Wildfire Electric Grid Resilience

Problem

• Modeling wildfires depends on accurate vegetation characterization
• Previously used in fire models to assess hazard to assets of interest has historically been static and years old.

Approach

• Leveraging previous work from the Resilient Energy Systems-funded Lab Directed Research and Development (LDRD)
• Generate ML-derived vegetation characterization from fusing satellite imagery and weather station data and pushing into wildfire simulations

Impact

• Improve utilities ability to assess, plan, and adapt to wildfires
• Pilot Sandia’s wildfire analytics product with utility partner to update their Hazard Fire Areas with Sandia’s developed wildfire risk geospatial dataset to provide utilities with daily situational awareness on fire risk

Problem

• Determine wildfire risk to critical infrastructure in near-real-time and understand resulting impacts to the grid that could lead to cascading failure.

Approach

• Apply machine learning to weather station data weather station data to determine near-real-time fuel moisture, leverage wildfire spread software and Sandia grid modeling to identify probabilistic component damage.

Impact

• Provide decision makers with an interactive map which shows our near-real-time fuels condition layer. This allows the user to run wildfire simulations and analyze component damage.

Opportunity

• Explore vectors for the technology through customer discovery interviews

Accomplishments

• Conducted over 75 customer discovery interviews covering multiple industry verticals

Problem

• Mitigation for wildfire threat is very expensive, utilities need a data-driven approach to compare and contrast investments.

Approach

• Model impacts to wildfire risk based on mitigation type simulated, assessing how each one meets objectives, including grid hardening, fuel reduction with surrounding landowners, and vegetation mitigation.

Impact

• Our modeling efforts will provide a data-driven decision support tool that assesses mitigation costs and benefits against specific objectives.

Problem

• Not all utilities and co-ops have access to capabilities to assess current real-time wildfire conditions, risks, mitigation solutions and comparisons, current wildfire projected grid impacts, and public safety power shutoff (PSPS) information.

Approach

• Leveraging significant R&D investments (many listed on this website), to create a wildfire-grid tool for utilities and co-ops. A tool specifically tailored to utilities and coops to better understand:
  • Real-time dynamic utility wildfire risk map
  • Customizable Public Safety Power Shutoff (PSPS) calculator
  • Burn probability through vegetation characterization
  • Wildfire to grid impact analysis
  • Mitigation scenarios / solutions and comparisons

Impacts

• This tool will allow utilities and coops to better plan for and monitor wildfire risks. This will allow for a mitigation of grid-ignited wildfires and mitigate the impact of major wildfires.

Problem

• Utilities are often forced to be reactive to wildfire conditions as they evolve and there is an urgent need to develop and implement proactive planning and mitigation resources with a longer time horizon for effective vegetation management.

Approach

• This project will model fire conditions for the next fire season to improve proactive vegetation planning and mitigation of wildfire threat and improve resiliency.
• Develop a modeling pipeline that integrates seasonal weather forecasts with vegetation growth and fire spread simulations to develop burn probability estimates for the upcoming fire season.
• Analyze effectiveness of vegetation management strategies in reducing wildfire risks, including targeted vegetation management recommendations and grid hardening efforts in high-risk areas.

Impact

• This approach will provide planning information that gives utilities longer lead times to implement management strategies, enabling proactive planning rather than reactive responses, and improving the resource allocation and budgeting for managing these threats.
• This work will help utilities effectively locate priority areas where ignition and wildfire risk are high due to vegetation and improve wildfire resiliency planning by developing actionable mitigation recommendations tailored to the specific needs of an electric utility.

Problem

•  Utilities require accurate up-to-date fire spread models to safely and efficiently distribute mitigation resources.
• Accurate modeling of wildfire spread requires detailed and up-to-date input data on the vegetation conditions such as amount of biomass, vegetation cover type and flammability characteristics.

Approach

• Leverage machine learning (ML) with frequent revisit multiband satellite imagery to predict vegetation coverage for accurate up-to-date fuel model generation.
• Machine learning model down-selection and optimization.
• Run fire spread simulations against historic wildfires to validate fuel model generation.

Impact

• Increase the accuracy and reliability of fire spread models with on-the-fly fuel model generation from up-to-date satellite data.

Problem

• It is challenging to achieve both safety from wildfire and reduce the number of customers impacted by PSPS. PSPS action may severely impact critical loads such as hospitals and leave the grid network vulnerable to additional contingencies in post-PSPS periods.

Approach

• Development of optimization models to minimize load-shedding and impact on customers without compromising wildfire risk
• Ensuring more critical loads getting served in PSPS events
• Development of PSPS forecasting models
• Leveraging the PSPS forecasts to complement the optimization model and help the utilities to select the best PSPS strategy

Impact

• Solutions for utilities to identify when to deploy PSPS, where and how to implement it in a more strategic way
• Solutions to implement PSPS with minimal load-shedding and without impacting a large number of critical customers

Problem

• Lightning features key to fire ignition and grid/asset disruption have yet to be identified due to limitations of existing lightning datasets. Therefore, suppression efforts begin only after fire is large enough to be detected by remote-sensing or an asset is already malfunctioning or offline.

