21 research outputs found
Practical applications of multi-agent systems in electric power systems
No full textThe transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur
Designing for reliability in wind turbine condition monitoring systems
No full textFor wind turbine condition-based maintenance to be economically advantageous over the more conventional periodic based maintenance, high reliability is required of the systems which carry out the monitoring. The use of condition monitoring systems (CMS) is becoming common place in offshore wind turbines, due to the high cost associated with maintenance; however for onshore wind turbines the benefits aren’t quite so obvious. Still, for both onshore and offshore wind turbine condition-based maintenance, it is essential that these systems can operate for long periods of time providing an accurate diagnosis of the state of the wind turbine’s health. Through the build and installation of one CMS and the design, build and installation of another the author aims to share experience and lessons learnt to aid the design of any new CMS. A brief description will be given of the first system installed followed by the issues that were experienced following its installation. The author will then go on to discuss the design of a new CMS which has been installed in a Vestas V42 wind turbine and the improvements that were made to it to increase its reliability and ruggedness
Identifying prognostic indicators for electrical treeing in solid insulation through pulse sequence analysis
No full textPredictive maintenance attempts to evaluate the condition of equipment and predict the future trend of the equipment's aging, in order to reduce costs when compared to the two traditional approaches: corrective and preventive maintenance. This prediction requires an accurate prognostic model of aging. In solid insulation, the ultimate goal of prognostics is to predict the advent of failure, i.e., insulation breakdown, in terms of remaining useful life (RUL). One fault is electrical treeing, which is progressive thus leading to potentially catastrophic failure. Research has shown that diagnosis of faults can be achieved based on partial discharge (PD) monitoring [1], i.e., phase-resolved and pulse sequence analysis (PSA). This work will explore the extension of this concept towards predicting evolution of the defect: moving beyond diagnostics towards prognostics. To do this, there is a need for further investigation of prognostic features within PD characteristics leading up to breakdown. In this work, a needle-plane test arrangement was set up using a hypodermic needle and pre-formed silicone rubber as test samples. The visual observations and tree growth measurements were made using a digital microscope. PD data was captured using a radio frequency (RF) sensor and analysed using PSA. The main idea of the PSA approach is the strong relationship between two consecutive pulses caused by PD activities, which can give an understanding of the local degradation processes [1]. As for electrical treeing, a breakdown indicator in PSA is the appearance of heavily clustered data points that lie diagonally in scatter plots of the differential ratio of voltage and time of consecutive charges (Un = Δun/Δtn) [2,3]. Figure 1 shows an example of a plot that changed to a diagonal line after 14 hours of aging time. This paper investigates the formation of the diagonal line based on the distribution of the plot from the start of electrical treeing until breakdown occurs. Finally, statistical features of the PSA plot are given and will be used for lifetime prediction of insulation samples in future work
Identifying Prognostic Indicators for Electrical Treeing in Solid Insulation through PD Analysis
No full textThis paper presents early results from an experimental study of electrical treeing on commercially available pre-formed silicone samples. A needle-plane test arrangement was set up using hypodermic needles. Partial discharge (PD) data was captured using both the IEC 60270 electrical method and radio frequency (RF) sensors, and visual observations are made using a digital microscope. Features of the PD plot that corresponded to electrical tree growth were assessed, evaluating the similarities and differences of both PD measurement techniques. Three univariate phase distributions were extracted from the partial discharge phase-resolved (PRPD) plot and the first four statistical moments were determined. The implications for automated lifetime prediction of insulation samples due to electrical tree development are discussed
Generating PD data from electrical treeing in silicone rubber for insulation lifetime modelling
No full textElectrical treeing is a degradation phenomenon in solid dielectric material resulting from high and non-uniform electrical field or partial discharges (PD). The presence of electrical treeing, therefore, can be examined through PD monitoring by looking for characteristic features within the phase-resolved plot of PD data. As electrical trees evolve in time, time-resolved analysis of PD data may be more descriptive of the correspondence between discharges and tree propagation. Continuing partial discharges in electrical treeing may lead to catastrophic failure, but there is still a lack of understanding of the evolution of PD characteristics prior to breakdown. This paper focuses on a method of simplifying the production and growth of electrical trees in silicone rubber (SiR), an advanced insulating material that is widely used in high voltage cable accessories due to its excellent insulation and mechanical performance. Crucially, commercially available pre-formed samples of SiR are used to ensure consistency and eliminate the need for the mixing, degassing and heating process in sample preparation. The experimental methodology is described, in terms of sample preparation, applied voltage regime, and data capture. A constant 50 Hz AC voltage is applied to the samples (with a needle-plane test arrangement using hypodermic needles) at a level sufficient to induce PD, leading to breakdown within hours. Both IEC 60270 electrical method and radio frequency (RF) sensors are used to capture PD data, while a digital microscope is used for visual observation. The paper describes the features found within the PD phase-resolved plot, and evaluates the similarities and differences between the two measurement techniques. Future work aims to automatically detect those features corresponding to electrical tree growth, and give a lifetime prediction for the insulation samples being studied
