Estimation of Variable-Speed-Drive Power Consumption From Harmonic Content

Lee, K.D.; Leeb, Steven B.; Norford, L.K.; Armstrong, Peter; Holloway, Jack W.; Shaw, Steven R.

doi:10.1109/tec.2005.852963

Cited by 100 publications

(56 citation statements)

References 15 publications

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“…A robust empirical literature on risk perceptions could potentially help improve education and ameliorate concerns (e.g., Slovic, 1987). Additional work extends learnings regarding privacy from other domains to energy sensors such as smart meters and home area networks (Pai et al, 2008;Mulligan & Perzanowski, 2007;Lisovich & Wicker, 2008;Subrahmanyam, 2008 Norfold and Leeb (1996), Lee et al (2005) …”

Section: Institute Policies To Ensure That Utilities Enable the Han Cmentioning

confidence: 79%

“…Early research focused on disaggregating simple resistive appliances that have a finite number of operating states, which are typical for the residential sector (e.g., Hart, 1992;Sultanem, 1991;Marceau and Zmeureanu, 2000;Leeb et al, 1995). Some effort (e.g., Roos et al, 1994;Lee et al, 2005) has also been invested in studying electric appliances in the industrial and commercial sector, which commonly have more complicated operating principles (e.g., variable power devices such as motors).…”

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

“…Multiple studies attempt to extend the types of identifiable loads to lower-power, more complex devices found in modern homes and offices (e.g., computer, copier) or industrial buildings (e.g., variable speed fans) (Laughman et al, 2003;Lee, 2003;Shaw et al, 1998;Lee et al, 2005). In Shaw et al (1998), current waveforms were collected at a sampling rate of several kHz and used to compute in-phase and quadrature spectral envelopes (time-dependent, averaged Fourier coefficients) that characterize harmonic behavior.…”

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

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Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

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Section: Institute Policies To Ensure That Utilities Enable the Han Cmentioning

confidence: 79%

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

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Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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“…The NILM has already been demonstrated effective in residential, commercial [3], [4] and automotive environments [5]. The research presented in this thesis applies the concept of non-intrusive load monitoring to shipboard engineering systems.…”

Section: Motivation For Researchmentioning

confidence: 99%

“…The NILM can also be used to diagnose faults that commonly occur in electromechanical systems like HVAC plants [14]. The NILM's ability to examine harmonic current information can be used to create performance metrics for variable speed drives and to study the electrical interference caused by power converters [4].…”

Section: Objective and Outline Of Thesismentioning

confidence: 99%

Diagnostic indicators for shipboard systems using non-intrusive load monitoring

DeNucci

Cox

Leeb

et al. 2005

IEEE Electric Ship Technologies Symposium, 2005.

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Field studies have demonstrated that the Non-Intrusive Load monitor (NILM) can provide real-time indication of the condition of electro-mechanical systems on board naval vessels. Results from data collected from engineering systems on board USCGC SENECA , a 270-foot U.S. Coast Guard cutter, indicate that the NILM can effectively identify faults, failures and deviations from normal operating conditions on numerous shipboard engineering systems.Data collected from the sewage system identified metrics that can be applied, for example, to cycling systems (high pressure air, hydraulic systems, etc.) to differentiate between periods of heavy usage and fault conditions. Sewage system variability and randomness was minimized by employing a MATLAB simulation designed to permit exploration of system behavior that might not have been exposed during other conditions. Simulation data suggests that the presence, location and magnitude of a spike in the pump run distribution indicated the presence of a leak. Data from the actual shipboard system, when subjected to a quantifiable leak, displayed the same behavior.Data collected from the Auxiliary Seawater (ASW) System indicated that the NILM is able to predict the failure of a flexible coupling linking the pump and motor components. The ASW motor-pump system was modeled using a 5 th order induction motor simulation to explore the electro-mechanical relationships between the motor, coupling and pump. Changes to the mechanical parameters of the coupling were captured in the electrical signature of the motor in both the simulation and shipboard data.Frequency domain analysis of the ASW System data also suggested that the clogging of a heat exchanger on a critical shipboard system can be identified with the NILM, although the extent of diagnosis is dependent on the system flow patterns. Further development of hardware and software, along with continued research into the behavior of shipboard systems, will allow the NILM to augment existing monitoring systems and potentially serve as a stand-alone indicator of critical system performance.

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