Using IS to Assess an Electric Power System's Real-Time Stability

2013 ◽  
Vol 28 (4) ◽  
pp. 60-66 ◽  
Author(s):  
Zhao Yang Dong ◽  
Yan Xu ◽  
Pei Zhang ◽  
Kit Po Wong
Keyword(s):  
Electric Power ◽  
Real Time ◽  
Time Stability ◽  
Real Time Stability

Real time stability and viability prediction of the anticancer BCG after lyophilization

2015 ◽  
Vol 41 (11) ◽  
pp. 1769-1776 ◽  
Author(s):  
Dalia Mohamed ◽  
Ibrahim Elsayed ◽  
Aly Fahmy Mohamed ◽  
Soad Ali Yehia
Keyword(s):  
Real Time ◽  
Time Stability ◽  
Real Time Stability

A data-driven real-time stability metric for SST-based microgrids

2022 ◽  
Vol 134 ◽  
pp. 107397
Author(s):  
Farzad Ferdowsi ◽  
Hesan Vahedi ◽  
Ali Jafarian Abianeh ◽  
Chris S. Edrington ◽  
Touria Elmezyani
Keyword(s):  
Real Time ◽  
Data Driven ◽  
Time Stability ◽  
Real Time Stability

Real Time Stability Monitoring

2014 ◽  
pp. 59-98
Author(s):  
Mladen Kezunovic ◽  
Sakis Meliopoulos ◽  
Vaithianathan Venkatasubramanian ◽  
Vijay Vittal
Keyword(s):  
Real Time ◽  
Time Stability ◽  
Stability Monitoring ◽  
Real Time Stability

scholarly journals Real–time stability testing of air–dried primers and fluorogenic hydrolysis probes stabilized by trehalose and xanthan

BioTechniques ◽  
2014 ◽  
Vol 57 (3) ◽  
pp. 151-155 ◽  
Author(s):  
Markus Rombach ◽  
Dominique Kosse ◽  
Bernd Faltin ◽  
Simon Wadle ◽  
Günter Roth ◽  
...  
Keyword(s):  
Real Time ◽  
Stability Testing ◽  
Time Stability ◽  
Real Time Stability

A Real-Time Stability Indicator for Micromilling: An Experimental Validation

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Shashwat Kushwaha ◽  
Benjamin Gorissen ◽  
Jun Qian ◽  
Dominiek Reynaerts
Keyword(s):  
Standard Deviation ◽  
Real Time ◽  
Experimental Validation ◽  
Prediction Method ◽  
Direct Consequence ◽  
Milling Process ◽  
Stability Prediction ◽  
Time Stability ◽  
Strong Candidate ◽  
Real Time Stability

In contrast to the well-established stability prediction tools, a robust real-time stability indicator is proposed for micromilling process, and it opens the possibility of online chatter avoidance based on successful detection. In this paper, a robust and easy-to-compute stability indicator is presented. This approach exploits the virtue of a stable milling process—the displacement of the vibrating tool repeats with a period of tooth passing. It has been observed that the standard deviation of the tool displacement sampled at once per tooth passing frequency is indicative of chatter, where a low standard deviation coincides with stable cutting. An increase in standard deviation is the direct consequence of an increase in asynchronous motion of the tool, coinciding with chatter. As it is also well known, this asynchronous vibration of the tool results in distinct marks on the workpiece surface. This paper presents the experimental validation of this real-time stability indicator. The ease of implementation makes the presented stability indicator a strong candidate for applications in chatter avoidance based on detection. The results are also verified against the standard stability prediction method.

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