Approach

• We will develop a novel lightning monitoring system that provides total lightning current (and energy), which we hypothesize is a key quantity for understanding and predicting ignition by lightning.

Impact

• Lightning results in the most detrimental wildfire impact, accounting for over 50% of acreage burned worldwide and over 90% in Arctic regions.
• By providing a presently nonexistent lightning total-current dataset from a well-instrumented asset location, this project will enable predictive early warning tools through identification of lightning’s key features in wildfire ignition, and act as pathfinder for future monitoring systems.

Problem

• Humans are notoriously bad at understanding state uncertainty and probability. Prior research suggests that different representations of uncertainty can lead to different patterns of decisions. Such as decisions of whether or not and when to evacuate homes during wildfires, or the decision of when to do Public Safety Power Shutoffs (PSPS).

Approach

• Grid operations (and modeling) involve complex, heterogeneous, uncertain information. How should that data be presented to support optimal decision making?

Impact

• More optimal grid operation and planning decisions, because of a better understanding on how visualization of uncertainty affects those decisions.

Problem

• An unknown time lag occurs between lightning ignition and fire detection such that, “there are no datasets that unambiguously relate igniting lightning to the corresponding wildfires” [Moris et al., 2020] and hence no data to train near-real-time prediction models that could enable preemptive response to suspect locations.

Approach

• We will develop an experimentally-driven computational model to understand the predictability of lightning-ignited fire from first principles. We will leverage Sandia’s ongoing development of codes EMPIRE (plasma discharge physics/chemistry) and SIERRA (heat transport / fire reaction dynamics).

Impact

• The recent rise in uncontrollable wildfire indicates an immediate need for early/preemptive suppression.
• A first principles understanding of wildfire ignition by lightning will enable shortened lead time for lightning caused wildfire response and will provide guidance for lightning monitoring requirements and wildfire prediction tools.

Problem

• The potential for fire ignition is proportional to the duration of the arc, and current protection schemes generally take 100 milliseconds to a second to operate

Solution

• Develop fast, local, bi-directional, data-driven fault detection and location schemes for distribution systems, including DER high-penetrations, that operate in less than 4 milliseconds

Impact

• Achieve sensitive protection schemes instead of Public Safety Power Shutoffs (PSPS)
• Prototype box developed with custom DSP hardware for sampling, running developed algorithms, and making fault decisions – tested in microgrid
• Involved with the Institute of Electrical and Electronics Engineers Power System Relaying Commitee D45 Reduction of Forest Fire Hazard
• Participation in DOE’s Energy I-Corps program to better understand industry needs and refine prototype capabilities. Mentored by the Public Service Company of New Mexico (PNM).

Problem

• There is limited access and availability of grid data to train AI/ML algorithms for detecting events on the grid that might cause wildfires, such as high-impedance faults or downed conductors

Approach

• Through a multi-national lab effort, develop a library of free publicly available data of voltage and current measurements during grid events. This includes data from utility partners, newly installed very high-frequency sensors, and laboratory tests such as arcing
• Enable future wildfire prevention algorithms by adding data to the Grid Event Signature Library (GESL) that is specifically targeted towards potential wildfire ignition events such as faults, downed conductors, arcing, and conductor slap.

Impact

• Reducing risk of initiating wildfires and responding precisely during a wildfire event by creating a standardized database to drive actionable solutions to wildfire initiated by the electric grid
• Making data directly available at ORNL hosted website: https://gesl.ornl.gov/

Problem

• The increased amount of electric vehicle charging load and the incapability of serving it during climate disasters will impact both the electric utility and the transportation sector, and result in jeopardizing the current evacuation plan.

Approach

• Identify the impacts of increased electric vehicle penetration on both the transportation and electric grid during climate disaster evacuation plans.
• Evaluate electric vehicle charging demand based on climate disaster scenarios (e.g. short/long distance evacuation requirements).
• Find the optimal locations to establish new EV infrastructures and utilize the existing ones to ensure maximum electric vehicle utilization (e.g. maximum uptime, facilitation of commuting to shelters, grid assets, and backup power resources), and minimal evacuation time.

Impact

• Impacts analyses of EV penetration on the power grid during evacuation
• Solutions to satisfy the unserved EV load during evacuations
• Multi-objective optimization models to maximize evacuation efficiency and utilization of EV supporting infrastructure.

Problem

• The likelihood of high-voltage insulator failure is expected to increase with surface contamination, natural aging, and carbon deposition from previous breakdowns.

