Publications

Abstract not provided.

The protection systems (circuit breakers, relays, reclosers, and fuses) of the electric grid are the primary component responding to resilience events, ranging from common storms to extreme events. The protective equipment must detect and operate very quickly, generally <0.25 seconds, to remove faults in the system before the system goes unstable or additional equipment is damaged. The burden on protection systems is increasing as the complexity of the grid increases; renewable energy resources, particularly inverter-based resources (IBR) and increasing electrification all contribute to a more complex grid landscape for protection devices. In addition, there are increasing threats from natural disasters, aging infrastructure, and manmade attacks that can cause faults and disturbances in the electric grid. The challenge for the application of AI into power system protection is that events are rare and unpredictable. In order to improve the resiliency of the electric grid, AI has to be able to learn from very little data. During an extreme disaster, it may not be important that the perfect, most optimal action is taken, but AI must be guaranteed to always respond by moving the grid toward a more stable state during unseen events.

Fast detection and isolation of faults in a DC microgrid is of particular importance. Fast tripping protection (i) increases the lifetime of power electronics (PE) switches by avoiding high fault current magnitudes and (ii) enhances the controllability of PE converters. This paper proposes a traveling wave (TW) based scheme for fast tripping protection of DC microgrids. The proposed scheme utilizes a discrete wavelet transform (DWT) to calculate the high-frequency components of DC fault currents. Multiresolution analysis (MRA) using DWT is utilized to detect TW components for different frequency ranges. The Parseval energy of the MRA coefficients are then calculated to demonstrate a quantitative relationship between the fault current signal energy and coefficients’ energy. The calculated Parseval energy values are used to train a Support Vector Machine classifier to identify the fault type and a Gaussian Process regression engine to estimate the fault location on the DC cables. The proposed approach is verified by simulating two microgrid test systems in PSCAD/EMTDC.

Increased penetration of Distributed Energy Resources and microgrids have fundamentally changed the operation al characteristics of Low Voltage (LV) network systems. Current LV network protection philosophy and practice are due for a significant re vamp to keep up with changing operating conditions. This workshop invites four of the major LV network users in the US to discuss the challenges they face today and the new technologies they have been experimenting with in light of this workshop discussion, use cases for further hardware-in-the-loop testing efforts are proposed to evaluate new LV network protection solutions.

With the increase in penetration of inverter-based resources (IBRs) in the electrical power system, the ability of these devices to provide grid support to the system has become a necessity. With standards previously developed for the interconnection requirements of grid-following inverters (GFLI) (most commonly photovoltaic inverters), it has been well documented how these inverters 'should' respond to changes in voltage and frequency. However, with other IBRs such as grid-forming inverters (GFMIs) (used for energy storage systems, standalone systems, and as uninterruptable power supplies) these requirements are either: not yet documented, or require a more in deep analysis. With the increased interest in microgrids, GFMIs that can be paralleled onto a distribution system have become desired. With the proper control schemes, a GFMI can help maintain grid stability through fast response compared to rotating machines. This paper will present an experimental comparison of commercially available GFMIand GFLI ' responses to voltage and frequency deviation, as well as the GFMIoperating as a standalone system and subjected to various changes in loads.

Modern distribution systems can accommodate different topologies through controllable tie lines for increasing the reliability of the system. Estimating the prevailing circuit topology or configuration is of particular importance at the substation for different applications to properly operate and control the distribution system. One of the applications of circuit configuration estimation is adaptive protection. An adaptive protection system relies on the communication system infrastructure to identify the latest status of power. However, when the communication links to some of the equipment are outaged, the adaptive protection system may lose its awareness over the status of the system. Therefore, it is necessary to estimate the circuit status using the available healthy communicated data. This paper proposes the use of machine learning algorithms at the substation to estimate circuit configuration when the communication to the tie breakers is compromised. Doing so, the adaptive protection system can identify the correct protection settings corresponding to the estimated circuit topology. The effectiveness of the proposed approach is verified on IEEE 123 bus test system.

This paper presents a novel approach for fault location and classification based on combining mathematical morphology (MM) with Random Forests (RF). The MM stage of the method is used to pre-process voltage and current data. Signal vector norms on the output signals of the MM stage are then used as the input features for a RF machine learning classifier and regressor. The data used as input for the proposed approach comprises only a window of 50 µs before and after the fault is detected. The proposed method is tested with noisy data from a small simulated system. These results show 100% accuracy for the classification task and prediction errors with an average of ~13 m in the fault location task.