University of Nebraska College of Medicine Class of 1988
No full textIrene Agostini, James Scott Albin, Renee Marie David Albin, Martin Long Arnold, David Mauldin Arteaga, Kevin Scott Baker, Scott Erick Balogh, Sanjay Bansal, Sean Gregory Barry, Brenda Jean Barth, Bonnie J. Bateman, Mark W Becher, Curtis George Benesch, Steve Clair Boyer, Kyle Coreen Bryans, Mary Frances Caffrey, Mark Lee Catterson, Rebecca Christensen, William Gerard Cimino, Stacey Barton Dagle, Kimberly Kay Davis, Neal Thomas DeCosta, James David Demmel, Jeanne Marian Franco Doyle, John Vincent Durso, Mary Beth Duryea, Peter Timothy Egan, John C. Ely, Jeff Lynn Fidler, Patricia Mary Rita Fitzgibbons, Douglas Alan Foulk, Todd Edward Fristo, Mark Allan Gauthier, Michael Richard Goodman, Donald Dean Graham, Jr., Paul Robert Gregory, Jr., Kenneth Paul Gross, Thomas Gene Gross, Dwight Earl Gurley, Michael Lee Haag, Phillip John Haberman, Steven Vincent Hagan, Michael Thomas Hagley, Julie Jill Hamann, Sharon Jean Hammer, Calvin Jay Hansen, Jeffrey Dale Harrison, Mary Christine Hauser, Michael Craig Havekost, Frank Edward Hwkins, Kathryn Ann Hedges, Susan Corning Long Hollins, Christine Marie Holm, Barry Allen Hoover, Kirk Scott Hutton, Robert Andrew Iaffaldano, Cheryl Bellanca Jack, Paul Leon Jacobsen, James Jagers, Sarah Mae Jantzen, Ravijot Singh Johar, Kristin Kay Myers Johnson, Michael Oliver Johnson, David Vernon Kassen, Brenda Kay Keller, Seung J. Kim, Kirk Allen Kinberg, Michael John Kozal, Kathryn Janice Leeper, Nicholas Yuvienco Lorenzo, Peter Donadl Lueninghoener, William Michael Lydiatt, George Benjamin Lynch, Nancy Sue Barrett Mathews, Patrick James McCarville, Matthew Thomas McLeay, Susan Elizabeth McNeil, Marilyn F. Aschoff Mellor, Stephanie Jeanne Meyers, David J. Miller, Jon James Morton, Mark Alan Mozer, Kathleen J. Muffly, Clifford Martin Myles, Douglas Donn Netz, Chi Dang Nguyen, Charles Edward Olson, Julie Ann Overcash, Jan Ellen Paisley, Frederick Mark Paz, Scott Huntly Plantz, Scott Charles Rasmussen, Rebeca Jo Stingley Rezaei, Roy Wallace Robertson, Joseph Gordon Rogers, Lynn Krista Rosenlof, Patricia Joan Kemmy Ryan, Timothy Wayne Ryschon, John Paul Safranek, Denise Marjorie Sammons, Lori Ann Sapp, Kane Loux Schaphorst, Nancy Ann Scheinost, Michael B. Schneider, Steven Eugene Seals, Jeffry Michael Seizys, James Edward Smith, Marshelle Denise Smith, James Harrison Sorrell, William Thomas Sorrell, Theodore Harry Stathos, Christine Louise Stevens, Dale Frank Sutherland, Noel Allen Timmons, Jon Paul Trevisani, James Frederick Tritz, Leslie William Veskrna, Michael Gerald Wadzinski, David John Watts, Kelly Jean Wehrman, Stephen Vern Wendt, Martin Wayne Wetzel, Daniel S. Williamson, Mark E. Wilson, Scott Denman Wilt, Wayne Terence Wolfrey, Geoffrey Zeldes, George Allen Zieg, Kenneth Allen Zoucha, Kevin Robert Zuerleinhttps://digitalcommons.unmc.edu/comclass/1069/thumbnail.jp
Prognostic modeling for electrical treeing in solid insulation using pulse sequence analysis
No full textThis paper presents a prognostic framework for estimating the time-to-failure (TTF) of insulation samples under electrical treeing stress. The degradation data is taken from electrical treeing experiments on 25 epoxy resin samples. Breakdown occurs in all tests within 2.5 hours. Partial discharge (PD) data from 18 samples are used as training data for prognostic modeling and 7 for model validation. The degradation parameter used in this model is the voltage difference between consecutive PD pulses, which decreases prior to breakdown. Every training sample shows a decreasing exponential trend when plotting the root mean squared (RMS) of the voltage difference for 5 minute batches of data. An average model from the training data is developed to determine the RMS voltage difference during breakdown. This breakdown indicator is verified over three time horizons of 25, 50 and 75 minutes. Results show the best estimation of TTF for 50 minutes of data, with error within quantified bounds. This suggests the framework is a promising approach to estimating insulation TTF
Managing remote online partial discharge data
No full textThe volume of data produced by existing partial discharge monitoring systems is often too large for engineers to examine in detail, leading to data being ignored and useful indicators of health being missed. The case study reported in this paper recorded 21 839 events around an HVDC reactor over a six-day period. We estimate that it takes 1 min to check whether an event requires detailed study, leading to over two man-months of effort to locate important events in a dataset of this size. Additionally, online monitoring data are stored onsite, and may require an engineer's visit for collection. This paper presents an approach to remote partial discharge monitoring, supported by automated data interpretation and prioritization, which enables engineers to remotely find and download important data. Results from the case study are used to illustrate these concept
The Author(s) Pharmacovigilance and Risk Management Strategies 2017: Overview of the Generic Drug Program and Surveillance
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