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Title: Low-Cost, Robust, Threat-Aware Wireless Sensor Network for Assuring the Nation's Energy Infrastructure

Abstract

In lieu of performing laboratory testing, Eaton Corporation and Oak Ridge National Laboratories (ORNL) conducted an additional field test in March 2007 at ORNL facilities. The results of this test summarized in the report entitled 'DE-FC26-04NT42071, Final Technical Report' submitted to the Department of Energy on June 27, 2007.

Authors:
;
Publication Date:
Research Org.:
Eaton Corporation
Sponsoring Org.:
USDOE
OSTI Identifier:
924028
DOE Contract Number:
FC26-04NT42071
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; FIELD TESTS; SENSORS; DETECTION; SABOTAGE; ENERGY SOURCES; NATIONAL SECURITY

Citation Formats

Carlos H. Rentel, and Peter J. Marshall. Low-Cost, Robust, Threat-Aware Wireless Sensor Network for Assuring the Nation's Energy Infrastructure. United States: N. p., 2007. Web. doi:10.2172/924028.
Carlos H. Rentel, & Peter J. Marshall. Low-Cost, Robust, Threat-Aware Wireless Sensor Network for Assuring the Nation's Energy Infrastructure. United States. doi:10.2172/924028.
Carlos H. Rentel, and Peter J. Marshall. Fri . "Low-Cost, Robust, Threat-Aware Wireless Sensor Network for Assuring the Nation's Energy Infrastructure". United States. doi:10.2172/924028. https://www.osti.gov/servlets/purl/924028.
@article{osti_924028,
title = {Low-Cost, Robust, Threat-Aware Wireless Sensor Network for Assuring the Nation's Energy Infrastructure},
author = {Carlos H. Rentel and Peter J. Marshall},
abstractNote = {In lieu of performing laboratory testing, Eaton Corporation and Oak Ridge National Laboratories (ORNL) conducted an additional field test in March 2007 at ORNL facilities. The results of this test summarized in the report entitled 'DE-FC26-04NT42071, Final Technical Report' submitted to the Department of Energy on June 27, 2007.},
doi = {10.2172/924028},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Mar 30 00:00:00 EDT 2007},
month = {Fri Mar 30 00:00:00 EDT 2007}
}

Technical Report:

