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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

Recent proliferation of distributed energy sources in distribution or sub-transmission systems necessitates close monitoring of these three-phase power grids which typically operate under unbalanced loading conditions. Unlike the transmission systems where the network equations are commonly based on the positive sequence component models, a detailed three phase model will have to be used in implementing network applications for these systems. In the specific case of the state estimator, where measurement and parameter errors may bias the solution, bad data and parameter error detection algorithms should also be incorporated. Implementing the state estimator and error detection algorithms for three-phase systems impose additional computational burden and modifications to the state estimation code. This paper proposes a practical solution to avoid these issues by using synchronized phasor measurements and modal decoupling. The previously developed parameter error detection algorithm based on the normalized Lagrange multipliers (NLM) test is applied to the measurements independently in each mode in parallel, not only saving CPU time but also avoiding new code development for a three-phase estimator. Different parameter error scenarios are created and tested to verify the effectiveness of the proposed error detection approach.

Khalili, Ramtin↗
DOE OSTI2024

A Case Study in NREL DERMS Asset Management Implementation

The National Renewable Energy Laboratory (NREL) and Smarter Grid Solutions (SGS) plan to present a case study on their implementation of SGS' Strata Grid Distributed Energy Resource Management System at NREL's Energy Systems Integration Facility (ESIF). The ESIF is one of the nation's premier energy systems integration laboratory facilities focused on development and deployment of clean energy technologies and resilient delivery systems. NREL looked for a DERMS to install at the ESIF that is capable of replicating real world scenarios to control and monitor distributed devices from small residential scale to the grid substation level. The DERMS system demonstrates the use cases of beneficial operation and coordination of modern grid devices, leverage DERs for improved grid planning and operation, demand-side management and customer engagement through bidirectional communication with utilities and energy market operations. The presentation will cover the delivery strategy of NREL and SGS, how the use cases help NREL's research, and NREL and SGS plan to expand the system and use cases.

ARIES↗
DOE OSTI2024

A Microwave Photonics Optical Fiber Method for Measuring Distributed Strain for Hydrologic Applications in the Vadose and Saturated Zones

Distributed strain measurements appear to hold significant potential for monitoring hydrologic processes. Coherence-length-gated Microwave Photonics Interferometry (CMPI) is a distributed sensing technique that measures strain in an optical fiber by reading optical interference phase changes in the microwave domain. The technique provides 10nε resolution when cm spatial resolution is applied. CMPI was used to measure the strain caused by small periodic variations in air pressure (25 Pa amplitude and 4 Hz) in a sand-filled laboratory column used to represent barometric loading in the vadose zone. The strain varied as a periodic function with an amplitude that decreased and a phase that increased with depth. The distribution of amplitude and phase of the strain depended on the water saturation, permeability, and presence of a barrier at the top of the sand. A theoretical analysis suggests that the air pressure diffusivity estimated from pressure data is similar to the diffusivity estimated from the distributed strain. These results indicated that the CMPI distributed strain measurement system could be used to improve monitoring and characterization of the vadose and underlying saturated zone.

Hua, Liwei↗
DOE OSTI2020

Designing a decentralized fault-tolerant software framework for smart grids and its applications

The vision of the ‘Smart Grid’ anticipates a distributed real-time embedded system that implements various monitoring and control functions. As the reliability of the power grid is critical to modern society, the software supporting the grid must support fault tolerance and resilience of the resulting cyber-physical system. This paper describes the fault-tolerance features of a software framework called Resilient Information Architecture Platform for Smart Grid (RIAPS). The framework supports various mechanisms for fault detection and mitigation and works in concert with the applications that implement the grid-specific functions. The paper discusses the design philosophy for and the implementation of the fault tolerance features and presents an application example to show how it can be used to build highly resilient systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Development of High-Temperature Bonding Techniques to Enable High-Temperature Static or Dynamic Strain Measurements

Current light-water nuclear reactors rely on a variety of different sensors and sensor applications to meet their structural health monitoring needs throughout the entirety of the reactor primary, secondary, and containment systems. Optical fiber–based sensor technologies could provide solutions to reduce the sensor system footprint while enhancing the measurement fidelity and spatial resolution by leveraging distributed monitoring techniques. Moreover, advanced reactors may require optical fiber–based sensors for structural health monitoring because their operating temperatures will exceed the limits of conventional transducers used to acquire dynamic strain or acoustic data in nuclear power plants. Therefore, this report describes experiments targeting the development of high-temperature bonding techniques that would allow for potentially long lengths of fibers to be bonded to metallic reactor components in advanced reactor systems. The high temperatures experienced within target application, next-generation nuclear reactors, necessitate a high-temperature resistant bond to limit the amount of tension on the fiber at the target application temperature. The primary bonding method investigated in this work is brazing; hot-rolling has also been investigated to a lesser extent. Both techniques are well-suited to bonding optical fibers to large reactor components such as primary coolant piping, pressure vessels, or heat exchangers. Optical frequency domain reflectometry was used to monitor the strain in metal-coated optical fibers before, during, and after the high-temperature bonding process. On select optical fibers that were successfully bonded, additional thermal cycling was performed to assess the extent to which the fiber remained bonded based on the expected thermal expansion of the test specimen material. The results of the various experiments yielded the following general conclusions: (1) brazing is a viable technique for bonding and allows significant compressive strain to be applied to the fiber at room temperature; (2) hot-rolling is a viable technique as well, which has been more optimized than the brazing technique for bonding, but less residual compressive strain has been observed with this technique; and (3) both techniques will need further development and optimization to demonstrate bonding of a long length of fiber that can provably operate at relevant temperatures for an advanced nuclear reactor application.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Chapter Ten - Power, Buildings, and Other Critical Networks: Integrated Multisystem Operation

