Robust Bad Data Detection Method for Microgrid Using Improved ELM and DBSCAN Algorithm
Publication: Journal of Energy Engineering
Volume 144, Issue 3
Abstract
Bad data must be detected in the microgrid because they mislead the decision making of energy management systems (EMSs). The authors propose a robust detection approach that combines an improved robust extreme learning machine (R-ELM) and density-based spatial clustering algorithm with noise (DBSCAN). To resist the impact of outliers in training data, R-ELM applies robust estimation and orthogonal transformation to the ELM training process. After training, R-ELM is used to construct an error-filtering map to extract the characteristics of microgrid measurements. These characteristics are analyzed by DBSCAN to identify bad data. The detection performance of this proposed approach is verified by historical data from a four-terminal ring-shaped DC microgrid prototype. Compared with the back-propagation neural network and ELM, R-ELM is validated to have good robustness. DBSCAN is also verified to outperform traditional K-means clustering. Overall, the approach described here maintains its robustness against outliers and achieves fast and effective detection of bad data in the microgrid.
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References
Abur, A., and Exposito, A. G. (2004). Power system state estimation: theory and implementation, Marcel Dekker, New York.
Anwar, A., and Mahmood, A. (2016). “Stealthy and blind false injection attacks on SCADA EMS in the presence of gross errors.” IEEE PES General Meeting, IEEE Power & Energy Society, Piscataway, NJ, 1–6.
Anwar, A., Mahmood, A. N., and Pickering, M. (2016). “Data-driven stealthy injection attacks on smart grid with incomplete measurements.” Intelligence and security informatics, Springer International Publishing, Cham, Switzerland, 180–192.
Anwar, A., Mahmood, A. N., and Pickering, M. (2017). “Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements.” J. Comput. Syst. Sci., 83(1), 58–72.
Baldick, R., Clements, K. A., Pinjo-Dzigal, Z., and Davis, P. W. (1997). “Implementing nonquadratic objective functions for state estimation and bad data rejection.” IEEE Trans. Power Syst., 12(1), 376–382.
Barreto, G. A., and Barros, A. L. B. P. (2016). “A robust extreme learning machine for pattern classification with outliers.” Neurocomputing, 176, 3–13.
Beg, O., Johnson, T., and Davoudi, A. (2017). “Detection of false-data injection attacks in cyber-physical DC microgrids.” IEEE Trans. Ind. Inf., 13(5), 2693–2703.
Bretas, N. G., and Bretas, A. S. (2015). “A two steps procedure in state estimation gross error detection, identification, and correction.” Int. J. Electr. Power Energy Syst., 73, 484–490.
Clewer, B. C. (1986). “State estimation and bad data detection in electrical power systems.” Ph.D. dissertation, Durham Univ., Durham, U.K., 39–44.
Cramer, M., Goergens, P., and Schnettler, A. (2015). “Bad data detection and handling in distribution grid state estimation using artificial neural networks.” PowerTech, 2015 IEEE Eindhoven, IEEE Power & Energy Society, Piscataway, NJ, 1–6.
Ester, M., Kriegel, H. P., Sander, J., and Xu, X. (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise.” Proc., 2nd Int. Conf. on Knowledge Discovery and Data Mining, AAAI, Menlo Park, CA, 226–231.
Frenay, B., and Verleysen, M. (2016). “Reinforced extreme learning machines for fast robust regression in the presence of outliers.” IEEE Trans. Cybern., 46(12), 3351–3363.
Gan, G., Ma, C., and Wu, J. (2007). Data clustering: Theory, algorithms, and applications, American Statistical Association and SIAM, Alexandria, VA.
Goodall, C. R. (1993). “13 computation using the QR decomposition.” Handbook of statistics, Vol. 9, 467–508.
Gu, W., Lou, G., Tan, W., and Yuan, X. (2017). “A nonlinear state estimator-based decentralized secondary voltage control scheme for autonomous microgrids.” IEEE Trans. Power Syst., 32(6), 4794–4804.
Hao, J., Piechocki, R. J., Kaleshi, D., Chin, W. H., and Fan, Z. (2015). “Sparse malicious false data injection attacks and defense mechanisms in smart grids.” IEEE Trans. Ind. Inf., 11(5), 1–12.
Heijden, F. V. D. (2005). Classification, parameter estimation and state estimation: An engineering approach using MATLAB, Wiley, Hoboken, NJ.
Horata, P., Chiewchanwattana, S., and Sunat, K. (2013). “Robust extreme learning machine.” Neurocomputing, 102(2), 31–44.