Approach

• Investigate laboratory techniques for artificial contamination and electrical aging
• Procure grid insulators (purchased or donated)
• Application of laboratory-based degradation techniques
• High-voltage breakdown testing of clean and contaminated/aged insulators
• Define risk metrics for contamination and aging at which point risk of failure increases
• Develop failure thresholds and tracking criteria (working with wildfire propagation projects at Sandia) that can be used for grid health predictions in response to wildfires.

Impact

• This information will create a basis for developing high-voltage insulator contamination and aging prediction tools that utilities can use for mitigation planning for parts of the grid impacted by wildfires.

Problem

• Modeling wildfire smoke is a critical component of estimating decreases in solar energy production, and thus maintaining a resilient electric grid. However, wildfire dynamics are governed by complex weather– vegetation – human coupled processes. Further, smoke transport is a computationally challenging task, requiring high performance computing and typically seen as prohibitive at the large spatial and temporal scales required for grid planning efforts.

Approach

• Develop a coupled landscape, weather, wildfire smoke modeling platform using existing tools, and made effective at scale with neural network surrogate models and machine learning.

Impact

• Gridded measure of the reduction in potential solar energy at annual timesteps for the next 30 years.
• A surrogate model to identify spatial smoke impact attribution, to describe where the smoke travels and evolves from, permitting targeted spatial smoke impact attribution, allowing each solar potential reduction pixel to describe the pixels where the smoke evolved from, permitting targeted forest management plans and explainable model output for managers and decision makers.

Problem

• Electrical outage time due to wildfires needs to be minimized.

Approach

• Today, linemen are frequently stationed in areas affected by wildfires.  They await safety approval to access damaged areas and then they proceed to assess and repair the affect grid infrastructure.

Impact

• In partnership with electric grid utilities, identify and evaluate approaches to obtaining key missing information for wildfire grid recovery
• Develop and demonstrate a wildfire grid recovery methodology that includes optimal restoration decisions

• Conference presentation: Jubair Yusuf, “Improving Resiliency during Emergencies considering Power Grid- Transportation Interactions: Modeling, Challenges and Potential Solutions”, AI Edge Symposium, Niskayuna, NY, Sep 2025
• Journal publication: Soroush Vahedi, Junbo Zhao, Brian Pierre, Bin Wang and Jubair Yusuf, “Probabilistic Resilience-Oriented Assessment Approach for Transmission Networks under Wildfires,” in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2025.3599431.4, Aug 2025
• Conference publication: Shengqi Yuan, Junbo Zhao, Jubair Yusuf, Brian Pierre, “Graph Deep Reinforcement Learning for Electric Vehicle Charging Navigation Considering Power-Transportation Interactions,” IEEE PES General Meeting, Austin, TX, July 2025
• Conference publication: Oscar S. Acosta, Jubair Yusuf, William F. Vining, Alejandro D. Owen Aquino, “Evaluating Electric Vehicle Hosting Capacity Methods in Distribution Networks during Wildfires,” IEEE PES General Meeting, Austin, TX, July 2025