The electrifying transportation sector, the increasing grid interactivity of the built environment, the rapidly expanding number of devices within the Internet-of-Things, and the overall trend toward highly connected systems and interdependent networks is revolutionizing the operation of the electric power grid. While these changes are presenting grid operators with new challenges to ensure an efficient, reliable, and sustainable operation of the grid, they also enable a new suite of resources that can be utilized for assisting the grid in times of need. In this chapter, we explore how these recent changes and trends are affecting modern power systems and discuss the benefits and challenges of an increasingly electrified and interconnected world. We will observe how various critical infrastructure, that is, buildings, water and gas, transportation, and telecommunication networks are highly dependent on power network operations but, with improved coordination and control, can also provide valuable assets to the grid in times of need.

electricity markets↗
DOE OSTI2025

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Distributed fiber optic sensing is a cutting-edge technology that has found extensive applications in the monitoring of Ensuring the safety, integrity, and operational efficiency of underground product pipelines is vital for maintaining the nation’s critical infrastructure. Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensors—were employed to measure key parameters like hoop strain, pressure, and acoustic vibrations. The underground product pipeline's outer diameter is 30 inches, the wall thickness is 1.28 inches, and the 3-foot depth. The fiber deployment strategies, and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety.

distributed fiber sensing↗
DOE OSTI2024

Detecting fractures and monitoring hydraulic fracturing processes at the first EGS Collab testbed using borehole DAS ambient noise

Enhanced geothermal systems (EGS) require cost-effective monitoring of fracture networks. We validate the capability of using borehole distributed acoustic sensing (DAS) ambient noise for fracture monitoring using core photos and core logs. The EGS Collab project has conducted 10 m scale field experiments of hydraulic fracture stimulation using 50–60 m deep experimental wells at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The first EGS Collab testbed is located at 1616.67 m (4850 ft) depth at SURF and consists of one injection well, one production well, and six monitoring wells. All wells are drilled subhorizontally from an access tunnel called a drift. The project uses a single continuous fiber-optic cable installed sequentially in the six monitoring wells to record DAS data for monitoring hydraulic fracturing during stimulation. We analyze 60 s time records of the borehole DAS ambient noise data and compute the noise root-mean-square (rms) amplitude on each channel (points along the fiber cable) to obtain DAS ambient noise rms amplitude depth profiles along the monitoring wellbore. Our noise rms amplitude profiles indicate amplitude peaks at distinct depths. We compare the DAS noise rms amplitude profiles with borehole core photos and core logs and find that the DAS noise rms amplitude peaks correspond to the locations of fractures or lithologic changes indicated in the core photos or core logs. We then compute the hourly DAS noise rms amplitude profiles in two monitoring wells during three stimulation cycles in 72 h and find that the DAS noise rms amplitude profiles vary with time, indicating the fracture opening/growth or closing during the hydraulic stimulation. Our results demonstrate that borehole DAS passive ambient noise can be used to detect fractures and monitor fracturing processes in EGS reservoirs.

58 GEOSCIENCES↗
DOE OSTI2022

Real-Time Monitoring of Gas-Phase and Dissolved CO 2 Using a Mixed-Matrix Composite Integrated Fiber Optic Sensor for Carbon Storage Application

