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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Dynamic concurrent learning method to neutralize cyber attacks and faults for industrial asset monitoring nodes

Input signals may be received from monitoring nodes of the industrial asset, each input signal comprising time series data representing current operation. A neutralization engine may transform the input signals into feature vectors in feature space, each feature vector being associated with one of a plurality of overlapping batches of received input signals. A dynamic decision boundary may be generated based on the set of feature vectors, and an abnormal state of the asset may be detected based on the set of feature vectors and a predetermined static decision boundary. An estimated neutralized value for each abnormal feature value may be calculated based on the dynamic decision boundary and the static decision boundary such that a future set of feature vectors will be moved with respect to the static decision boundary. An inverse transform of each estimated neutralized value may be performed to generate neutralized signals comprising time series data that are output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cyber Secure Sensor Network for Fossil Fuel Power Generation Assets Monitoring

The energy sector is undergoing digital transformation which is to say more and more power generation assets have become automated and connected to the internet. The connected sensors can tap into plants to monitor the health of assets and manage fleets remotely. These are just some of the benefits digitalization is bringing to the power industry. Within a power plant, control systems are no longer concerned with one system or one piece of equipment, but rather whole fleet of assets inter-connected with smart sensors which have the function of continuously monitoring and transmitting real-time operational data to operators. These connected systems will form a part of the industrial internet of things (IIoT). The big data scenarios provide benefits of performing system prognostics and optimization which is a key selling point of power plant digitalization.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

DISTRIBUTION TRANSFORMER ASSET MONITORING ON THE GRID EDGE USING SMART SENSOR DATA

As new loads such as rooftop photovoltaics, electric vehicles and other distributed energy resources become commonplace on the distribution grid, the stress on already aging assets begins to escalate. This increased loading and changing dynamics can exacerbate failure rates. While traditional monitoring efforts focus on transmission and generation assets, utilities are now beginning to pay close attention to distribution assets in order to increase reliability indices and reduce cost from unexpected outages. This research develops low-cost and scalable methods to monitoring the health of a critical distribution grid asset: the service transformer. Existing methods in literature are either invasive and thus difficult to implement or require the device to be tested offline in an expensive lab setting. Data from the ubiquitous smart meter as well as a novel Bluetooth based transformer monitor are leveraged to automatically notify the utility of deteriorating or damaged transformers. Voltage, temperature, and vibration are some of the signals measured and analyzed by the proposed algorithms to predict transformer failures. Furthermore, these algorithms are designed to keep the implementation and processing costs low by taking advantage of edge computing where possible.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2015

System and Method for Monitoring Distributed Asset Data

A computer-based monitoring system and monitoring method implemented in computer software for detecting, estimating, and reporting the condition states, their changes, and anomalies for many assets. The assets are of same type, are operated over a period of time, and outfitted with data collection systems. The proposed monitoring method accounts for variability of working conditions for each asset by using regression model that characterizes asset performance. The assets are of the same type but not identical. The proposed monitoring method accounts for asset-to-asset variability; it also accounts for drifts and trends in the asset condition and data. The proposed monitoring system can perform distributed processing of massive amounts of historical data without discarding any useful information where moving all the asset data into one central computing system might be infeasible. The overall processing is includes distributed preprocessing data records from each asset to produce compressed data.

Gorinevsky, Dimitry↗
DOE OSTI2020

Substation Secondary Asset Health Monitoring and Management System (SSHM)