• Journal publication: Jubair Yusuf, Holly Eagleston, Michelle Bester and Brian Pierre, “A Framework for Wildfire Risk Assessment to Electric Grid,” in IEEE Access, vol. 13, pp. 115433-115445, 2025, doi: 10.1109/ACCESS.2025.3583964 June 2025
• Journal publication: Soroush Vahedi, Junbo Zhao, Brian Pierre, Fangni Lei, Emmanouil Anagnostou, Kang He, Charles Jones, and Bin Wang, “Wildfire and power grid nexus in a changing climate,” Nature Reviews Electrical Engineering, March 2025, https://doi.org/10.1038/s44287-025-00150-0
• Panel presentation: Brian J. Pierre, “Power System Security: Electric Grid Wildfire Mitigation,” IEEE PES General Meeting, Austin, TX, July 2025.
• Conference presentation: Brian J. Pierre, “Power System Security: Mitigate Electric Grid Wildfire Ignitions,” FERC Software Conference: Increasing Real-Time and Day-Ahead Market and Planning Efficiency Through Improved Software, July 2025.
• Panel presentation: Brian J. Pierre, “Reliability, Resilience & Wildfire Mitigation,” 2025 IEEE Power & Energy Society (PES) Summit: Achieving a More Reliable and Resilient Energy Future, May 2025.
• Journal publication: Holly Eagleston, Michelle Bester, Jubair Yusuf, Adit Damodaran, and Matthew J. Reno, “Systemic Drivers of Electric-Grid-Caused Catastrophic Wildfires: Implications for Resilience in the United States,” Challenges, vol 16, no. 13., Feb. 2025. https://doi.org/10.3390/challe16010013
• Panel discussion: Brian J. Pierre, opened the conference one day on a “Special Breaking News Panel on Wildfires,” with Quanta, BC Hydro, and San Diego Gas & Electric, IEEE Grid Edge Conference & Exposition, San Diego, CA, Jan. 2025.
• Panel presentation: Miguel Jimenez Aparicio, “Novel Traveling Wave based protection scheme for distribution systems with high penetration of distributed energy resources using Machine Learning,” IEEE Grid Edge Conference & Exposition, San Diego, CA, Jan. 2025.
• IEEE Report: “Protection Methods Used to Reduce Wildfire Risks Due to Transmission and Distribution Lines”, IEEE PES-TR130, June 2025.
• Journal Publication: S. Paruthiyil, A. Bidram, M. Jimenez-Aparicio, J. Hernandez-Alvidrez, A. R. R. Dow, M. J. Reno, D. Bauer, “Traveling Wave-based Fault Detection and Location in a Real Low-Voltage DC Microgrid”, IET Smart Grid, Jan. 2025.
• Conference Publication: M. Jimenez Aparicio, J. Hernandez Alvidrez, O. S. Acosta, and M. J. Reno, “An Ultra-Fast Relay Prototype for Low-Impedance Fault Protection in Power Distribution Systems”, IEEE PES General Meeting, July 2025.
• Patent Awarded: 17/864,990 “Dynamic Mode Decomposition Systems and Methods to Detect Power Quality Events”, 12,366,598, 2025
• Panel presentation: Matthew J. Reno, “AI for Grid Resilience”, Tutorial on AI/ML and Overview of AI Energy Applications for Interagency Advanced Power Group (IAPG), April 2025
• Panel moderator: Brian J. Pierre, “Eliminating Grid Initiated Wildfires while Protecting Critical Infrastructure from Wildfire,” with panelists from DOE, Sandia, and the University of Utah, IEEE Grid Edge Conference & Exposition, San Diego, CA, Jan. 2025.
• Panel moderator: Jubair Yusuf, “Challenges with High Electric Vehicle Penetration during Emergencies,” with panelists from Sandia, NextEra Energy Resources, Quanta Technologies, and San Diego Gas & Electric, IEEE Grid Edge Conference & Exposition, San Diego, CA, Jan. 2025.
• Panel presentation: Holly Eagleston, “Optimizing Fuels Thinning for Wildfire Risk Reduction,” IEEE Grid Edge Conference & Exposition, San Diego, CA, Jan. 2025.
• Panel presentation: Brian J. Pierre, “Weathering the Firestorm: Wildfire Electric Grid Resilience,” IEEE Grid Edge Conference & Exposition, San Diego, CA, Jan. 2025.