Incorrect modeling of control characteristics for inverter-based resources (IBRs) can affect the accuracy of electric power system studies. In many distribution system contexts, the control settings for behind-the-meter (BTM) IBRs are unknown. This paper presents an efficient method for selecting a small number of time series samples from net load meter data that can be used for reconstructing or classifying the control settings of BTM IBRs. Sparse approximation techniques are used to select the time series samples that cause the inversion of a matrix of candidate responses to be as well-conditioned as possible. We verify these methods on 451 actual advanced metering infrastructure (AMI) datasets from loads with BTM IBRs. Selecting 60 15-minute granularity time series samples, we recover BTM control characteristics with a mean error less than 0.2 kVAR.

With the increase in penetration of inverter-based resources (IBRs) in the electrical power system, the ability of these devices to provide grid support to the system has become a necessity. With standards previously developed for the interconnection requirements of grid-following inverters (GFLI) (most commonly photovoltaic inverters), it has been well documented how these inverters 'should' respond to changes in voltage and frequency. However, with other IBRs such as grid-forming inverters (GFMIs) (used for energy storage systems, standalone systems, and as uninterruptable power supplies) these requirements are either: not yet documented, or require a more in deep analysis. With the increased interest in microgrids, GFMIs that can be paralleled onto a distribution system have become desired. With the proper control schemes, a GFMI can help maintain grid stability through fast response compared to rotating machines. This paper will present an experimental comparison of commercially available GFMIand GFLI ' responses to voltage and frequency deviation, as well as the GFMIoperating as a standalone system and subjected to various changes in loads.

For the resiliency of both small and large distribution systems, the concept of microgrids is arising. The ability for sections of the distribution system to be 'self-sufficient' and operate under their own energy generation is a desirable concept. This would allow for only small sections of the system to be without power after being affected by abnormal events such as a fault or a natural disaster, and allow for a greater number of consumers to go through their lives as normal. Research is needed to determine how different forms of generation will perform in a microgrid, as well as how to properly protect an islanded system. While synchronous generators are well understood and generally accepted amongst utility operators, inverter-based resources (IBRs) are less common. An IBR's fault characteristic varies between manufacturers and is heavily based on the internal control scheme. Additionally, with the internal protections of these devices to not damage the switching components, IBRs are usually limited to only 1.1-2.5p.u. of the rated current, depending on the technology. This results in traditional protection methods such as overcurrent devices being unable to 'trip' in a microgrid with high IBR penetration. Moreover, grid-following inverters (commonly used for photovoltaic systems) require a voltage source to synchronize with before operating. Also, these inverters do not provide any inertia to a system. On the other hand, grid-forming inverters can operate as a primary voltage source, and provide an 'emulated inertia' to the system. This study will look at a small islanded system with a grid-forming inverter, and a grid-following inverter subjected to a line-to-ground fault.

A novel method for fault classification and location is presented in this paper. This method is divided into an initial signal processing stage that is followed by a machine learning stage. The initial stage analyzes voltages and currents with a window-based approach based on the dynamic mode decomposition (DMD) and then applies signal norms to the resulting DMD data. The outputs for the signal norms are used as features for a random-forests for classifying the type of fault in the system as well as for fault location purposes. The method was tested on a small distribution system where it showed an accuracy of 100% in fault classification and a mean error of ~ 30 m when predicting the fault location.

Modern distribution systems can accommodate different topologies through controllable tie lines for increasing the reliability of the system. Estimating the prevailing circuit topology or configuration is of particular importance at the substation for different applications to properly operate and control the distribution system. One of the applications of circuit configuration estimation is adaptive protection. An adaptive protection system relies on the communication system infrastructure to identify the latest status of power. However, when the communication links to some of the equipment are outaged, the adaptive protection system may lose its awareness over the status of the system. Therefore, it is necessary to estimate the circuit status using the available healthy communicated data. This paper proposes the use of machine learning algorithms at the substation to estimate circuit configuration when the communication to the tie breakers is compromised. Doing so, the adaptive protection system can identify the correct protection settings corresponding to the estimated circuit topology. The effectiveness of the proposed approach is verified on IEEE 123 bus test system.

This paper presents a simulation and respective analysis of traveling waves from a 5-bus distribution system connected to a grid-forming inverter (GFMI). The goal is to analyze the numerical differences in traveling waves if a GFMI is used in place of a traditional generator. The paper introduces the topic of traveling waves and their use in distribution systems for fault clearing. Then it introduces a Simulink design of said 5-bus system around which this paper is centered. The system is subject to various simulation tests of which the results and design are explained further in the paper to discuss if and how exactly inverters affect traveling waves and how different design choices for the system can impact these waves. Finally, a consideration is made for what these traveling waves represent in a practical environment and how to properly address them using the information derived in this study.