Electric Power Group, LLC (EPG) was awarded DOE OE0000850 to design, develop, and demonstrate a real-time software application for substation secondary equipment health monitoring and management at host utility American Electric Power (AEP), a cost share partner. This project addresses the need for monitoring substation equipment health and providing operators with tools to identify and take pre-emptive action to avoid catastrophic equipment failure. By monitoring synchrophasor data in real time, data anomalies that indicate potential asset failure can be detected and alerts can be sent to operators in time to take corrective actions. EPG developed two data driven algorithms to identify abnormal equipment signature patterns as well as a method using substation linear state estimation (SLSE). The software has been deployed on hardened PC’s and tested and validated for cost share partner AEP’s substations - 138kV and 765 kV. The SSHM software has been accepted by AEP and final demonstration of DOE - OE0000850 for AEP and DOE was successfully completed on March 17th, 2020. EPG developed the Substation Secondary Asset Health Monitoring (SSHM) Platform to analyze equipment failure signatures in PMU data and alert substation personnel when pre-emptive inspection, repairs and other actions are warranted to prevent potential catastrophic failures. This is the first system that automatically monitors the health of substation secondary assets and alerts users to emerging failures using synchrophasor measurements. SSHM uses high resolution PMU data from PTs, CTs, and CCVTs to analyze equipment signatures and identify anomalies that are precursor indicators of potential equipment failure. When an anomaly is detected, there is a likelihood of potential failure of monitored equipment, the system alerts the user with visual alarms including alarm trend charts and indicator lights on a oneline diagram of the substation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Vector-Ordering Filter Procedure for Data Reduction

The vector-ordering filter (VOF) technique involves a procedure for sampling a large population of data vectors to select a subset of data vectors that fully characterize the state space of the large population. The VOF technique enables a large reduction of the volume of data that must be handled in the automated monitoring system and method discussed in the two immediately preceding articles. In so doing, the VOF technique enables the development of data-driven mathematical models of a monitored asset from sets of data that would otherwise exceed the memory capacities of conventional engineering computers. Data-driven mathematical models have been shown to offer high fidelity for purposes of control and monitoring of assets. In practice, a collection of asset-operating observations is acquired with the intention that the collection contain observations characteristic of the full dynamic range of operation of the asset. Often, such a collection contains an extremely large number of observations, many of which are redundant. The VOF technique fills the need for a means to extract, from the original collection of observational data, a reduced data matrix that excludes redundant data while maintaining the full statistical character and dynamic range of the original data. The reduced data matrix can then be used as the input data for development of a mathematical model of the monitored asset, or as training data for a neural-network substitute for an explicit mathematical model of the asset. Alternatively, the reduced data matrix can, itself, be used directly as a mathematical model of the monitored asset, as is commonly done in multivariate state-estimation techniques. The original data are collected from the asset over a range of operating states and are put in matrix form. Each column vector in the original data matrix represents the signal values acquired at a particular operational state of the asset. Thus, the number of columns of the original data matrix equals the number of observed states and the number of rows in this matrix equals the number of signals acquired at each observation. In the VOF technique, one extracts the reduced data matrix from the original data matrix through the selection of a representative subset of the column (state) vectors.

Bickford, Randall L.↗
DOE OSTI2022

Application of Margin-Based Methods to Assess System Health

Health management of complex systems such as nuclear power plants is an essential task to guarantee system reliability. This task can be greatly enhanced by constantly monitoring asset status and performances and process such data (through anomaly detection, diagnostic, and prognostic computational algorithms) to identify asset degradation trends and faulty states. While such information and data are typically available for many of the assets, they are not propagated from the asset to the system level in order to identify the most critical assets and prioritize maintenance and surveillance activities. The main reason is driven by the fact that current reliability modeling techniques are inadequate to process such information/data. This is due to the nature of these techniques which are based on the concept of failure rate/probability that do not serve an operational context where quantitative asset health information is available. Simply stated, current reliability techniques serve a run-to-failure operational setting and not a predictive maintenance one where the goal is to perform maintenance and surveillance activities only when they are needed based on asset health. The risk informed asset management (RIAM) project is focusing on the development of a different kind of reliability modeling techniques designed to adequately serve a predictive operational setting. Such reliability techniques move aways from a failure rate/probability to a margin-based mindset where margin is here used as a metric to quantify asset health based only on current and past operational experience of the asset under consideration. In addition, margin-based reliability techniques are able to propagate asset health information from the component to system level and provide importance measure to each asset. This report summarizes a recent activity performed in collaboration with plant modernization pathway designed to integrate monitoring data into margin-based reliability models. Such activity focuses on a specific system of an existing nuclear power plant where large amount of historic monitoring data is used to monitor asset and system health.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Optical Fiber Sensor Technology Development and Field Validation for Distribution Transformer and Other Grid Asset Health Monitoring