• Conference presentation: Julia Tilles, Ray Martinez, Alexander L. Brown, Matthew Hopkins, Michael Stock, “Lightning interactions with dielectric media, an experimental investigation using lab-based proxy,” American Geophysical Union Fall Meeting (AGU 2024), Dec. 2024.
• Conference presentation: Michael Stock, Joseph Berry, Julia Tilles, Vanna Chmielewski, “Investigating the relationship between wildfires and lightning,” American Geophysical Union Fall Meeting (AGU 2024), Dec. 2024.
• Conference presentation: Joseph Berry, Michael Stock, Julia Tilles, Eric Bruning, Cooper Gray, “Simultaneous Optical and Electric Field Measurements of Long Continuing Current Lightning Across Different Storm Modes,” American Geophysical Union Fall Meeting (AGU 2024), Dec. 2024.
• Panel presentation: Brian J. Pierre, “Electric Grid Security Wildfire Risk and Resilience: Wildfire ignition and consequence mitigation,” IEEE Resilience Week, Austin, TX, Dec. 2024.
• Conference publication: Alexander L. Brown, Catherine Thomas, Julia N. Tilles, Matthew M. Hopkins, “Interrogating Heat Transport from Lightning Contributing to Ignition,” Third Pacific Rim Thermal Engineering Conference, Dec. 2024.
• Conference presentation: Alex Brown, John Hewson, Julia Tilles, “WUI Fires and Flame Spread with SIERRA/Fuego,” Wildland Urban Interface (WUI) Fire Symposium, Nov. 2024.
• Field Data Gathering Campaign: Michelle Bester led a team to collect tree inventory, hazard fuel, Terrestrial LiDAR, and surface vegetation data at four sites along partner powerlines within a National Forest in partnership with a utility and the U.S. Forest Service. The data collected will be ground-truth data for machine learning algorithms to train future satellite imagery data to assess wildfire risk. Nov. 2024.
• Report publication: Bruning, E. C., A. Back, S. A. Behnke, S. Goodman, C. Hogg, T. Lang, and J. Tilles, “A roadmap for a lightning modeling grand challenge,” v.1.0.0. Tech. rep. Oct. 2024. doi: 10.5281/zenodo.14624043.
• Hosted workshop by Sandia: Alex Haddad and Brian Pierre put on the “Resilient Energy Systems Mission Campaign: Public Utility of New Mexico (PNM) / Sandia Wildfire Workshop.” Albuquerque, NM, Nov. 2024.
• Workshop presentation: Holly Eagleston, “Wildfire Modeling and Vegetation Management,” Resilient Energy Systems Mission Campaign: Public Utility of New Mexico (PNM) / Sandia Wildfire Workshop, Albuquerque, NM, Nov. 2024.
• Workshop presentation: Julia Tilles, “Lighting Ignition,” Resilient Energy Systems Mission Campaign: Public Utility of New Mexico (PNM) / Sandia Wildfire Workshop, Albuquerque, NM, Nov. 2024.
• Workshop presentation: Matthew Reno, “Fast Protection to Mitigate Wildfire Ignition,” Resilient Energy Systems Mission Campaign: Public Utility of New Mexico (PNM) / Sandia Wildfire Workshop, Albuquerque, NM, Nov. 2024.
• Workshop presentation: Daniel Krofcheck, “SMOKEWISE: Smoke and Wildfire Impacts on Solar Energy Resilience Modeling,” Resilient Energy Systems Mission Campaign: Public Utility of New Mexico (PNM) / Sandia Wildfire Workshop, Albuquerque, NM, Nov. 2024.
• Workshop presentation: Ross Guttromson, “Wildfire Recovery and Restoration,” Resilient Energy Systems Mission Campaign: Public Utility of New Mexico (PNM) / Sandia Wildfire Workshop, Albuquerque, NM, Nov. 2024.
• Workshop presentation: Laura Matzen, “Visualizing Uncertainty: Wildfire Evacuation and Public Safety Power Shutoff,” Resilient Energy Systems Mission Campaign: Public Utility of New Mexico (PNM) / Sandia Wildfire Workshop, Albuquerque, NM, Nov. 2024.
• Conference Publication: Laura E. Matzen, Mallory C. Stites, Kristin M. Divis, Alexander E. Bendeck, John T. Stasko, Lace M. Padilla, “Effects of forecast order, cost, and risk on decision making with multiple forecast visualizations,” IEEE VIS 2024 in the proceedings of the Uncertainty Visualization Workshop, Oct. 2024.
• Invited Presentation: Brian J. Pierre, “New Mexico Wildfire Electric Grid Ignition Mitigation,” demonstration presentation to New Mexico Legislators and State Officials, Sept. 2024.
• Invited Presentation: Brian J. Pierre, “Wildfire Electric Grid Resilience Program Overview,” presentation to Operational Technology (OT) Defender Fellowship Program, July 2024.
• Panel Presentation: Brian J. Pierre, “Moving Toward a Climate Resilient Power System: Wildfire Electric Grid Resilience,” panel presentation at 2024 American Control Conference, July 2024.
• Conference publication: Jubair Yusuf, Adam Summers, “Evacuation Planning Under Increasing Electric Vehicle Penetration: Challenges and Solutions,” IEEE PES General Meeting, Seattle, WA, July 2024.