As the legacy distance protection schemes are starting to transition from impedance-based to traveling wave (TW) time-based, it is important to perform diligent simulations prior to commissioning the TW relay. Since Control-Hardware-In-the-Loop (CHIL) simulations have recently become a common practice for power system research, this work aims to illustrate some limitations in the integration of commercially available TW relays in CHIL for transmission-level simulations. The interconnection of Frequency-Dependent (FD) with PI-modeled transmission lines, which is a common practice in CHIL, may lead to sharp reflections that ease the relaying task. However, modeling contiguous lines as FD, or the presence of certain shunt loads, may cover certain TW reflections. As a consequence, the fault location algorithm in the relay may lead to a wrong calculation. In this paper, a qualitative comparison of the performance of commercially available TW relay is carried out to show how the system modeling in CHIL may affect the fault location accuracy.

The proper coordination of power system protective devices is essential for maintaining grid safety and reliability but requires precise knowledge of fault current contributions from generators like solar photovoltaic (PV) systems. PV inverter fault response is known to change with atmospheric conditions, grid conditions, and inverter control settings, but this time-varying behavior may not be fully captured by conventional static fault studies that are used to evaluate protection constraints in PV hosting capacity analyses. To address this knowledge gap, hosting capacity protection constraints were evaluated on a simplified test circuit using both a time-series fault analysis and a conventional static fault study approach. A PV fault contribution model was developed and utilized in the test circuit after being validated by hardware experiments under various irradiances, fault voltages, and advanced inverter control settings. While the results were comparable for certain protection constraints, the time-series fault study identified additional impacts that would not have been captured with the conventional static approach. Overall, while conducting full time-series fault studies may become prohibitively burdensome, these findings indicate that existing fault study practices may be improved by including additional test scenarios to better capture the time-varying impacts of PV on hosting capacity protection constraints.

This paper proposes a framework to explain and quantify how a Traveling Wave (TW)-based fault location classifier, a Random Forest, is affected by different TW propagation factors. The classifier's goal is to determine the faulty Protection Zone. In order to work with a simplified, yet realistic, distribution system, this work considers a use case with different configurations that are obtained by optionally including several common distribution elements such as voltage regulators, capacitor banks, laterals, and extra loads. Simulated faults are decomposed in frequency bands using the Stationary Wavelet Transform, and the classifier is trained with such signals' energy. SHapley Additive exPlanations (SHAP) are used to identify the most important features, and the effect of different fault configurations is quantified using the Jensen-Shannon Divergence. Results show that distance, the presence of voltage regulators and the fault type are the main factors that affect the classifier's behavior.

Frequent changes in penetration levels of distributed energy resources (DERs) and grid control objectives have caused the maintenance of accurate and reliable grid models for behind-the-meter (BTM) photovoltaic (PV) system impact studies to become an increasingly challenging task. At the same time, high adoption rates of advanced metering infrastructure (AMI) devices have improved load modeling techniques and have enabled the application of machine learning algorithms to a wide variety of model calibration tasks. Therefore, we propose that these algorithms can be applied to improve the quality of the input data and grid models used for PV impact studies. In this paper, these potential improvements were assessed for their ability to improve the accuracy of locational BTM PV hosting capacity analysis (HCA). Specifically, the voltage- and thermal-constrained hosting capacities of every customer location on a distribution feeder (1,379 in total) were calculated every 15 minutes for an entire year before and after each calibration algorithm or load modeling technique was applied. Overall, the HCA results were found to be highly sensitive to the various modeling deficiencies under investigation, illustrating the opportunity for more data-centric/model-free approaches to PV impact studies.

The installation of digital sensors, such as advanced meter infrastructure (AMI) meters, has provided the means to implement a wide variety of techniques to increase visibility into the distribution system, including the ability to calibrate the utility models using data-driven algorithms. One challenge in maintaining accurate and up-to-date distribution system models is identifying changes and event occurrences that happen during the year, such as customers who have changed phases due to maintenance or other events. This work proposes a method for the detection of phase change events that utilizes techniques from an existing phase identification algorithm. This work utilizes an ensemble step to obtain predicted phases for windows of data, therefore allowing the predicted phase of customers to be observed over time. The proposed algorithm was tested on four utility datasets as well as a synthetic dataset. The synthetic tests showed the algorithm was capable of accurately detecting true phase change events while limiting the number of false-positive events flagged. In addition, the algorithm was able to identify possible phase change events on two real datasets.

For the resiliency of both small and large distribution systems, the concept of microgrids is arising. The ability for sections of the distribution system to be 'self-sufficient' and operate under their own energy generation is a desirable concept. This would allow for only small sections of the system to be without power after being affected by abnormal events such as a fault or a natural disaster, and allow for a greater number of consumers to go through their lives as normal. Research is needed to determine how different forms of generation will perform in a microgrid, as well as how to properly protect an islanded system. While synchronous generators are well understood and generally accepted amongst utility operators, inverter-based resources (IBRs) are less common. An IBR's fault characteristic varies between manufacturers and is heavily based on the internal control scheme. Additionally, with the internal protections of these devices to not damage the switching components, IBRs are usually limited to only 1.1-2.5p.u. of the rated current, depending on the technology. This results in traditional protection methods such as overcurrent devices being unable to 'trip' in a microgrid with high IBR penetration. Moreover, grid-following inverters (commonly used for photovoltaic systems) require a voltage source to synchronize with before operating. Also, these inverters do not provide any inertia to a system. On the other hand, grid-forming inverters can operate as a primary voltage source, and provide an 'emulated inertia' to the system. This study will look at a small islanded system with a grid-forming inverter, and a grid-following inverter subjected to a line-to-ground fault.