Power transformers are critical pieces of infrastructure in the electric grid that are both extremely expensive and difficult to replace. These transformers often have long lead times for replacement, and failures can create long service disruptions. This project has developed a new suite of sensors designed to give early warning of the impending failure of these important power transformers before it is too late and a major failure occurs. By using novel fiber optic sensors instead of conventional existing technologies, we are able to measure transformer characteristics indicative of impending failures in ways that were not previously possible. These new optical fiber-based sensors are completely immune to the strong magnetic fields present in power transformers, as well as being capable of using distributed measurement techniques. Distributed measurement techniques enable the fiber to return information all along its length as opposed to only collecting data at a single point; as would a thermocouple or standard pressure sensor.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2007

Web-Based Real-Time Emergency Monitoring

The Web-based Real-Time Asset Monitoring (RAM) module for emergency operations and facility management enables emergency personnel in federal agencies and local and state governments to monitor and analyze data in the event of a natural disaster or other crisis that threatens a large number of people and property. The software can manage many disparate sources of data within a facility, city, or county. It was developed on industry-standard Geo- Spatial software and is compliant with open GIS standards. RAM View can function as a standalone system, or as an integrated plugin module to Emergency Operations Center (EOC) software suites such as REACT (Real-time Emergency Action Coordination Tool), thus ensuring the widest possible distribution among potential users. RAM has the ability to monitor various data sources, including streaming data. Many disparate systems are included in the initial suite of supported hardware systems, such as mobile GPS units, ambient measurements of temperature, moisture and chemical agents, flow meters, air quality, asset location, and meteorological conditions. RAM View displays real-time data streams such as gauge heights from the U.S. Geological Survey gauging stations, flood crests from the National Weather Service, and meteorological data from numerous sources. Data points are clearly visible on the map interface, and attributes as specified in the user requirements can be viewed and queried.

Harvey, Craig A.↗
NASA NTRS2011

RAPID: Collaborative Commanding and Monitoring of Lunar Assets

RAPID (Robot Application Programming Interface Delegate) software utilizes highly robust technology to facilitate commanding and monitoring of lunar assets. RAPID provides the ability for intercenter communication, since these assets are developed in multiple NASA centers. RAPID is targeted at the task of lunar operations; specifically, operations that deal with robotic assets, cranes, and astronaut spacesuits, often developed at different NASA centers. RAPID allows for a uniform way to command and monitor these assets. Commands can be issued to take images, and monitoring is done via telemetry data from the asset. There are two unique features to RAPID: First, it allows any operator from any NASA center to control any NASA lunar asset, regardless of location. Second, by abstracting the native language for specific assets to a common set of messages, an operator may control and monitor any NASA lunar asset by being trained only on the use of RAPID, rather than the specific asset. RAPID is easier to use and more powerful than its predecessor, the Astronaut Interface Device (AID). Utilizing the new robust middleware, DDS (Data Distribution System), developing in RAPID has increased significantly over the old middleware. The API is built upon the Java Eclipse Platform, which combined with DDS, provides platform-independent software architecture, simplifying development of RAPID components. As RAPID continues to evolve and new messages are being designed and implemented, operators for future lunar missions will have a rich environment for commanding and monitoring assets.