• Journal publication: Laura Matzen, Breannan C. Howell, Marie Tuft, Kristin M. Divis, “Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making,” Computer Graphics Forum, vol. 43, no. 3, June 2024.
• Magazine article: Ali Bidram, Matthew J. Reno, Seyyed A. G. K. Abadi, Miguel Jimenez Aparicio, Daniel Bauer, “Trends in dc Microgrids: From the control and protection perspective,” in IEEE Electrification Magazine, vol. 12, no. 2, pp. 33-47, DOI: 10.1109/MELE.2024.3385978, June 2024.
• Invited panel: Brian J. Pierre, “Sandia National Laboratories Wildfire Grid Resilience Program,” invited panel session at the EPRI Wildfire Advisory Group (WAG) Regional Workshop, June 2024.
• Workshop presentation: Brian J. Pierre, “Wildfire Grid Resilience Program Overview: Mitigate Electric Grid Ignition and Wildfire Consequences,” invited presentation at the EPRI Wildfire Advisory Group (WAG) Regional Workshop, June 2024.
• Workshop presentation: Holly Eagleston, “Understanding Fire Risk Metrics for Mitigation Decision Making,” invited presentation at the EPRI Wildfire Advisory Group (WAG) Regional Workshop, June 2024.
• Workshop presentation: Daniel Krofcheck, “SMOKEWISE: vegetation and climate driven wildfire smoke forecasting for critical infrastructure decision making,” invited presentation at the EPRI Wildfire Advisory Group (WAG) Regional Workshop, June 2024.
• Workshop presentation: Julia Tilles, “Lightning and flashover impact for wildfire and grid disruption,” invited presentation at the EPRI Wildfire Advisory Group (WAG) Regional Workshop, June 2024.
• Workshop presentation: Matthew Reno, “Fast Tripping Relays for Reduced Wildfire Ignition Risk,” invited presentation at the EPRI Wildfire Advisory Group (WAG) Regional Workshop, June 2024.
• Conference publication: Laura Matzen, Breannan. C. Howell, Marie Tuft, Kristin M. Divis, “Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making,” EuroVis, May 2024.
• Award nomination: “Signal-Based Fast Tripping Protection Schemes for Electric Power Distribution System Resilience,” Sandia National Laboratories R&D 100 Award nomination, May 2024.
• Invited presentation: Holly Eagleston, “Data-Driven Analytics for Wildfire Risk,” Resilient Energy Systems Mission Campaign, External Advisory Board, Albuquerque, NM, May 2024.
• Journal publication: C. Remy, D. Krofcheck, A. Keyser, M. Hurteau, “Restoring frequent fire to dry conifer forests delays the decline of subalpine forests in the southwest United States under projected climate,” Journal of Applied Ecology, vol. 61, pp. 1508-1519, May 2024. DOI: 10.1111/1365-2664.14689
• Report: Matthew J. Reno, Miguel Jimenez-Aparicio, Trpal Patel, Adam Summers, et al. “Adaptive Protection and Control for High Penetration PV and Grid Resilience Final Technical Report,” Sandia National Laboratories, SAND2024-05240, April 2024.
• Hosted workshop by Sandia: “Lightning Modeling Workshop,” Hosted by Sandia National Laboratories, (~60 people, SNL, LANL, NASA, NOAA, NCAR, 12 Universities), Albuquerque, NM, April 1-3, 2024.
• Workshop presentation: Hopkins, et. al., “HEAF Modeling with Sierra,” Lightning Modeling Workshop, Albuquerque, NM, April 2024.
• Journal publication: Shubhasmita Pati, Milan Biswal, Satish J. Ranade, Olga Lavrova, Matthew J. Reno, “Evaluating the use of Shapelets in Traveling Wave based Fault Detection and Classification in Distribution Systems,” IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 2507-2516, DOI: 10.1109/TIA.2023.3331661, Mar. 2024.
• Journal publication: Helia Hosseinpour, Laura Matzen, Kristin M. Divis, Spencer C. Castro, Lace Padilla, “Examining limits of small multiples: Frame quantity impacts judgments with line graphs,” IEEE Transactions on Visualization and Computer Graphics, DOI:10.1109/TVCG.2024.3372620, Mar. 2024.
• Invited presentation: Holly Eagleston, “Wildfire Impacts to Critical Infrastructure and Communities”.  Integrating Equity in Wildfire Emergency Management Webinar Series. University at Buffalo and University of Central Florida, Mar. 2024.
• Invited presentation: Miguel Jiménez Aparicio, “GridSense: A fast fault detection and location prototype,” New Mexico Rural Electric Cooperatives (NMREC) Monthly Meeting, Feb. 2024.
• Conference publication: Daniel Zintsmaster, Munim Bin Gani, Sukumar Brahma, Matthew J. Reno, Miguel Jimenez Aparicio, and Javier Hernandez-Alvidrez, “Hardware Implementation and Validation of a dc Protection Scheme Based on Local Measurements,” IEEE Texas Power and Energy Conference (TPEC), DOI:10.1109/TPEC60005.2024.10472288, Feb. 2024.