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  • The objective of this project was to create a low-cost, robust anticipatory wireless sensor network (A-WSN) to ensure the security and reliability of the United States energy infrastructure. This document highlights Eaton Corporation's plan to bring these technologies to market.
  • Eaton, in partnership with Oak Ridge National Laboratory and the Electric Power Research Institute (EPRI) has completed a project that applies a combination of wireless sensor network (WSN) technology, anticipatory theory, and a near-term value proposition based on diagnostics and process uptime to ensure the security and reliability of critical electrical power infrastructure. Representatives of several Eaton business units have been engaged to ensure a viable commercialization plan. Tennessee Valley Authority (TVA), American Electric Power (AEP), PEPCO, and Commonwealth Edison were recruited as partners to confirm and refine the requirements definition from the perspective of the utilities that actually operatemore » the facilities to be protected. Those utilities have cooperated with on-site field tests as the project proceeds. Accomplishments of this project included: (1) the design, modeling, and simulation of the anticipatory wireless sensor network (A-WSN) that will be used to gather field information for the anticipatory application, (2) the design and implementation of hardware and software prototypes for laboratory and field experimentation, (3) stack and application integration, (4) develop installation and test plan, and (5) refinement of the commercialization plan.« less
  • Eaton has developed an advanced energy management solution that has been deployed to several Industries of the Future (IoF) sites. This demonstrated energy savings and reduced unscheduled downtime through an improved means for performing predictive diagnostics and energy efficiency estimation. Eaton has developed a suite of online, continuous, and inferential algorithms that utilize motor current signature analysis (MCSA) and motor power signature analysis (MPSA) techniques to detect and predict the health condition and energy usage condition of motors and their connect loads. Eaton has also developed a hardware and software platform that provided a means to develop and test thesemore » advanced algorithms in the field. Results from lab validation and field trials have demonstrated that the developed advanced algorithms are able to detect motor and load inefficiency and performance degradation. Eaton investigated the performance of Wireless Sensor Networks (WSN) within various industrial facilities to understand concerns about topology and environmental conditions that have precluded broad adoption by the industry to date. A Wireless Link Assessment System (WLAS), was used to validate wireless performance under a variety of conditions. Results demonstrated that wireless networks can provide adequate performance in most facilities when properly specified and deployed. Customers from various IoF expressed interest in applying wireless more broadly for selected applications, but continue to prefer utilizing existing, wired field bus networks for most sensor based applications that will tie into their existing Computerized Motor Maintenance Systems (CMMS). As a result, wireless technology was de-emphasized within the project, and a greater focus placed on energy efficiency/predictive diagnostics. Commercially available wireless networks were only utilized in field test sites to facilitate collection of motor wellness information, and no wireless sensor network products were developed under this project. As an outgrowth of this program, Eaton developed a patented energy-optimizing drive control technology that is complementary to a traditional variable frequency drives (VFD) to enable significant energy savings for motors with variable torque applications, such as fans, pumps, and compressors. This technology provides an estimated energy saving of 2%-10% depending on the loading condition, in addition to the savings obtained from a traditional VFD. The combination of a VFD with the enhanced energy-optimizing controls will provide significant energy savings (10% to 70% depending on the load and duty cycle) for motors that are presently connected with across the line starters. It will also provide a more favorable return on investment (ROI), thus encouraging industries to adopt VFDs for more motors within their facilities. The patented technology is based on nonintrusive algorithms that estimate the instantaneous operating efficiency and motor speed and provide active energy-optimizing control of a motor, using only existing voltage and current sensors. This technology is currently being commercialized by Eaton’s Industrial Controls Division in their next generation motor control products. Due to the common nonintrusive and inferential nature of various algorithms, this same product can also include motor and equipment condition monitoring features, providing the facility owner additional information to improve process uptime and the associated energy savings. Calculations estimated potential energy savings of 261,397GWh/Yr ($15.7B/yr), through retrofitting energy-optimizing VFDs into existing facilities, and incorporating the solution into building equipment sold by original equipment manufacturers (OEMs) and installed by mechanical and electrical contractors. Utilizing MCSA and MPSA for predictive maintenance (PM) of motors and connected equipment reduces process downtime cost and the cost of wasted energy associated with shutting down and restarting the processes. Estimated savings vary depending on the industry segment and equipment criticality per facility/process. Average downtime for an industrial facility is 4-12 hours with a cost/hr of $7500/hr, with large, critical processes reaching $50-100k/hr. Specific downtime costs are not included in this report because of customer confidentiality, but projected savings across the Industries of the Future (IoF) are still expected to be comparable to the original program estimates. Two generations of customer field deployments and evaluation have been completed during the course of this project. Results from these customer sites have been used for identifying the scope and improving the developed energy and wellness algorithms. The field deployments have confirmed that the hardware for sensing and sampling motor currents and voltages are reliable and able to provide an adequate signal-to-noise ratio from the electrical noise present on the motor signals.« less
  • Data centers occupy less than 2% of the federally owned portfolio under the jurisdiction, custody or control of the U.S. General Services Administration (GSA), but represent nearly 5% of the agency’s overall energy budget. Assuming that energy use in GSA’s data centers tracks with industry averages, GSA can anticipate that data center energy use will grow at an annual rate of 15%, a doubling of energy use every five years.1 In fact, energy is the single largest operating expense for most data centers. Improving the energy performance of data center systems supports progress toward meeting federally mandated greenhouse gas emission-­reductionmore » goals, while reducing operating and energy costs and allowing for greater flexibility in future expansion by eliminating the need to provide additional power and cooling. Studies sponsored by the U.S. Department of Energy (DOE) and the U.S. Environmental Protection Agency (EPA) have shown that energy use can be reduced by 25% through implementation of best practices and commercially available technologies. The present study evaluated the effectiveness of a strategy to cost- effectively improve the efficiency of data center cooling, which is the single largest non-­IT load. The technology that was evaluated consists of a network of wireless sensors—including branch circuit power monitors, temperature sensors, humidity sensors, and pressure sensors, along with an integrated software product to help analyze the collected data. The technology itself does not save energy; however, its information collection and analysis features enable users to understand operating conditions and identify problem areas. In addition, data obtained by this technology can be input into assessment tools that can identify additional best practice measures. Energy savings result from the implementation of the best practices. The study was conducted to validate the premise that providing data center operators with detailed, real- time measurement of environmental parameters and power consumption enables them to establish baseline performance, discover areas of sub-optimal performance, and identify concrete opportunities for improvement.« less