Abstract
The total replacement value of the US transmission lines alone (excluding land) is conservatively estimated at over $100 billion dollars [1] and triples when including transformers and circuit breakers. Investment in new transmission equipment has significantly declined over the past 15 years. Some of the equipment is well beyond intended life, yet is operated under increasing stress, as load growth, new generation, and economically motivated transmission flows push equipment beyond nameplate limits. Maintaining acceptable electric transmission system reliability and delivering electric energy at low energy prices requires innovations in sensing, diagnostics, communications, data management, processing, algorithms, risk assessment, decision-making (for operations, maintenance, and planning), and process coordination. This paper overviews a comprehensive approach to develop methods and processes in these areas, driven by the ultimate objective to develop a hardware-software prototype capable of auto-steering the information-decision cycles inherent to managing operations, maintenance, and planning of the high-voltage electric power transmission systems.
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References
Gallaher, M., Johnston, S., Kirby, B.: Changing measurement and standards needs in a deregulated electric utility industry, Research Triangle Institute (November 1999), http://www.rti.org/pubs/dereg.pdf
Lu, C.J., Meeker, W.Q.: Using Degradation Measures to Estimate a Time-to-Failure Distribution. Technometrics 35, 161–174 (1993)
Meeker, W.Q., LuValle, M.J.: An Accelerated Life Test Model Based on Reliability Kinetics. Technometrics 37, 133–146 (1995)
Meeker, W.Q., Escobar, L.A., Lu, C.J.: Accelerated Degradation Tests: Modeling and Analysis. Technometrics 40, 89–99 (1998)
Grall, A., Dieulle, L., Berenguer, C., Roussignol, M.: Continuous-Time Predictive-Maintenance Scheduling for a Deteriorating System. IEEE Transactions on Reliability 51, 141–150 (2002)
Meeker, W.Q., Escobar, L.A., Chan, V.: Using Accelerated Tests to Predict Service Life in Highly-Variable Environments. In: Martin, J.W., Bauer, D.R. (eds.) Service Life Prediction Methodology and Metrologies, ch. 19. American Chemical Society, Washington (2002)
Garrigoux, C.G., Meeker, W.Q.: Assessing the Effect of In-Service Inspections on the Reliability of Degrading Components. In: Balakrishnan, N. (ed.) Recent Advances in Life-Testing and Reliability. CRC Press, Boca Raton (1995)
Anders, G.: Probability Concepts in Electric Power Systems. John Wiley, New York (1990)
da Silva, A.L., Endrenyi, J.: Application of First Passage Times in the Markov Representation of Electric Power Systems. In: Proceedings of the 4th International Conference on Probabilistic Methods Applied to Power Systems, Rio de Janeiro, Brazil (September 1994)
Gershwin, S.B.: Hierarchical Flow Control: A Framework for Scheduling and Planning Discrete Events in Manufacturing Systems. Proceedings of the IEEE 77(1), 195–209 (1989)
Raiffa, H.: Decision analysis: Introductory lectures on choices under uncertainty. Addison-Wesley, Reading (1968)
Mnarschak, J.: Economic information, decision, and prediction, vol. II. D. Reidel Publishing Co. (1974)
Baker, A.: Business decision-making. Croom-Helm (1981)
Hirshleifer, J.: Time, Uncertainty, and Information. Basil Blackwell, Malden (1989)
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McCalley, J.D. et al. (2006). Auto-steered Information-Decision Processes for Electric System Asset Management. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758532_59
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DOI: https://doi.org/10.1007/11758532_59
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