Abstract
Risk analysis is always the pivotal part of emergency preparedness for critical infrastructure protection such as power grid and traffic network. The main contribution of this paper is to employ case-based reasoning (CBR) method (combines case representation and retrieval) to illustrate a risk assessment framework for protecting power grid. It focuses on two core key parts: (1) Using ontology model to express precursors of risk, the described semantic network contains sub-concepts to outline selected precursors from hazards, environment, responders and physical system and (2) by analysis of emergency scenario precursors with sub-concept similarity and fuzzy value similarity calculation, the potential risks could be recognized to assist to retrieve the past knowledge, and effective and feasible actions would be taken to decrease the threats or cut disaster loss for power grid. Via a case study, the result shows that the proposed method extends the applicability of conventional CBR technique to numbers of real-world settings.
This work is supported by the National Science Foundation of China (NSFC) under the Grant Nos. 91024028, 91024031, 91324018, 71172156 and 71473058.
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References
Goh, Y.M., Chua, D.K.H.: Case-based Reasoning Approach to Construction Hazard Identification: Case Representation and Retrieval. Journal of Construction Engineering and Management 136(2), 170–178 (2009)
Patkus, B., Schnare, R.E.: Disaster Preparedness Workbook for Cultural Institutions Within the Military. Department of Defense Legacy Resource Management Program, U.S. Naval War College Library (2009)
Schank, R., Abelson, R.: Scripts, Plans, Goals and Understanding. Erlbaum, Hillsdale (1977)
Aamodt, A., Plaza, E.: Case-based Reasoning: Foundational Issue. Methodological Variation, and System Approaches, AI Com-Artificial Intelligence Communication 7(1), 39–59 (1994)
Gavin, F., Sun, Z.H.: R5 Model for Case-Based Reasoning. Knowledge-Based Systems 16, 59–65 (2003)
Kleindorfer, P., Saad, G.: Managing Disruption Risks in Supply Chains. Production and Operations Management 14(1), 53–68 (2005)
Mendes, J.R.P., Morooka, C.K., Guilherme, I.R.: Case-based Reasoning in Offshore Well Design. Journal of Petroleum Science and Engineering 40(1–2), 47–60 (2003)
Zhao, J.S., Cui, L., Zhao, L.H., Qiu, T., Chen, B.Z.: Learning HAZOP Expert System by Case-based reasoning and Ontology. Computer Chemistry Engineering 33(1), 371–378 (2009)
Goh, Y.M., Chua, D.K.H.: Case-based Reasoning for Construction Hazard Identification: Case Representation and Retrieval. Journal of Construction Engineering and Management 135(11), 1181–1189 (2009)
Balducelli, C., D’Esposito, C.: Genetic Agents in an EDSS System to Optimize Resources Management and Risk Object Evacuation. Safety Science 35(1–3), 59–73 (2000)
Remm, M., Remm, K.: Case-based Estimation of the Risk of Enterobiasis. Artificial Intelligence Medical 43(3), 167–177 (2008)
Lu, Y., Li, Q.M., Xiao, W.J.: Case-based Reasoning for Automated Safety Risk Analysis on Subway Operation: Case Representation and Retrieval. Safety Science 57, 75–81 (2013)
Vong, C.M., Wong, P.K., Ip, W.F.: Case-based Expert System Using Wavelet Packet Transform and Kernel-based Feature Manipulation for Engine Ignition System Diagnosis. Engineering Application Artificial Intelligence 24(7), 1281–1294 (2011)
Gruber, T.R.: A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5(2), 199–220 (1993)
Gilboai, I., Schmeidler, D.: Case-based Decision Theory. Quarterly Journal of Economics 110, 605–639 (1995)
Phimister, J.R., Bier, V.M., Kunreuther, H.C.: Accident Precursor Analysis and Management: Reducing Technological Risk through Diligence. National Academy Press, Washington, D.C. (2004)
Feng, Y., Xiangyang, L., Yingxiong, L.: Discovery method on failure factors of collaborative allocation under power grid emergency. In: International Conference on Collaboration Technologies and Systems, U.S. San Diego, pp. 281–286 (2013)
Feng, Yu., Li, X., Yue, G.: Fault-tree Analysis for Power Gird Emergency Logistics System under Large-scale Natural Disaster. Applied Mechanics and Materials 986–987, 311–314 (2014)
Tadrat, J., Boonjing, V., Pattaraintakorn, P.: A New Similarity Measure in Formal Concept Analysis for Case-based Reasoning. Expert Systems with Applications 39(1), 967–972 (2012)
Fan, Z., Li, Y., Wang, X., Liu, Y.: Hybrid Similarity Measure for Case Retrieval in CBR and Its Application to Emergency Response towards Gas Explosion. Expert Systems with Applications 41(5), 2526–2534 (2014)
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Yu, F., Li, X., Wang, S. (2015). CBR Method for Risk Assessment on Power Grid Protection Under Natural Disasters: Case Representation and Retrieval. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9155. Springer, Cham. https://doi.org/10.1007/978-3-319-21404-7_9
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DOI: https://doi.org/10.1007/978-3-319-21404-7_9
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