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous modes, which will produce new stresses on the grid-interfacing power electronics components, such as DC/AC inverters. In previous work, multiple DER devices were instrumented to evaluate additional component stress under multiple reactive power setpoints. We utilize quasi-static time-series simulations to determine voltage-reactive power mode (volt-var) mission profile of inverters in an active power system. Mission profiles and loss estimates are then combined to estimate the reduction of the useful life of inverters from different reactive power profiles. It was found that the average lifetime reduction was approximately 0.15% for an inverter between standard unity power factor operation and the IEEE 1547 default volt-var curve based on thermal damage due to switching in the power transistors. For an inverter with an expected 20-year lifetime, the 1547 volt-var curve would reduce the expected life of the device by 12 days. This framework for determining an inverter's useful life from experimental and modeling data can be applied to any failure mechanism and advanced inverter operation.

The paper proposes an implementation of Graph Neural Networks (GNNs) for distribution power system Traveling Wave (TW) - based protection schemes. Simulated faults on the IEEE 34 system are processed by using the Karrenbauer Transform and the Stationary Wavelet Transform (SWT), and the energy of the resulting signals is calculated using the Parseval's Energy Theorem. This data is used to train Graph Convolutional Networks (GCNs) to perform fault zone location. Several levels of measurement noise are considered for comparison. The results show outstanding performance, more than 90% for the most developed models, and outline a fast, reliable, asynchronous and distributed protection scheme for distribution level networks.

Interest in the application of DC Microgrids to distribution systems have been spurred by the continued rise of renewable energy resources and the dependence on DC loads. However, in comparison to AC systems, the lack of natural zero crossing in DC Microgrids makes the interruption of fault currents with fuses and circuit breakers more difficult. DC faults can cause severe damage to voltage-source converters within few milliseconds, hence, the need to quickly detect and isolate the fault. In this paper, the potential for five different Machine Learning (ML) classifiers to identify fault type and fault resistance in a DC Microgrid is explored. The ML algorithms are trained using simulated fault data recorded from a 750 VDC Microgrid modeled in PSCAD/EMTDC. The performance of the trained algorithms are tested using real fault data gathered from an operational DC Microgrid located on the Kirtland Air Force Base. Of the five ML algorithms, three could detect the fault and determine the fault type with at least 99% accuracy, and only one could estimate the fault resistance with at least 99% accuracy. By performing a self-learning monitoring and decision making analysis, protection relays equipped with ML algorithms can quickly detect and isolate faults to improve the protection operations on DC Microgrids.

Incorrect modeling of control characteristics for inverter-based resources (IBRs) can affect the accuracy of electric power system studies. In many distribution system contexts, the control settings for behind-the-meter (BTM) IBRs are unknown. This paper presents an efficient method for selecting a small number of time series samples from net load meter data that can be used for reconstructing or classifying the control settings of BTM IBRs. Sparse approximation techniques are used to select the time series samples that cause the inversion of a matrix of candidate responses to be as well-conditioned as possible. We verify these methods on 451 actual advanced metering infrastructure (AMI) datasets from loads with BTM IBRs. Selecting 60 15-minute granularity time series samples, we recover BTM control characteristics with a mean error less than 0.2 kVAR.

The application of traveling wave principles for fault detection in distribution systems is challenging because of multiple reflections from the laterals and other lumped elements, particularly when we consider communication-free applications. We propose and explore the use of Shapelets to characterize fault signatures and a data-driven machine learning model to accurately classify the faults based on their distance. Studies of a simple 5-bus system suggest that the use of Shapelets for detecting faults is promising. The application to practical three-phase distribution feeders is the subject of continuing research.

The paper proposes an implementation of Graph Neural Networks (GNNs) for distribution power system Traveling Wave (TW) - based protection schemes. Simulated faults on the IEEE 34 system are processed by using the Karrenbauer Transform and the Stationary Wavelet Transform (SWT), and the energy of the resulting signals is calculated using the Parseval's Energy Theorem. This data is used to train Graph Convolutional Networks (GCNs) to perform fault zone location. Several levels of measurement noise are considered for comparison. The results show outstanding performance, more than 90% for the most developed models, and outline a fast, reliable, asynchronous and distributed protection scheme for distribution level networks.