Laboratory coupling approach

This chapter deals with the coupling of smart grid laboratories for joint experiments. Therefore, various possibilities are outlined and a reference implementation is introduced. Finally, the vision of a distributed, virtual research infrastructure is presented

Education and training needs, methods, and tools

The importance of education and training in the domain of power and energy systems targeting the topics of cyber-physical energy systems/smart grids is discussed in this chapter. State-of-the art laboratory-based and simulation-based tools are presented, aiming to address the new educational needs

An integrated pan-European research infrastructure for validating smart grid systems

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.This work is supported by the European Community’s Horizon 2020 Program (H2020/2014-2020) under project “ERIGrid” (Grant Agreement No. 654113). Further information is available at the corresponding website www.erigrid.eu

Failure Analysis and Diagnosis Scheme in Distribution Systems

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.Intelligent Electrical Power Grid

Incipient Equipment Failure Assessment and Avoidance through Robust Detection Technqiue

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).</p

Improved Grid Reliability by Robust Distortion Detection and Classification Algorithm

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

Grüneisen divergence near the structural quantum phase transition in ScF 3

The cubic perovskite ScF 3 exhibits pronounced isotropic negative thermal expansion and a structural quantum phase transition. We present measurements of the thermal expansion from 200 K to about 1.6 K. We find evidence for stress effects at both high and low temperatures. The coefficient of volumetric thermal expansion and the Grüneisen parameter exhibit power-law temperature dependences above 12 K. The Grüneisen parameter changes sign near 2.4 K and reaches +160 by 1.6 K. Our results are consistent with predictions of Grüneisen divergence on the ordered side of a quantum critical point

Improved Grid Reliability by Robust Distortion Detection and Classification Algorithm

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.Intelligent Electrical Power Grid

High Impedance Fault Detection using Advanced Distortion Detection Technique

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."Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public."Intelligent Electrical Power Grid

Towards Improved Reliability Indices using Waveform Distortions in Distribution System

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."Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public."Intelligent Electrical Power Grid