement in grid reliability indices. The paper further investigates these concepts by evaluating case studies on an IEEE-34 Node Distribution feeder test system in Real Time.@en

Power system operation is of vital importance and must be developed far beyond today’s practice to meet future needs. Almost all European countries are facing an abrupt and very important increase of renewables with intrinsically varying yields which are difficult to predict. In addition, an increase of new types of electric loads and a reduction of traditional production from bulk generation can be observed as well. Hence, the level of complexity of system operation steadily increases. Because of these developments, the traditional power system is being transformed into a smart grid. Previous and ongoing research has tended to focus on how specific aspects of smart grids can be developed and validated, but until now there exists no integrated approach for analysing and evaluating complex smart grid configurations. To tackle these research and development needs, a pan-European research infrastructure is realized in the ERIGrid project that supports the technology development as well as the roll-out of smart grid technologies and solutions. This paper provides an overview of the main results of ERIGrid which have been achieved during the last four years. Also, experiences and lessons learned are discussed and an outlook to future research needs is provided.

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A High Impedance Fault (HIF) in the power distribution systems remains mostly undetected by conventional protection schemes due to low fault currents. Apart from degrading the reliability of power supply to customers, HIF can impose a high cost on the utilities due to technical damages. The nonlinear and asymmetric nature of HIF makes its detection and identification very challenging. HIF signatures are in the form of minute-level distortions in the observable AC sinusoidal voltage and current waveforms but these signatures do not follow a clear pattern. In this paper, we present Advanced Distortion Detection Technique (ADDT), based on waveform analytics to distinguish and detect HIF. In addition, the ADDT analysis provides a fair assessment about the location and severity of HIF for efficient decision-making at the DSO level. ADDT is computationally lightweight and can be implemented in actual relays, hence it is enabled to provide an easy and cost-effective solution to HIF detection issues. ADDT robustness is tested in several simulation cases of interest using the IEEE-34 and IEEE-13 distribution test feeder systems in RTDS power system simulator. The test results successfully demonstrate the effectiveness and robustness of ADDT.

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For smart grid assessment one needs to simulate varieties of components in different software environments. However, the existing simulation tools are domain oriented and cannot fulfil this need natively. Therefore, a smart grid simulation environment has to be established with accordingly accurate models for intra and inter-domain elements as well as interfaces and framework for coordination of those models in a holistic scenario. This paper presents part of the development of this smart grid simulation environment by implementing co-simulation interface for PSCAD using the mosaik framework based on functional mock-up interface (FMI). This co-simulation interface is tested using a modified dynamic model of IEEE 9 bus test system simulated in PSCAD while the wind turbine controller is simulated in MATLAB/Simulink. The results show a significant advantage over alternative methods in terms of a reduction in simulation runtime and compatibility with different simulation environments.@en

Work Package JRA2 has developed advanced simulation-based methods to check and validateSmart Grid scenarios, configurations and applications. The required models cover the various partsof Smart Grids, such as power system infrastructures, control algorithms, communication systems,market aspects or/and external factors like weather and people. The challenge is that the individualmodels of these parts are of very different nature (continuous, discrete, stochastic, etc.). From thetechnical perspective, this challenge has been overcome with the help of offline co-simulation, wherethe most suitable simulation tools for all considered domain can be coupled via the Functional Mockup Interface specification. From the methodological perspective, this challenge has been addressedby basing the work in JRA2 on ERIGrid’s holistic testing procedure.@en

This report summarizes the work conducted within ERIGrid related to an integrated simulation environment for large-scale systems.The main goal of the JRA2 is to develop advanced simulation-based tools and methods to validate Smart Grid scenarios, configurations and applications in con-text of co-simulation. The work done in D-JRA2.1 involved assessment of specialized simulation packages for Smart Grids and to develop tools to couple these simulation packages for co-simulation. New tools and models were also developed as some of the existing tools were not sufficient enough to achieve the appropriate couplings. In D-JRA2.2 co-simulation-based assessment methods were developed to compare the performance between monolithic and co-simulations. In D-JRA2.3 we aim to combine all the work done under WP JRA2 to present an integrated simulation package that can be applied to Large Scale systems. The assessment methods developed in D-JRA2.2 have been tested initially in small systems to measure the performance and identify possible flaws. How-ever, the complexity increases significantly in large scale realistic systems. This report documents the challenges faced when the systems and their models grow larger (i.e., upscaled) and how different large scale specific phenomena and issues were identified. After the identification of the challenges, the assessment methods were modified and packaged into an in-tegrated simulation environment which can be used for scaled out systems. The simulation pack-ages are provided as an addendum along with this report while their details are concisely docu-mented in this report.@en

Due to the increased deployment of renewable energy sources and intelligent components the electric power system will exhibit a large degree of heterogeneity, which requires inclusive and multi-disciplinary system assessment. The concept of co-simulation is a very attractive option to achieve this; each domain-specific subsystem can be addressed via its own specialized simulation tool. The applicability, however, depends on aspects like standardised interfaces, automated case creation, initialisation, and the scalability of the co-simulation itself. This work deals with the inclusion of the Functional Mock-up Interface for co-simulation into the DIgSILENT PowerFactory simulator, and tests its accuracy, implementation, and scalability for the grid connection study of a wind power plant. The coupling between the RMS mode of PowerFactory and MATLAB/Simulink in a standardised manner is shown. This approach allows a straightforward inclusion of black-boxed modelling, is easily scalable in size, quantity, and component type.@en

