Publications

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This article is the first in a two-part series on the influence of inverter-based resources (IBRs) s on microgrid protection. In part one, the focus is on microgrids deployed on radial circuits. This article discusses some of the challenges related to the protection of IBR-based microgrids and presents some ongoing research and solutions in the area. The different controls for IBRs are discussed to present how their short current signatures and dynamic response under faults impact microgrid protection. Recently, microgrids have gained much attention in the electric power industry due to their capability for improving power system reliability and resiliency, their impact on increasing the use of renewable resources, the reduced cost of distributed energy resource (DER) equipment, and the continuing evolution of applicable codes and standards.

The integration of renewable and distributed energy resources to the electric power system is expected to increase, particularly at the distribution level. As a consequence, the grid will become more modular consisting of many interconnected microgrids. These microgrids will likely evolve from existing distribution feeders and hence be unbalanced in nature. As the world moves towards cleaner and distributed generation, microgrids that are 100% inverter sourced will become more commonplace. To increase resiliency and reliability, these microgrids will need to operate in both grid-connected and islanded modes. Protection and control of these microgrids needs to be studied in real-time to test and validate possible solutions with hardware-in-the-loop (HIL) and real communication delays. This paper describes the creation of a real-time microgrid test bed based on the IEEE 13-bus distribution system using the RTDS platform. The inverter models with grid-forming and grid-following control schemes are discussed. Results highlighting stable operation, power sharing, and fault response are shown.

The goal of this paper is to utilize machine learning (ML) techniques for estimating the distribution circuit topology in an adaptive protection system. In a reconfigurable distribution system with multiple tie lines, the adaptive protection system requires knowledge of the existing circuit topology to adapt the correct settings for the relay. Relays rely on the communication system to identify the latest status of remote breakers and tie lines. However, in the case of communication system failure, the performance of adaptive protection system can be significantly impacted. To tackle this challenge, the remote circuit breakers and tie lines' status are estimated locally at a relay to identify the circuit topology in a reconfigurable distribution system. This paper utilizes Support Vector Machine (SVM) to forecast the status of remote circuit breakers and identify the circuit topology. The effectiveness of proposed approach is verified on two sample test systems.

Distribution system model accuracy is increasingly important and using advanced metering infrastructure (AMI) data to algorithmically identify and correct errors can dramatically reduce the time required to correct errors in the models. This work proposes a data-driven, physics-based approach for grouping residential meters downstream of the same service transformer. The proposed method involves a two-stage approach that first uses correlation coefficient analysis to identify transformers with errors in their customer grouping then applies a second stage, using a linear regression formulation, to correct the errors. This method achieved >99% accuracy in transformer groupings, demonstrated using EPRI's Ckt 5 model containing 1379 customers and 591 transformers.

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Topology identification in transmission systems has historically been accomplished using SCADA measurements. In distribution systems, however, SCADA measurements are insufficient to determine system topology. An accurate system topology is essential for distribution system monitoring and operation. Recently there has been a proliferation of Advanced Metering Infrastructure (AMI) by the electrical utilities, which improved the visibility into distribution systems. These measurements offer a unique capability for Distribution System Topology Identification (DSTI). A novel approach to DSTI is presented in this paper which utilizes the voltage magnitudes collected by distribution grid sensors to facilitate identification of the topology of the distribution network in real-time using Linear Discriminant Analysis (LDA) and Regularized Diagonal Quadratic Discriminant Analysis (RDQDA). The results show that this method can leverage noisy voltage magnitude readings from load buses to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.

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The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modeling and impact analysis. Unlike conventional scenario-based studies, quasi-static time-series (QSTS) simulations can realistically model time-dependent voltage controllers and the diversity of potential impacts that can occur at different times of year. However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1-second resolution is often required, which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled. This computational burden is a clear limitation to the adoption of QSTS simulations in interconnection studies and for determining optimal control solutions for utility operations. The solutions we developed include accurate and computationally efficient QSTS methods that could be implemented in existing open-source and commercial software used by utilities and the development of methods to create high-resolution proxy data sets. This project demonstrated multiple pathways for speeding up the QSTS computation using new and innovative methods for advanced time-series analysis, faster power flow solvers, parallel processing of power flow solutions and circuit reduction. The target performance level for this project was achieved with year-long high-resolution time series solutions run in less than 5 minutes within an acceptable error.

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This paper discusses the findings from an EPRI industry survey mapping the state, current practices, and future needs of distribution load modeling in the U.S. and internationally. The paper provides a benchmark for distribution utilities and a view of the current industry state and future needs for researchers and other readers. The survey found the parameters and measurements available and utilized for load modeling to vary widely between the utilities and data types. Loads were found to be largely modeled based on different load allocation methods. While distribution planning was found to focus on assessing peak load conditions, some utilities evaluate other time instances and/or explore time-series assessments. Simple grid edge and voltage sensitivity models were found common. The identified future needs include access for additional data, as well as methods to process and utilize the increasing data, handle masked load, and perform time-series load modeling.

The recent growth of sensing devices on the distribution system, such as smart meter deployment, has enabled a wide variety of data-driven distribution system model calibration algorithms. A challenge associated with developing algorithms for model calibration tasks is the determination of parameters for a particular algorithm. This work proposes a method for parameter selection utilizing silhouette score analysis that allows these parameters to be tuned on a per-feeder basis. This method leverages cluster analysis and the distance matrices often produced by phase identification methods. The proposed method was tested on 5 feeders from 2 different utilities to select the number of clusters used in a spectral clustering phase identification algorithm. A synthetic dataset was then used to validate the method with the phase identification algorithm performing with 100% accuracy.

This paper presents a new method for detecting power quality disturbances, such as faults. The method is based on the dynamic mode decomposition (DMD)-a data-driven method to estimate linear dynamics whose eigenvalues and eigenvectors approximate those of the Koopman operator. The proposed method uses the real part of the main eigenvalue estimated by the DMD as the key indicator that a power quality event has occurred. The paper shows how the proposed method can be used to detect events using current and voltage signals to distinguish different faults. Because the proposed method is window-based, the effect that the window size has on the performance of the approach is analyzed. In addition, a study on the effect that noise has on the proposed approach is presented.

As a result of the increase in penetration of inverter-based generation such as wind and solar, the dynamics of the grid are being modified. These modifications may threaten the stability of the power system since the dynamics of these devices are completely different from those of rotating generators. Protection schemes need to evolve with the changes in the grid to successfully deliver their objectives of maintaining safe and reliable grid operations. This paper explores the theory of traveling waves and how they can be used to enable fast protection mechanisms. It surveys a list of signal processing methods to extract information on power system signals following a disturbance. The paper also presents a literature review of traveling wave-based protection methods at the transmission and distribution levels of the grid and for AC and DC configurations. The paper then discusses simulations tools to help design and implement protection schemes. A discussion of the anticipated evolution of protection mechanisms with the challenges facing the grid is also presented.