Simulation Panel on Conductivity for Polarization and Depolarization Current (PDC) Measurement of Offline Monitoring Using LabVIEW

2013 ◽  
Vol 284-287 ◽  
pp. 1104-1108
Author(s):  
Zainir Rubiatul B. Addawiyah ◽  
Muhamad Nor B. Asiah ◽  
Ishak Iqbal Rafei ◽  
Mohd Jamail Nor Akmal

Ageing is the major problem to the life span of high voltage insulation. Thus, continuous assessment method had gained an interest recently due to the economic concern. The measurement and evaluation of the dielectric response is one of the methods to diagnose transformer insulation condition. Dielectric diagnosis through polarization and depolarization current (PDC) measurement used dielectric response in time domain to assess the insulator. This paper presents the PDC data analysis system developed using LabVIEW software. The developed user-friendly system will enable user to run the recorded PDC data to evaluate the performance of the insulator in term of its conductivity and response function. The analysis result on PDC data recorded from field run using this system also been presented in this paper. Based on the obtained result, the system developed has been successfully helping user to do analysis on PDC data by giving the PDC pattern, conductivity value and response function of the insulator tested.

2020 ◽  
pp. 49-52
Author(s):  
Trine Aabo Andersen

A new fast measuring method for process optimization of sucrose crystallization using image analysis based on high quality images and algorithms is introduced. With the mobile, non-invasive at-line system all steps of the sucrose crystallization can be measured to determine the crystal size distribution. The image analysis system is easy to operate and is as well an efficient laboratory solution with user-friendly and customized software. In comparison to sieve analysis, image analyses performed with the ParticleTech Solution have been proven to be reliable.


1994 ◽  
Vol 17 (1) ◽  
pp. 161-200 ◽  
Author(s):  
Juan Pascual-Leone ◽  
Raymond Baillargeon

A dialectical constructivist model of mental attention ("effort") and of working memory is briefly presented, and used to explicate subjects' processing in misleading test items. We illustrate with task analyses of the Figural Intersections Test (FIT). We semantically derive a set of 10 Theoretical Structural Predictions (TSP) that stipulate relations between mental attentional resources (mental-power: Mp) and the systematically varied mental demand of items (mental-demand: Md), as they jointly codetermine probable performance (conditional probabilities of passing and failing). These predictions are evaluated on first approximation using a known family of ordered Latent Class models, all probabilistic versions of Guttman's unidimensional scale. Parameters of these models were estimated using the Categorical Data Analysis System of Eliason (1990). Main results are: (1) Data fit Lazarsfeld's latent-distance model, providing initial support for our 10 predictions; (2) The M-power of children (latent Mp-classes) when assessed behaviourally may increase with age in a discrete manner, and have the potential to generate interval scales of measurement; (3) In the light of our results what statisticians often consider "error of measurement" appears (in part) to be signal, not noise: The organismic signal of misleading (Y-) processes that in their dialectical (trade-off) interaction with success-producing (X-) processes generate performance.


Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1948
Author(s):  
Chenmeng Zhang ◽  
Kailin Zhao ◽  
Shijun Xie ◽  
Can Hu ◽  
Yu Zhang ◽  
...  

Power capacitors suffer multiple impulse voltages during their lifetime. With the multiple impulse voltage aging, the internal insulation, oil-film dielectric may deteriorate and even fail in the early stage, which is called accumulative effect. Hence, the time-domain dielectric response of oil-film dielectric with multiple impulse voltage aging is studied in this paper. At first, the procedure of the preparation of the tested samples were introduced. Secondly, an aging platform, impulse voltage generator was built to test the accumulative effect of capacitor under multiple impulse voltage. Then, a device was used to test the time-domain dielectric response (polarization depolarization current, PDC) of oil-film dielectric in different aging states. And finally, according to the PDC data, extended Debye model and characteristic parameters were obtained by matrix pencil algorithm identification. The results indicated that with the increase of impulse voltage times, the time-domain dielectric response of oil-film dielectric changed accordingly. The polarization current curve moved up gradually, the insulation resistance decreased when subjected to the repeated impulses. In frequency domain, the frequency spectrum of tan δ changed along with the impulse accumulation aging, especially at low frequency. At last, combined with the aging mechanism of oil-film dielectric under multiple impulse voltage, the test results were discussed.


2021 ◽  

Abstract The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide. The enormous number of design and analysis options available for correctly implementing, analyzing and interpreting research can be overwhelming. Statistical Analysis System (SAS®) is the most widely used statistical software in the world and SAS® OnDemand for Academics is now freely available for academic insttutions. This is a user-friendly guide to statistics using SAS® OnDemand for Academics, ideal for facilitating the design and analysis of plant science experiments. It presents the most frequently used statistical methods in an easy-to-follow and non-intimidating fashion, and teaches the appropriate use of SAS® within the context of plant science research. This book contains 21 chapters that covers experimental designs and data analysis protocols; is presented as a how-to guide with many examples; includes freely downloadable data sets; and examines key topics such as ANOVA, mean separation, non-parametric analysis and linear regression.


2018 ◽  
Vol 89 (10) ◽  
pp. 10K114 ◽  
Author(s):  
M. C. Thompson ◽  
T. M. Schindler ◽  
R. Mendoza ◽  
H. Gota ◽  
S. Putvinski ◽  
...  

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