Huang, G. B., Zhou, H., Ding, X., and Zhang, R. (2012). “Extreme learning machine for regression and multiclass classification.” IEEE Trans. Syst. Man Cybern., Part B, 42(2), 513–529.
Huang, G. B., Zhu, Q. Y., and Siew, C. K. (2006). “Extreme learning machine: Theory and applications.” Neurocomputing, 70(1–3), 489–501.
Huang, S. J., and Lin, J. M. (2002). “Enhancement of power system data debugging using GSA-based data-mining technique.” IEEE Trans. Power Syst., 17(4), 1022–1029.
Huang, Y., Esmalifalak, M., Nguyen, H., and Zheng, R. (2013). “Bad data injection in smart grid: Attack and defense mechanisms.” IEEE Commun. Mag., 51(1), 27–33.
Huang, Y., Tang, J., Cheng, Y., Li, H., Campbell, K. A., and Han, Z. (2016). “Real-time detection of false data injection in smart grid networks: An adaptive CUSUM method and analysis.” IEEE Syst. J., 10(2), 532–543.
Li, S., Yılmaz, Y., and Wang, X. (2015). “Quickest detection of false data injection attack in wide-area smart grids.” IEEE Trans. Smart Grid, 6(6), 2725–2735.
Lo, K. L., Zeng, P. L., Marchand, E., and Pinkerton, A. (1992). “New bad-data detection and identification technique based on rotation of measurement order for sequential state estimation.” IEE Proc. C (Generation, Transmission and Distribution), 139(5), 387–401.
MATLAB [Computer software]. MathWorks, Natick, MA.
Mohammadpourfard, M., Sami, A., and Seifi, A. (2017). “A statistical unsupervised method against false data injection attacks: A visualization-based approach.” Expert Syst. Appl. Int. J., 84, 242–261.
Ouyang, T., Zha, X., Qin, L., Xiong, Y., and Huang, H. (2017). “Model of selecting prediction window in ramps forecasting.” Renewable Energy, 108, 98–107.
Rana, M. M. (2017). “Least mean square fourth based microgrid state estimation algorithm using the internet of things technology.” PLoS One, 12(5), 1–13.
Rana, M. M., and Li, L. (2015). “An overview of distributed microgrid state estimation and control for smart grids.” Sensors, 15(2), 4302–4325.
Shahnia, F., Majumder, R., Ghosh, A., Ledwich, G., and Zare, F. (2010). “Operation and control of a hybrid microgrid containing unbalanced and nonlinear loads.” Electr. Power Syst. Res., 80(8), 954–965.
SIMULINK [Computer software]. MathWorks, Natick, MA.
Viswanath, P., and Suresh Babu, V. (2009). “Rough-DBSCAN: A fast hybrid density based clustering method for large data sets.” Pattern Recog. Lett., 30(16), 1477–1488.
Wu, Y., Onwuachumba, A., and Musavi, M. (2013). “Bad data detection and identification using neural network-based reduced model state estimator.” IEEE Green Technologies Conf., IEEE Computer Society, Washington, DC, 183–189.
Wu, Y., Xiao, Y., Hohn, F., Nordstrom, L., Wang, J., and Zhao, W. (2017). “Bad data detection using linear WLS and sampled values in digital substations.” IEEE Trans. Power Delivery, 33(1), 150–157.
Xia, N., Gooi, H. B., Chen, S., and Hu, W. (2016). “Decentralized state estimation for hybrid AC/DC microgrids.” IEEE Syst. J., PP(99), 1–10.
Xie, X. L., Bian, G. B., Hou, Z. G., Feng, Z. Q., and Hao, J. L. (2016). “Preliminary study on Wilcoxon-norm-based robust extreme learning machine.” Neurocomputing, 198(Jul), 20–26.
Yang, L., Li, Y., and Li, Z. (2017). “Improved-ELM method for detecting false data attack in smart grid.” Int. J. Electr. Power Energy Syst., 91, 183–191.
Zhao, J., Zhang, G., Scala, M. L., and Wang, Z. (2016a). “Enhanced robustness of state estimator to bad data processing through multi-innovation analysis.” IEEE Trans. Ind. Inf., 13(4), 1610–1619.
Zhao, S., Zhang, Y., Moquin, J., and Mantooth, A. (2016b). “The hierarchical energy management control for residential energy harvesting system.” Energy Conversion Congress and Exposition (ECCE), IEEE Power Electronics Society, Piscataway, NJ, 1–7.
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©2018 American Society of Civil Engineers.
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Received: Apr 4, 2017
Accepted: Nov 10, 2017
Published online: Mar 21, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 21, 2018
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