• Invited presentation: Laura Matzen, “How uncertainty visualizations impact human decision making,” invited presentation EPRI Hosted Utilities Webinar: Sandia National Laboratories Distinguished Presentations, Dec. 2023.
• Invited presentation: Brian J. Pierre, “Wildfire Grid Resilience Program Overview,” invited presentation EPRI Hosted Utilities Webinar: Sandia National Laboratories Distinguished Presentations, Dec. 2023.
• Journal publication: Miguel Jimenez-Aparicio, Trupal R. Patel, Matthew J. Reno, Javier Hernandez-Alvidrez, “Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV,” IEEE Access, vol. 11, DOI: 10.1109/ACCESS.2023.3330464, Nov. 2023.
• Conference publication: Javier Hernandez-Alvidrez, Andrew R. R. Dow, Miguel Jimenez-Aparicio, Matthew J. Reno, Daniel Bauer, and Daniel Ruiz, “Field Validation of a Local, Traveling-Wave, Fault Detection Method for DC Microgrids,” IEEE Innovation Smart Grid Technologies Latin America (ISGT LA), DOI: 10.1109/ISGT-LA56058.2023.10328289, Nov. 2023.
• Conference publication: Andrew R. R. Dow, Matthew J. Reno, Miguel Jimenez-Aparicio, Daniel Bauer, Daniel Ruiz, and Brian Ward, “Testing and Characterization of Fault Scenarios of a Hierarchical DC Microgrid for Residential Applications,” CIGRE US National Committee (USNC) Grid of the Future (GOTF) Symposium, Oct. 2023.
• Patent: “Systems and Methods for Fast Fault Detection and Protection Using Discrete Wavelet Transform.” U.S. Provisional Application No. 63/534,222, filed August 8, 2023.
• Conference publication: Miguel Jimenez-Aparicio, Javier Hernandez-Alvidrez, Armando Y. Montoya, and Matthew J. Reno, “Micro Random Forest: A Local, High-Speed Implementation of a Machine-Learning Fault Location Method for Distribution Power Systems,” IEEE PES General Meeting, DOI: 10.1109/PESGM52003.2023.10253010, July 2023.
• Conference publication: Javier Hernandez-Alvidrez, Miguel Jimenez-Aparicio, and Matthew J. Reno, “Testing Double-Ended Traveling-Wave Protection Schemes in Distribution Systems,” IEEE PES General Meeting, DOI: 10.1109/PESGM52003.2023.10252718, July 2023.
• Conference presentation: Matthew Reno, “Machine Learning for Traveling Wave Protection and DC Systems,” IEEE PES General Meeting, July 2023.
• Conference presentation: Matthew Reno, “Machine Learning for Fault Detection and Classification,” IEEE PES General Meeting, July 2023.
• Journal publication: Miguel Jimenez Aparicio, Felipe Wilches-Bernal, Matthew J. Reno, “Local, Single-Ended, Traveling-Wave Fault Location on Distribution Systems Using Frequency and Time-Domain Data,” IEEE Access, vol. 11, DOI: 10.1109/ACCESS.2023.3296737, July 2023.
• Conference publication: Sajay K. Paruthiyil, Ali Bidram, Miguel Jimenez Aparicio, Javier Hernandez, and Matthew J. Reno, “Hardware Implementation of a Traveling Wave Protection Device for DC Microgrids” IEEE Kansas Power & Energy Conference (KPEC), DOI: 10.1109/KPEC58008.2023.10215446, April 2023.
• Conference poster: Helia Hosseinpour, Laura Matzen, Kristin M. Divis, Spencer C. Castro, Lace Padilla, “Examining the limits of small multiples: Set size impacts accuracy in line graph judgments,” Poster presented at the Gordon Research Conference on Visualization in Science and Education, July 2023.
• Invited presentation: Laura Matzen, “Investigations of the Impact of Information about Uncertainty on Human Decision Making,” invited presentation to DEVCOM Army Research Laboratory, June 2023.
• Workshop presentation: Laura Matzen, “Investigations of the Impact of Information about Uncertainty on Human Decision Making,” invited presentation at PNNL Human Factors Workshop, May 2023.
• Conference presentation: Holly Eagleston, “Energy Release Component for Situational Awareness of Wildfire Conditions,” Edison Electric Institute Wildfire Technology Summit, Phoenix, AZ, Feb. 2023.
• Conference publication: Armando Montoya, Miguel Jimenez-Aparicio, Javier Hernandez-Alvidrez, and Matthew J. Reno, “A Fast Microprocessor-Based Traveling Wave Fault Detection System for Electrical Power Networks,” IEEE Innovative Smart Grid Technologies (ISGT), DOI: 10.1109/ISGT51731.2023.10066370, Jan. 2023.
• Conference publication: Miguel Jimenez-Aparicio, Javier Hernandez-Alvidrez, and Matthew J. Reno, “Fast Traveling Wave Detection and Identification Method for Power Distribution Systems Using the Discrete Wavelet Transform,” IEEE Innovative Smart Grid Technologies (ISGT), DOI: 10.1109/ISGT51731.2023.10066438, Jan. 2023.

