Non‐intrusive induction motor speed detection

Orji, Uzoma A.; Remscrim, Zachary; Schantz, Christopher; Donnal, John; Paris, James; Gillman, Mark; Surakitbovorn, Kawin; Leeb, Steven B.; Kirtley, James L.

doi:10.1049/iet-epa.2014.0274

Cited by 12 publications

(2 citation statements)

References 33 publications

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“…The saturation upper and lower levels are determined on the basis of the reference speed ω r * and the determined actual slip ω sl * . For each saturation sector, the distance between upper and lower limits must be set higher than the value of a fullload slip at nominal speed, which is typically 0.05 pu [18]. This constraint also imposes a limit on the number of saturation sectors N (-) being available for the selection.…”

Section: Adaptive Adjustment Of Dynamic Saturation Limitsmentioning

confidence: 99%

Adaptive V/f-Based Control for Induction Machines in Distributed Electric Propulsion Systems

Sztykiel

Peña-Alzola

Jones

et al. 2018

2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles &Amp; Interna

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a novel adaptive control function is presented as an improvement to the classic volts per hertz (V/f) technique where high torque oscillations occur during rapid changes of the rotating speed. The proposed adaptive feature of an improved controller limits torque oscillations by injecting a pulse and imposing the predetermined bands on a voltage reference waveform whenever the scheduled change in speed occurs. This adaptive function has been integrated with the closed-loop V/f control scheme, subsequently modelled and evaluated numerically using transient simulation studies, as well as experimentally using an induction machine drive test rig. The results have shown that the proposed adaptive element provides an improved dynamic performance for using the V/f method, which is required in compact distributed drive systems with single voltage source inverter (VSI) controlling multiple interconnected motors.

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Section: Adaptive Adjustment Of Dynamic Saturation Limitsmentioning

confidence: 99%

Adaptive V/f-Based Control for Induction Machines in Distributed Electric Propulsion Systems

Sztykiel

Peña-Alzola

Jones

et al. 2018

2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles &Amp; Interna

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“…NILM is an intelligent energy monitoring technique which utilizes a single energy monitor to retrieve information of appliances from aggregated loads, such as power consumption and appliance type, in a non-intrusive way. Apart from energy savings, NILM is a helpful tool for predictive maintenance [1] or the determination of motor speed [31]. 08 Figure 1: The first plot shows an actual OFF, followed by an ON event of a monitor.…”

Section: Introductionmentioning

confidence: 99%

Appliance Event Detection - A Multivariate, Supervised Classification Approach

Kahl

Kriechbaumer

Jorde

et al. 2019

Proceedings of the Tenth ACM International Conference on Future Energy Systems

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Non-intrusive load monitoring (NILM) is a modern and still expanding technique, helping to understand fundamental energy consumption patterns and appliance characteristics. Appliance event detection is an elementary step in the NILM pipeline. Unfortunately, several types of appliances (e.g., switching mode power supply (SMPS) or multi-state) are known to challenge state-of-theart event detection systems due to their noisy consumption profiles. Classical rule-based event detection system become infeasible and complex for these appliances. By stepping away from distinct event definitions, we can learn from a consumer-configured event model to differentiate between relevant and irrelevant event transients.We introduce a boosting oriented adaptive training, that uses false positives from the initial training area to reduce the number of false positives on the test area substantially. The results show a false positive decrease by more than a factor of eight on a dataset that has a strong focus on SMPS-driven appliances. To obtain a stable event detection system, we applied several experiments on different parameters to measure its performance. These experiments include the evaluation of six event features from the spectral and time domain, different types of feature space normalization to eliminate undesired feature weighting, the conventional and adaptive training, and two common classifiers with its optimal parameter settings. The evaluations are performed on two publicly available energy datasets with high sampling rates: BLUED and BLOND-50.

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