The smart distribution system is the critical part of
the smart grid, which also plays an important role in the safe and reliable
operation of the power grid. The self-healing function of smart distribution
network will effectively improve the security, reliability and efficiency,
reduce the system losses, and promote the development of sustainable energy of
the power grid. The risk identification process is the most fundamental and
crucial part of risk analysis in the smart distribution network. The risk
control strategies will carry out on fully recognizing and understanding of the
risk events and the causes. On condition that the risk incidents and their
reason are identified, the corresponding qualitative / quantitative risk
assessment will be performed based on the influences and ultimately to develop
effective control measures. This paper presents the concept and methodology on
the risk identification by means of Hidden Semi-Markov Model (HSMM) based on
the research of the relationship between the operating characteristics/indexes
and the risk state, which provides the theoretical and practical support for
the risk assessment and risk control technology.
Cite this paper
F. Chang, W. Sheng, T. Zhang, Y. Zhang and X. Song, "Risk Identification based on Hidden Semi-Markov Model in Smart Distribution Network," Energy and Power Engineering
, Vol. 5 No. 4, 2013, pp. 954-957. doi: 10.4236/epe.2013.54B183
 R. Billinton and P. Wang, “Reliability Network Equivalent Approach to Distribution System Reliability Evaluation,” IEEE Proceedings: Generation, Transmission and Distribution, Vol. 145, No. 2, 1998, pp. 149-153.
 R. X. Liu, J. H. Zhang and D. Wu, “Research on Static Security Index of Distribution Network Based on Risk Theory,” Power system Protection and Control, Vol.39, No.15, 2011, pp. 89-95.
 H. Wan, J. D. Mccalley and V. Vittal, “Increasing Thermal Rating by Risk Analysis,” IEEE Trans. on Power Systems, Vol. 14, No. 3, 1999, pp. 815-828.
 W. H. Fu, J. D. Mccalley and V. Vittal, “Risk Assessment for Transformer Loading,” IEEE Transactions on Power Systems, Vol. 16, No. 3, 2001, pp. 346-353.
 M. Ni, J. D. Mccalley and V. Vittal, “Software Implementation of Online Risk-Based Security Assessment,” IEEE Trans on Power Systems, Vol. 18, No. 3, 2003, pp. 1165-11728. doi:10.1109/TPWRS.2003.814909
 H. Wan, J. D. Mccalley and V. Vittal, “Risk based Voltage Security Assessment,” IEEE Transactions on Power Systems, Vol. 15, No. 4, 2000, pp. 1247-1254.
 J. D. Mccalley, A. A. Fouad and V. Vittal, “A Risk-Based Security Index for Determining Operating Limits in Stability-Limited Electric Power Systems,” IEEE Transactions on Power Systems, Vol. 12, No. 3, 1997, pp. 1210-1219.doi:10.1109/59.630463
 Wikipedia, “Markov Process,” 2013.
 S. Z. Yu, “Hidden Semi-Markov Models,” Artificial Intelligence, Vol. 174, No. 2, 2009, pp. 215-243.
 W. Y. Li, “Risk Assessment of Power Systems: Models, Methods, and Applications,” 1st Edition, John Wiley & Sons Ltd., Chichester, 2004. doi:10.1002/0471707724
 R. Jin, Z. N. Xiao and J. Gong, “Markov Model for Reliability Assessment of Power Transformers,” High Voltage Engineering, Vol. 36, No. 2, 2010, pp. 322-328.
 M. Perman, A. Senegacnik and M. Tuma, “Semi-Markov models with an application to power-plant reliability analysis,” IEEE Transactions on Power Reliability, Vol. 46, No. 4, 1997, pp. 526-532. doi:10.1109/24.693787
 Y. Liang, R. M. Fricks and K. S. Trivedi, “Application of semi-Markov process and CTMC to evaluation of UPS system availability,” Proceedings of 2002 Annual Reliability and Maintainability Symposium, Seattle, 28-31 January 2002, pp. 584-591.
 L. J. Wang, G. Wang and B. Li, “Reliability Modeling of a Protection System Based on the Semi-Markov Process,” Automation of Electric Power Systems, Vol. 34, No. 18, 2010, pp. 6-10.
 X. Jun and X. Y. Xiao, “A Method of Reliability Cost Assessment in Distribution System Based on Semi-Markov Process,” Relay, Vol. 34, No. 12, 2006, pp. 57-62.