• Journal publication: Miguel Jimenez-Aparicio, Javier Hernandez-Alvidrez, Armando Y. Montoya, Matthew J. Reno, “Embedded, Real-Time, and Distributed Traveling Wave Fault Location Method Using Graph Convolutional Neural Networks” Energies, vol. 15, no. 20, DOI: 10.3390/en15207785, Oct. 2022.
• Conference publication: Miguel Jimenez-Aparicio, Matthew J. Reno, and John W. Pierre, “The High-Resolution Wavelet Transform: A Generalization of the Discrete Wavelet Transforms,” IEEE Ubiquitous Computing, Electronics, & Mobile Communication Conference (UEMCON), DOI: 10.1109/UEMCON54665.2022.9965716, Oct. 2022.
• Conference publication: Frank Miyagishima, Olga Lavrova, Sijo Augustine, Satish Ranade, Mathew J. Reno, and Javier Hernandez-Alvidrez, “Numerical Analysis of Traveling Waves in Power Systems with Grid Forming Inverters,” IEEE North American Power Symposium (NAPS), DOI: 10.1109/NAPS56150.2022.10012134, Oct. 2022.
• Conference publication: Miguel Jimenez-Aparicio, Matthew J. Reno, Felipe Wilches-Bernal, “Shapley Additive Explanations for Traveling Wave-based Protection on Distribution Systems,” IEEE North American Power Symposium (NAPS), DOI: 10.1109/NAPS56150.2022.10012195, Oct. 2022.
• Report: Holly Eagleston, Daniel Krofcheck, Forest Danford, Brian Pierre, Phillip Kay, Anthony Alberti, Iyare Oseghae, Robert Garrett, Westin Guthrie, “Dynamic Monitoring of Grid Vulnerability to Fire,” Sandia National Laboratories Report, SAND2022-14010, Sept. 2022.
• Report: Mathew J. Reno, Miguel Jimenez-Aparicio, Felipe Wilches-Bernal, Javier Hernandez-Alvidrez, Armando Montoya, Pedro Barba, Jack Flicker, Andrew Dow, Ali Bidram, Sajay K. Paruthiyil, Rudy Montoya, Binod Poudel, Benjamin Reimer, Olga Lavrova, Milan Biswal, Frank Miyagishima, Christopher Carr, Shubhasmita Pati, Satish J. Ranade, Santiago Grijalva, Shuva Paul, “Signal-Based Fast Tripping Protection Schemes for Electric Power Distribution System Resilience,” Sandia National Laboratories, SAND2022-13167, Sept. 2022.
• Workshop presentation: Miguel Jiménez Aparicio, “Asynchronous Traveling Wave-based Distribution System Protection with Graph Neural Networks,” Sandia Machine Learning and Deep Learning Workshop, July 2022.
• Conference publication: Javier Hernández-Alvídrez, Miguel Jiménez-Aparicio, and Matthew J. Reno, “Impact of Modeling Assumptions on Traveling Wave Protective Relays in Hardware in the Loop,” IEEE PES General Meeting, DOI: 10.1109/PESGM48719.2022.9916834, July 2022.
• Conference presentation: Holly Eagleston, Daniel Krofcheck, “Near-real-time Live and Dead Fuels Characterization: A Case Study for Infrastructure Resiliency to Wildfire in Southern California,” Fire and Climate Conference, Pasadena, CA, May 2022.
• Conference poster: Westin Guthrie. “Projecting vegetation condition and fire risk in southern California,” Fire and Climate Conference, Pasadena, CA, May 2022.
• Journal publication: Miguel Jimenez-Aparicio, Matthew J. Reno, Felipe Wilches-Bernal, “Traveling Wave Energy Analysis of Faults on Power Distribution Systems,” Energies, vol. 15, no. 8, DOI: 10.3390/en15082741, April 2022.
• Conference publication: Miguel Jimenez-Aparicio, Matthew J. Reno, and Felipe Wilches-Bernal, “Asynchronous Traveling Wave-based Distribution System Protection with Graph Neural Networks,” IEEE Kansas Power & Energy Conference (KPEC), DOI: 10.1109/KPEC54747.2022.9814818, April 2022.
• Conference publication: Shuva Paul, Santiago Grijalva, Miguel Jiménez-Aparicio, and Matthew J. Reno, “Knowledge-based Fault Diagnosis for a Distribution System with High PV Penetration,” IEEE Innovative Smart Grid Technologies (ISGT), DOI: 10.1109/ISGT50606.2022.9817496, April 2022.
• Conference publication: Felipe Wilches-Bernal, Miguel Jiménez-Aparicio, and Matthew J. Reno, “Algorithm for Fast Fault Location and Classification Based on Mathematical Morphology and Machine Learning,” IEEE Innovative Smart Grid Technologies (ISGT), DOI: 10.1109/ISGT50606.2022.9817473, April 2022.
• Conference publication: Felipe Wilches-Bernal, Miguel Jiménez-Aparicio, and Matthew J. Reno, “A Machine Learning-based Method using the Dynamic Mode Decomposition for Fault Location and Classification,” IEEE Innovative Smart Grid Technologies (ISGT), DOI: 10.1109/ISGT50606.2022.9817543, April 2022.
• Conference publication: Milan Biswal, Shubhasmita Pati, Satish J. Ranade, Olga Lavrova, and Matthew J. Reno, “Exploring the use of Shapelets in Traveling Wave based Fault Detection in Distribution Systems,” IEEE Texas Power and Energy Conference (TPEC), DOI: 10.1109/TPEC54980.2022.9750728, Feb. 2022.
• Workshop presentation: Matthew Reno, “AI-Based Protective Relays for Electric Grid Resiliency,” DOE Artificial Intelligence & Technology Office AI at DOE Workshop, Jan. 2022.