Torres, Recaredo J.↗
DOE OSTI2021

Distributed, Intelligent Edge-Sensing for a Smarter Grid

The electric grid is undergoing major transformations and developments resulting in unprecedented levels of volatility, uncertainty, and stress on grid infrastructure. Smart sensors and methods aiding in advanced visibility and situational awareness are key for tackling these issues. In this work, a decentralized architecture is proposed, where sensing, local computation and control capability are embedded in the edge devices, communicating with a set of trusted 'data mules' in a 'delay-tolerant' manner, while functioning autonomously. This system has been designed and implemented as an overall platform – called Global Asset Monitoring, Management and Analytics (GAMMA) Platform intended to provide the backbone for a global array of sensors and actuators. Further, as a building block for advanced current sensing solutions, a smart, low-cost ‘clip-on’ current sensor based on PCB-embedded Rogowski coil has been developed. The sensor hosts a novel signal conditioning stage allowing an 'auto-tuning' feature, resulting in a universal current sensor design for measuring a wide range of currents, including faults for smart grid applications. Finally, the research proposes a method to instrument and monitor key parameters for the most common electric utility asset – the pole-top distribution transformer. The work done in this research enables scalable, edge-intelligent sensing solutions for monitoring grid infrastructure, allowing utility operators to gain advanced visibility in an economical way.

Kulkarni, Shreyas Bhalchandra↗
DOE OSTI2023

Project: Corbomite - Product: ConsoleWorks REACT

TDi Technologies presents ConsoleWorks REACT, an advanced platform designed to tackle the complexities of cyber and operational risk assessment. This comprehensive solution goes beyond asset-focused approaches by considering the impact of both assets and people have on the security and operation of critical infrastructure, specifically targeting preventing gird mis-operation by evaluating real-time human interaction or commands with critical assets. The hypothesis suggests that by integrating the assessment of user commands into the overall risk assessment process, organizations can make more informed decisions, prioritize resources effectively, and respond promptly to potential threats. This hypothesis forms the basis for the development of a proactive and holistic risk management approach that is more comprehensive and context aware than traditional models which only look at assets, patch levels, configuration and threat intel by collecting that information off the network vs directly from the asset, human, and human interaction all in real-time. ConsoleWorks' unique man-in-the-middle architecture is a key feature that sets it apart in the cybersecurity landscape. This architecture enables the real-time observation, enforcement, and commands or interaction risk transparency of user interaction with critical infrastructure to be risk mitigated and audited as they are aggregated with device and human risk factors for a more comprehensive risk threat score across a device or group of devices. This architecture allows ConsoleWorks to act as a secure intermediary between users and critical assets, monitoring all interactions and ensuring that only authorized commands are sent to the asset to be executed. This not only enhances security but also provides a comprehensive audit trail of all user activities, contributing to compliance efforts and facilitating incident investigation and risk management. By integrating this unique architecture with our comprehensive risk assessment methodology, ConsoleWorks REACT provides a powerful solution for managing cyber and operational risks, enabling organizations to maintain a robust security posture and effectively mitigate potential threats. To that end, the primary objective of this project was to research and develop a robust solution that enables the energy industry to mitigate the risks associated with human actions that can compromise the security or operations of assets critical to energy delivery, generation, transmission and operation. ConsoleWorks REACT plays a pivotal role in achieving this goal by leveraging its unique capabilities to monitor and track all user activity. Through its Zero Trust approach, which emphasizes continuous verification, the platform ensures secure access to assets and serves as the centralized human response and notification platform for addressing cyber and operational issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

TRANSENSOR: Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout. Final Report

