Publications

As part of the project ? Designing Resilient Communities (DRC) : A Consequence - Based Approach for Grid Investment , ? funded by the United States (US) Department of Energy?s (DOE) Grid Modernization Laboratory Consortium (GMLC), Sandia National Labora tories (Sandia) is partnering with a variety of government , industry, and university participants to develop and test a framework for community resilience planning focused on modernization of the electric grid. This report provides a summary of the section of the project focused on h ardware demonstration of ?resilience nodes? concept . Acknowledgements ? SAG members ? P roject partners ? Project team/management ? P roject sponsors ? O ther stakeholders

The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate knowledge of these settings to be difficult to obtain in practice. This paper addresses the problem of determining inverter reactive power control settings from net load advanced metering infrastructure (AMI) data. The estimation is first cast as fitting parameterized control curves. We argue for an intuitive and practical approach to preprocess the AMI data, which exposes the setting to be extracted. We then develop a more general approach with a data-driven reactive power disaggregation algorithm, reframing the problem as a maximum likelihood estimation for the native load reactive power. These methods form the first approach for reconstructing reactive power control settings of solar PV inverters from net load data. The constrained curve fitting algorithm is tested on 701 loads with behind-the-meter (BTM) PV systems with identical control settings. The settings are accurately reconstructed with mean absolute percentage errors between 0.425% and 2.870%. The disaggregation-based approach is then tested on 451 loads with variable BTM PV control settings. Different configurations of this algorithm reconstruct the PV inverter reactive power timeseries with root mean squared errors between 0.173 and 0.198 kVAR.

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

This report is a summary of a 3-year LDRD project that developed novel methods to detect faults in the electric power grid dramatically faster than today’s protection systems. Accurately detecting and quickly removing electrical faults is imperative for power system resilience and national security to minimize impacts to defense critical infrastructure. The new protection schemes will improve grid stability during disturbances and allow additional integration of renewable energy technologies with low inertia and low fault currents. Signal-based fast tripping schemes were developed that use the physics of the grid and do not rely on communication to reduce cyber risks for safely removing faults.

Although there are increasing numbers of distributed energy resources (DERs) and microgrids being deployed, current IEEE and utility standards generally strictly limit their interconnection inside secondary networks. Secondary networks are low-voltage meshed (non-radial) distribution systems that create redundancy in the path from the main grid source to each load. This redundancy provides a high level of immunity to disruptions in the distribution system, and thus extremely high reliability of electric power service. There are two main types of secondary networks, called grid and spot secondary networks, both of which are used worldwide. In the future, primary networks in distribution systems that might include looped or meshed distribution systems at the primary-voltage (mediumvoltage) level may also become common as a means for improving distribution reliability and resilience. The objective of this multiyear project is to increase the adoption of microgrids in secondary networks and meshed distribution systems by developing novel protection schemes that allow for safe reliable operation of DERs in secondary networks. We will address these challenges by working with the appropriate stakeholders of secondary network operators, protection vendors, and standards committee. The outcomes of this project include: a) development and/or demonstration of candidate methods for enabling protection of secondary networks containing high levels of DER; b) development of modeling and testing tools for protection systems designed for use with secondary networks including DERs; and c) development of new industrial partnerships to facilitate widespread results dissemination and eventual commercialization of results as appropriate.

Conservation voltage reduction (CVR) is a common technique used by utilities to strategically reduce demand during peak periods. As penetration levels of distributed generation (DG) continue to rise and advanced inverter capabilities become more common, it is unclear how the effectiveness of CVR will be impacted and how CVR interacts with advanced inverter functions. In this work, we investigated the mutual impacts of CVR and DG from photovoltaic (PV) systems (with and without autonomous Volt-VAR enabled). The analysis was conducted on an actual utility dataset, including a feeder model, measurement data from smart meters and intelligent reclosers, and metadata for more than 30 CVR events triggered by the utility over the year. The installed capacity of the modeled PV systems represented 66% of peak load, but reached instantaneous penetrations reached up to 2.5x the load consumption over the year. While the objectives of CVR and autonomous Volt-VAR are opposed to one another, this study found that their interactions were mostly inconsequential since the CVR events occurred when total PV output was low.