Novel chemical sensors that improve detection and quantification of CO 2 are critical to ensuring safe and cost-effective monitoring of carbon storage sites. Fiber optic (FO) based chemical sensor systems are promising field-deployable systems for real-time monitoring of CO 2 in geological formations for long-range distributed sensing. Here, a mixed-matrix composite integrated FO sensor system was developed with a purely optical readout that reliably operates as a detector for gas-phase and dissolved CO 2 . A mixed-matrix composite sensor coating consisting of plasmonic nanocrystals and hydrophobic zeolite embedded in a polymer matrix was integrated on the FO sensor. The mixed-matrix composite FO sensor showed excellent reversibility/stability in a high humidity environment and sensitivity to gas-phase CO 2 over a large concentration range. This remarkable sensing performance was enabled by using plasmonic nanocrystals to significantly enhance the sensitivity and a hydrophobic zeolite to effectively mitigate interference from water vapor. The sensor exhibited the ability to sense CO 2 in the presence of other geologically relevant gases, which is of importance for applications in geological formations. A prototype FO sensor configuration which possesses a robust sensing capability for monitoring dissolved CO 2 in natural water was demonstrated. Reproducibility was confirmed over many cycles, both in a laboratory setting and in the field. More importantly, we demonstrated on-line monitoring capabilities with a wireless telemetry system, which transferred the data from the field to a website. The combination of outstanding CO 2 sensing properties and facile coating processability makes this mixed-matrix composite FO sensor a good candidate suitable for practical carbon storage applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sensing Electrical Networks Securely & Economically (SENSE)

The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Transmission and Distribution Real-Time Analysis Software for Monitoring and Control: Design and Simulation Testing

The US electric grid is facing operational, stability, and security challenges. Transmission system operators need some measure of visibility into distribution system renewable generation. Distribution system generation needs to support transmission system voltage. The grid is experiencing an expansion in measurement systems. How to take full advantage of this expansion and defend against attacks, both cyber and physical, poses additional challenges. This paper introduces software designed to meet these challenges. At the center of the software is an Integrated System Model (ISM) that spans from transmission to secondary distribution. The ISM is employed in real-time abnormality detection, voltage stability forecasting, and multi-mode control. The software architecture along with selected analysis modules is presented. Testing results are presented for: 1—attacks on utility infrastructure; 2—energy savings from optimal control; 3—distribution system control response during a low voltage transmission system event; 4—cyber-attacks on PV inverters, where physical inverters are used in hardware-in-the-simulation-loop studies. Contributions of this work include real-time analysis that spans from three-phase transmission through secondary distribution; an approach for detecting abnormalities that employs measurements from three independent measurement systems; and a multi-mode distribution system control that responds to cyber-attacks, physical attacks, equipment failures, and transmission system needs.

14 SOLAR ENERGY↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2025

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensor systems were tested to measure the key parameters, such as hoop strain, pipe pressure, surrounding soil temperature, and acoustic vibrations. The underground product pipeline’s outer diameter is 30 inches, the wall thickness is 1.28 inches, and 3 feet deep from the surface. The fiber deployment strategies and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety. These findings from pilot-scale testing offer valuable insights into advancing pipeline monitoring technologies and improving the reliability of underground pipeline systems.

fiber optic sensors↗
DOE OSTI2023

Trust Model System for the Energy Grid of Things Network Communications

Network communication is crucial in the Energy Grid of Things (EGoT). Without a network connection, the energy grid becomes just a power grid where the energy resources are available to the customer uni-directionally. A mechanism to analyze and optimize the energy usage of the grid can only happen through a medium, a communications network, that enables information exchange between the grid participants and the service provider. Security implementers of EGoT network communication take extraordinary measures to ensure the safety of the energy grid, a critical infrastructure, as well as the safety and privacy of the grid participants. With the dynamic nature of network communication of the EGoT, the information provided by the customer or the service provider can be falsified by a malicious attacker. Therefore, a trust model is necessary to monitor any abnormal activities. This paper describes a distributed trust model system that meets the need of the EGoT. This paper describes methods for evaluating and improving the distributed trust model using standard hypothesis testing metrics such as true positive, false positive, true negative, false negative, equal error rate, and F1 score. Example calculations are shown based on generated sample data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2024

In-Process Monitoring and Structural Health Monitoring of Large-Scale Additive Manufacturing Using Acoustic Emission Technique

ORNL collaborated with MISTRAS Group, Inc. to investigate acoustic emission (AE) as a structural health monitoring (SHM) method for large-scale additive manufacturing (AM). Large-scale AM is being adapted as method of producing large structures in a short lead time and cost-effective way. With the growing advancement in AM techniques and application, machine monitoring and part qualification is highly needed. There has been leading research focused on the manufacturing, feedstock material but minimum research on the SHM, defect detection, and nondestructive evaluation (NDE) for AM. Scanning large structure using conventional nondestructive testing (NDT) techniques, such as ultrasound or X-ray, and searching for potential defects can be very time consuming, challenging and cost prohibitive. AE is a passive technique that can be used to monitor and locate defect progression in large structure by distributing group of sensors around the part. This project utilized AE technique and system manufactured/designed by MISTRAS Group to monitor large-scale AM equipment (i.e. Big Area Additive Manufacturing (BAAM) system located at the Oak Ridge National Laboratory – Manufacturing Demonstration Facility (ORNL-MDF) and the printed parts it produces. The AE system provided valuable insight on defect development/progression during and post-printing process.

36 MATERIALS SCIENCE↗