Utilities across the world are wrestling with evolving market dynamics, population growth and climate change. Distributed Energy Resources (DERs) such as solar photovoltaics, distributed generators and energy storage systems are becoming important parts of the U.S. energy mix. These factors are driving an increasing need for low-cost grid asset monitoring. Aside from being costly, traditional utility monitoring systems are not sufficiently robust and do not provide real-time visibility into the condition of grid assets such as transformers. Lack of accurate real-time measurements on performance has resulted in the use of lagging indicators, such as oil sample analysis. To address this need, an innovative embedded optical sensing technology, Transformer Real-time Assessment Intelligent System (TRANSENSOR) was developed, validated and demonstrated in this project. To date, TRANSENSOR has focused on transformers but is extendable to other grid assets. In addition to the technology being embedded into new transformers during manufacturing, a retrofit configuration that can be installed on existing transformers in the field was also developed. It is anticipated that the ability to retrofit existing transformers will help accelerate adoption of the underlying TRANSENSOR technology. Phase 1 of the project focused on laboratory development and qualification of the technology. Following iterations and exploration of relevant optical sensing modalities and multiplexed configurations of interest for the transformer environment, an effective candidate configuration was agreed upon, down-selected and custom-designed for embedding into General Electric (GE) network transformers. Two new GE 500 kilovolt ampere (kVA), 27 kilovolt (kV) distribution network transformers were built with embedded fiber-optic (FO) sensors and successfully qualified per industry standards at GE’s Shreveport, Louisiana facility during Phase 1 of the project. Following the successful completion of Phase 1, the team proceeded to Phase 2, which focused on a field demonstration of the technology. Over Phase 2, the first new GE transformer built with embedded fiber-optic sensors was installed in an above-ground cage and connected to the grid at Con Edison’s Astoria facility. A second transformer equipped with TRANSENSOR was installed in an underground vault. Additionally, an older (1982 year model) GE transformer in an above-ground cage was retrofitted with fiber- optic sensors and reconnected to the grid at ConEd’s facility. Analysis of data acquired from the sensors showed interesting correlations with transformer loading. Additionally, key events such as transformer low-voltage network connection, primary-side energizing, and a pressure loss event from an oil sampling were detected by the TRANSENSOR system. Online data processing/feature extraction algorithms for the second transformer in the underground vault detected key features and event alerts that were transmitted through a wireless 4G connection. The remote deployment concept showed promising results with data collection running for a total of 8 months across the three (3) GE transformers instrumented for the Phase 2 field demonstration. This sets the stage well for further development and commercialization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Distribution Transformer Health Monitoring using Smart Meter Data

The distribution electric grid has become a highly complex and intelligent network with changing load and customer types. This has generated unprecedented challenges and opportunities for utility companies–opportunities especially in the area of asset health/performance management. Moreover, several utilities are increasingly moving from the traditional reactive and time-based asset monitoring approach to a more proactive condition based method. However, this needs to be done in a low-cost and efficient manner. Here, this paper explores how existing sensor infrastructure such as smart meters can be utilized to provide utility operators with more visibility into the health and operation of their assets. The paper focuses primarily on service transformers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Distributed sensing of electromagnetic components using optical fiber based methods

An OFDR based fiber-optics sensor for distributed real-time temperature rise monitoring of a transformer in operation has been disclosed. The fiber-optic sensor provides an effective solution to monitoring the physical structures of the transformer core, as well as accurately detecting the non-uniform temperature distribution inside the transformer, and thus provides innovative feedback to the transformer design by minimizing the core losses. Additionally, the method may be responsive to the presence of magnetic and electric fields, as well as responsive to various chemical species. The method allows novel approaches to real-time asset monitoring of power transformers while operational.

Ohodnicki, Paul R.↗
NASA NTRS1995

ORBCOMM: Initial operations

ORBCOMM, a subsidiary of Orbital Sciences Corporation (OSC), has designed a system to provide low-cost mobile two-way data communications for worldwide commercial markets. The primary application areas are data communications and messaging. The ORBCOMM System has been designed so a user can compose, transmit, and receive messages; control and monitor assets; and collect data on hand-held subscriber communicators (SC's). With a constellation of 36 LEO satellites and terrestrial facilities, users in the temperature climate zones around the world will have a satellite in view over 98% of the time, and will have to wait less than two minutes the rest of the time to directly access a satellite. A message transmitted from a SC and received by the satellite is relayed down to a regional gateway earth station (GES). The GES then transmits the message via fiber land-lines to the network control center (NCC). The NCC then determines the location of the recipient of the message and routes the message accordingly. The NCC can receive and transmit messages from terrestrial networks via X.400 and X.25 gateways. The NCC can translate messages from widely used e-mail systems such as Internet, cc:Mail, and Microsoft Mail into X.400 messages for transmission to and from ORBCOMM SC's.

Schoen, David↗