Distributed generation (DG) sources like photovoltaic (PV) systems with advanced inverters are able to perform grid-support functions, like autonomous Volt-VAR that attempts to mitigate voltage issues by injecting or consuming reactive power. However, the Volt-VAR function operates with VAR priority, meaning real power may be curtailed to provide additional reactive power support. Since some locations on the grid may be more prone to higher voltages than others, PV systems installed at those locations may be forced to curtail more power, adversely impacting the value of that PV system. Adaptive Volt-VAR (AVV) could be implemented as an alternative, whereby the Volt-VAR reference voltage changes over time, but this functionality has not been well-explored in the literature. In this work, the potential benefits and grid impacts of AVV were investigated using yearlong quasi-static time-series (QSTS) simulations. After testing a variety of allowable AVV settings, we found that even with aggressive settings AVV resulted in <0.01% real power curtailment and significantly reduced the reactive power support required from the PV inverter compared to conventional Volt-VAR but did not provide much mitigation for extreme voltage conditions. The reactive power support provided by AVV was injected to oppose large deviations in voltage (in either direction), indicating that it could be useful for other applications like reducing voltage flicker or minimizing interactions with other voltage regulating devices.

The increasing availability of advanced metering infrastructure (AMI) data has led to significant improvements in load modeling accuracy. However, since many AMI devices were installed to facilitate billing practices, few utilities record or store reactive power demand measurements from their AMI. When reactive power measurements are unavailable, simplifying assumptions are often applied for load modeling purposes, such as applying constant power factors to the loads. The objective of this work is to quantify the impact that reactive power load modeling practices can have on distribution system analysis, with a particular focus on evaluating the behaviors of distributed photovoltaic (PV) systems with advanced inverter capabilities. Quasi-static time-series simulations were conducted after applying a variety of reactive power load modeling approaches, and the results were compared to a baseline scenario in which real and reactive power measurements were available at all customer locations on the circuit. Overall, it was observed that applying constant power factors to loads can lead to significant errors when evaluating customer voltage profiles, but that performing per-phase time-series reactive power allocation can be utilized to reduce these errors by about 6x, on average, resulting in more accurate evaluations of advanced inverter functions.

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.

Downtown low-voltage (LV) distribution networks are generally protected with network protectors that detect faults by restricting reverse power flow out of the network. This creates protection challenges for protecting the system as new smart grid technologies and distributed generation are installed. This report summarizes well-established methods for the control and protection of LV secondary network systems and spot networks, including operating features of network relays. Some current challenges and findings are presented from interviews with three utilities, PHI PEPCO, Oncor Energy Delivery, and Consolidated Edison Company of New York. Opportunities for technical exploration are presented with an assessment of the importance or value and the difficulty or cost. Finally, this leads to some recommendations for research to improve protection in secondary networks.

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

Communication-assisted adaptive protection can improve the speed and selectivity of the protection system. However, in the event, that communication is disrupted to the relays from the centralized adaptive protection system, predicting the local relay protection settings is a viable alternative. This work evaluates the potential for machine learning to overcome these challenges by using the Prophet algorithm programmed into each relay to individually predict the time-dial (TDS) and pickup current (IPICKUP) settings. A modified IEEE 123 feeder was used to generate the data needed to train and test the Prophet algorithm to individually predict the TDS and IPICKUP settings. The models were evaluated using the mean average percentage error (MAPE) and the root mean squared error (RMSE) as metrics. The results show that the algorithms could accurately predict IPICKUP setting with an average MAPE accuracy of 99.961%, and the TDS setting with a average MAPE accuracy of 94.32% which is sufficient for protection parameter prediction.

Adaptive protection is defined as a real-time system that can modify the protective actions according to the changes in the system condition. An adaptive protection system (APS) is conventionally coordinated through a central management system located at the distribution system substation. An APS depends significantly on the communication infrastructure to monitor the latest status of the electric power grid and send appropriate settings to all of the protection relays existing in the grid. This makes an APS highly vulnerable to communication system failures (e.g., broken communication links due to natural disasters as well as wide-range cyber-attacks). To this end, this paper presents the addition of local adaptive modular protection (LAMP) units to the protection system to guarantee its reliable operation under extreme events when the operation of the APS is compromised. LAMP units operate in parallel with the conventional APS. As a backup, if APS fails to operate because of an issue in the communication system, LAMP units can accommodate a reliable fault detection and location on behalf of the protection relay. The performance of the proposed APS is verified using IEEE 123 node test system.

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.

