References
- Abur, A., and A. Gome–Exposito. 2004. Power system state estimation – Theory and implementation. New York: Marcel Dekker.
- Al-Othman, A., and M. Irving. 2005. Uncertainty modelling in power system state estimation. IET Generation, Transmission & Distribution 152:233–39. doi:10.1049/ip-gtd:20041167.
- Cao, C., J. Xu, Q. Peng, K. Wang, Y. Qiu, Y. Pu, X. Luo, and B. Shuai. 2007. Short-term traffic flow predication based on PSOSVM. Proceeding 1st Int. Conf. Transportation Eng. Amer. Soc. Civil Eng., Chengdu, China, vol. 246, pp. 28–28.
- Ceperic, E., V. Ceperic, and A. Baric. 2013. A strategy for short-term load forecasting by support vector regression machines. IEEE Transactions on Power Systems 28:4356–64. doi:10.1109/TPWRS.2013.2269803.
- Chang, C. C., and C. J. Lin. 2011. LIBSVM: A library for support vector machines. ACM Transaction on Intelligent Systems and Technology 1–27. doi:10.1145/1961189.1961199.
- Chen, J., and A. Abur. 2006. Placement of PMUs to enable bad data detection in state estimation. IEEE Transactions on Power Systems 21:1608–15. doi:10.1109/TPWRS.2006.881149.
- Cortes, C., and V. Vapnik. 1995. Support-vector networks. Machine Learning 20:273–97. doi:10.1007/BF00994018.
- Drucker, H., C. Burges, L. Kaufman, A. Smola, and V. Vapnik. 1996. Support vector regression machines. Advances in Neural Information Processing Systems 9:155–61.
- Haughton, D., and G. Heydt. 2013. A linear state estimation formulation for smart distribution systems. IEEE Transactions on Power Systems 28:1187–95. doi:10.1109/TPWRS.2012.2212921.
- Huang, C., C. Lee, K. Shih, and Y. Wang. 2010. Bad data analysis in power system measurement estimation using complex artificial neural network based on the extended complex kalman filter. European Transactions on Electrical Power 20:1082–100. doi:10.1002/etep.386.
- ISO – IEC – OIML – BIPM. 2008. Guide to the expression of uncertainty in measurement Accessed January 01 2018. https://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf.
- Leandros, A. M., and J. Jiang. 2014. Intrusion detection in SCADA systems using machine learning techniques. Science and Information Conference (SAI), London, UK, pp. 626–31.
- Monticelli, A. 1999. State estimation in electric power systems – A generalized approach. Massachusetts: Kluwer Academic Publishers.
- Schweppe, F., J. Wildes, and D. Rom. 1970. Power system static-state estimation, parts 1, 2 and 3. IEEE Transactions Power Apparatus and Systems 89:120–35. doi:10.1109/TPAS.1970.292678.
- Shiri, A., M. Afshar, and A. Rahimi-Kian. 2015. Electricity price forecasting using support vector machines by considering oil and natural gas price impacts. IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, Ontario, Canada, pp. 1–5.
- Smola, A., and B. Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14:199–222. doi:10.1023/B:STCO.0000035301.49549.88.
- Vaz, A., and L. Vicente. 2007. A particle swarm pattern search method for bound constrained global optimization. Journal of Global Optimization 39:197–219. doi:10.1007/s10898-007-9133-5.
- Zhang, J., G. Welch, G. Bishop, and Z. Huang. 2014. A two-stage kalman filter approach for robust and real-time power system state estimation. IEEE Transactions on Sustainable Energy 2:629–36. doi:10.1109/TSTE.2013.2280246.