Wireless Sensor Network for Advanced Energy Management Solutions

Theisen, Peter J. ; Bin Lu, Charles J. Luebke

Eaton Corporation, Innovation Center, 4201 North 27th Street, Milwaukee, WI 53216

关键词: Blowers;    Criticality;    Manufacturers;    Monitoring;    Signal-To-Noise Ratio;   

DOI  :  10.2172/964682
RP-ID  :  DOE/GO14000-1
RP-ID  :  FC36-04GO14000
RP-ID  :  964682

美国|英语

来源: UNT Digital Library

【 摘 要 】

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 these 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.