Many Electric Power Systems (EPS) already include geographically dispersed photovoltaic (PV) systems. These PV systems may not be co-located with highest-priority loads and, thus, easily integrated into a microgrid; rather PV systems and priority loads may be far away from one another. Furthermore, because of the existing EPS configuration, non-critical loads between the distant PV and critical load(s) cannot be selectively disconnected. To achieve this, the proposed approach finds ideal switch locations by first defining the path between the critical load and a large PV system, then identifies all potential new switch locations along this path, and finally discovers switch locations for a particular budget by finding the ones the produce the lowest Loss of Load Probability (LOLP), which is when load exceed generation. Discovery of the switches with the lowest LOLP involves a Particle Swarm Optimization (PSO) implementation. The objective of the PSO is to minimize the microgird’s LOLP. The approach assumes dynamic microgrid operations, where both the critical and non-critical loads are powered during the day and only the critical load at night. To evaluate the approach, this paper includes a case study that uses the topology and Advanced Metering Infrastructure (AMI) data from an actual EPS. For this example, the assessment found new switch locations that reduced the LOLP by up to 50% for two distant PV location scenarios.

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High penetration of solar photovoltaics can have a significant impact on the power flows and voltages in distribution systems. In order to support distribution grid planning, control and optimization, it is imperative for utilities to maintain an accurate database of the locations and sizes of PV systems. This paper extends previous work on methods to estimate the location of PV systems based on knowledge of the distribution network model and availability of voltage magnitude measurement streams. The proposed method leverages the expected impact of solar injection variations on the circuit voltage and takes into account the operation and impact of changes in voltage due to discrete voltage regulation equipment (VRE). The estimation model enables determining the most likely location of PV systems, as well as voltage regulator tap and switching capacitors state changes. The method has been tested for individual and multiple PV system, using the Chi-Square test as a metric to evaluate the goodness of fit. Simulations on the IEEE 13-bus and IEEE 123-bus distribution feeders demonstrate the ability of the method to provide consistent estimations of PV locations as well as VRE actions.

Renewable energy has become a viable solution for reducing the harmful effects that fossil fuels have on our environment, prompting utilities to replace traditional synchronous generators (SG) with more inverter-based devices that can provide clean energy. One of the biggest challenges utilities are facing is that by replacing SG, there is a reduction in the systems' mechanical inertia, making them vulnerable to frequency instability. Grid-forming inverters (GFMI) have the ability to create and regulate their own voltage reference in a manner that helps stabilize system frequency. As an emerging technology, there is a need for understanding their dynamic behavior when subjected to abrupt changes. This paper evaluates the performance of a GFMI when subjected to voltage phase jump conditions. Experimental results are presented for the GFMI subjected to both balanced and unbalanced voltage phase jump events in both P/Q and V/f modes.

Recent trends in PV economics and advanced inverter functionalities have contributed to the rapid growth in PV adoption; PV modules have gotten much cheaper and advanced inverters can deliver a range of services in support of grid operations. However, these phenomena also provide conditions for PV curtailment, where high penetrations of distributed PV often necessitate the use of advanced inverter functions with VAR priority to address abnormal grid conditions like over- and under-voltages. This paper presents a detailed energy loss analysis, using a combination of open-source PV modeling tools and high-resolution time-series simulations, to place the magnitude of clipped and curtailed PV energy in context with other operational sources of PV energy loss. The simulations were conducted on a realistic distribution circuit, modified to include utility load data and 341 modeled PV systems at 25% of the customer locations. The results revealed that the magnitude of clipping losses often overshadows that of curtailment but, on average, both were among the lowest contributors to total annual PV energy loss. However, combined clipping and curtailment loss are likely to become more prevalent as recent trends continue.

High penetration of distributed energy resources presents challenges for monitoring and control of power distribution systems. Some of these problems might be solved through accurate monitoring of distribution systems, such as what can be achieved with distribution system state estimation (DSSE). With the recent large-scale deployment of advanced metering infrastructure associated with existing SCADA measurements, DSSE may become a reality in many utilities. In this paper, we present a sensitivity analysis of DSSE with respect to phase mislabeling of single-phase service transformers, another class of errors distribution system operators are faced with regularly. The results show DSSE is more robust to phase label errors than a power flow-based technique, which would allow distribution engineers to more accurately capture the impacts and benefits of distributed PV.

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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.