As future power systems become increasingly complex and interconnected to other energy carriers, a single research infrastructure can rarely provide the required test-beds to study a complete energy system, especially if different types of real power hardware are expected to be in-the-loop. Therefore, virtual interconnection of laboratories for large-scale systems plays an important role for geographically distributed realtime simulation. This paper presents the improvements made in simulation fidelity as well as usability for establishing future simulator and laboratory connections. A general procedure is proposed and analyzed for geographically distributed real-time simulation, which allows users easily to adapt this procedure to specific test cases. A systematic and comprehensive analysis of a dynamic phasor based co-simulation interface algorithm and its improvements are provided to demonstrate the advantages as well as limitations of this approach.@en

Key contributor to normal power grid operations is optimal working of the various power grid equipment/apparatus. Nonoptimal operation of any of this equipment causes power quality problems which can pose great risk to the stability of the grid. Damaged or partially damaged equipment leaves characteristic signatures in the form of current and voltage waveform distortions. Detecting and localizing such signal distortions would contribute to grid reliability as the damaged equipment could be replaced in time before it can cause further damage. This paper proposes a Distortion Detection Technique (DDT) based on second-difference approach. This distortion detection technique has very low memory requirements and can be easily implemented on decentralized systems. The paper investigates the performance of this technique and evaluates it with case studies involving different kind of equipment failures simulated on Real Time Digital Simulator (RTDS).@en

Deviations from normal power grid operations, such as incipient faults, equipment damage, or weather related effects, have characteristic signatures in the current and voltage waveforms. Detecting and classifying such signal distortions as quick as possible can contribute to grid reliability since grid events can be responded to in time, i.e. before they lead to an outage. This paper proposes a new distortion detection algorithm, based on computationally very lightweight operations. The method does not require large datasets, has a small memory footprint, and therefore can be easily implemented on decentralized, embedded systems. This detection method constitutes the core of an overarching algorithm which accurately classifies the event even in case of a malfunctioning device and normal switching action. The paper investigates the performance of this new algorithm and evaluates it with four case studies for High Impedance Faults occurring on an IEEE 9 bus system.@en

High Impedance Fault (HIF) is a low fault current event which cannot always be efficiently detected or cleared by conventional protection systems. Voltage or current signal distortions are often a possible indicator of HIF signatures which need to be carefully analyzed. This paper proposes a new technique based on second-difference approach to detect signal distortions. The technique does not require large database and is computationally very lightweight. The performance of the proposed technique is compared against commercial protection relays connected in Hardware-in-Loop (HIL) mode to a Real Time Digital Simulator (RTDS) simulating HIF in an IEEE 9-bus system. Test result evaluation show that the proposed technique is accurate and dependable.@en

A driving force for the realization of a sustainable energy supply in Europe is the integration of distributed, renewable energy resources. Due to their dynamic and stochastic generation behaviour, utilities and network operators are confronted with a more complex operation of the underlying distribution grids. Additionally, due to the higher flexibility on the consumer side through partly controllable loads, ongoing changes of regulatory rules, technology developments, and the liberalization of energy markets, the system’s operation needs adaptation. Sophisticated design approaches together with proper operational concepts and intelligent automation provide the basis to turn the existing power system into an intelligent entity, a so-called smart grid. While reaping the benefits that come along with those intelligent behaviours, it is expected that the system-level testing will play a significantly larger role in the development of future solutions and technologies. Proper validation approaches, concepts, and corresponding tools are partly missing until now. This paper addresses these issues by discussing the progress in the integrated Pan-European research infrastructure project ERIGrid where proper validation methods and tools are currently being developed for validating smart grid systems and solutions.

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Distributed energy resources (DERs) have seen significant expansion in utilization over the past decade. This expansion is best observed with the rooftop solar panels whose penetration has substantially grown in terms of deployed MWs. With the transformation of the grid towards more distributed supply of electricity, a new set of challenges arise. Although the challenges for adoption of DERs are plenty which span across technical, economical and policy domain, in this paper we discuss simulation challenges within two particular domains, cyber-security and voltage stability. For addressing each of these challenges, co-simulation has shown to be a promising path to take. Co-simulation (or combined simulation) represents the connection of two or more simulation tools with the goal of addressing a particular problem that neither one of these tools could address individually. Within each of these domains, we discuss the aspects for the design of co-simulation that one must consider when addressing the problem. The discussion is followed by short simulation examples.@en

Work package JRA2 focuses on the development of advanced simulation-based methods to checkand validate smart grid scenarios, configurations and corresponding applications. The main aim isto employ offline simulation of scenarios where a combination of parallel processing, advanced optimization techniques, and design-of-experiments is used to master the system complexity. Secondary targets include the development of methods for HIL application as well as for the assessment of cyber-security concepts. This assessment will cover the following smart grid properties:system stability, system scalability, component interoperability, and information security. Eventuallyit is the goal to explore the operational limits and the sensitivity of these system properties towardssystem parameters.@en

This paper discusses and compares two approaches to address technical challenges in performing collaborative studies of power system dynamics. On one side, we consider the model migration approach which is an essential piece of dynamic model exchange. On the other side, we look at the co-simulation approach which is used to couple simulation tools together. The approaches are compared in terms of accuracy and the most probable reasons for discrepancy between dynamic responses are discussed. We complement the discussion and conclusions with the simulation results on a simple test system.@en