Abstract
The volume of data production is increased in smart power system by growing smart meters. Such data is applied for control, operation and protection objectives of power networks. Power companies can attain high indexes of efficiency, reliability and sustainability of the smart grid by appropriate management of such data. Therefore, the smart grids can be assumed as a big data challenge, which needs advanced information techniques to meet massive amounts of data and their analytics. This chapter investigates the utilization of huge data sets in power system operation, control, and protection, which are difficult to process with traditional database tools and often are known as big data. In addition, this paper covers two aspects of applying smart grid data sets, which include feature extraction, and system integration for power system applications. The application of big data methodology, which is analyzed in this study, can be classified to corrective, predictive, distributed, and adaptive approaches.
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Zhou, K., Chao, F., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215–225.
Momoh, J. A. (2009). Smart grid design for efficient and flexible power networks operation and control. In 2009 Power Systems Conference and Exposition,. PSCE’09. IEEE/PES. IEEE.
Amin, M. (2008). Challenges in reliability, security, efficiency, and resilience of energy infrastructure: Toward smart self-healing electric power grid. In 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century. IEEE.
Hossain, M. S., et al. (2016). Role of smart grid in renewable energy: An overview. Renewable and Sustainable Energy Reviews, 60, 1168–1184.
Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014). Data mining with big data,” IEEE Transactions on Knowledge and Data Engineering, 26, 97–107.
Zhou, K., & Yang, S. (2015). A framework of service-oriented operation model of China׳ s power system. Renewable and Sustainable Energy Reviews, 50, 719–725.
Efthymiou, C., & Kalogridis, G. (2010). Smart grid privacy via anonymization of smart metering data. In 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE.
McKenna, E., Richardson, I., & Thomson, M. (2012). Smart meter data: Balancing consumer privacy concerns with legitimate applications. Energy Policy, 41, 807–814.
Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652–687.
Kezunovic, M., Xie, L., & Grijalva, S. (2013). The role of big data in improving power system operation and protection. In 2013 IREP Symposium on Bulk Power System Dynamics and Control-IX Optimization, Security and Control of the Emerging Power Grid (IREP) (pp. 1–9).
Dalal, G., Gilboa, E., & Mannor, S. (2016). Distributed scenario-based optimization for asset management in a hierarchical decision making environment. Power Systems Computation Conference (PSCC), 2016, 1–9.
Yang, S.-l., & Shen, C. (2013). A review of electric load classification in smart grid environment. Renewable and Sustainable Energy Reviews, 24, 103–110.
IEEE. (2006). IEEE standard for calculating the current temperature of bare overhead conductors.
Wallnerstrom, C. J., Huang, Y., & Soder, L. (2015). Impact from dynamic line rating on winpower integration. IEEE Transactions on Smart Grid, 6(1), 343–350.
Chen, D. (2017). Research on traffic flow prediction in the big data environment based on the improved RBF neural network. IEEE Transactions on Industrial Informatics.
Shetty, R.P., Sathyabhama, A., & Adarsh Rai, A. (2016). Optimized radial basis function neural network model for wind power prediction. In 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP). IEEE.
Morales, J.M., et al. (2013). Integrating renewables in electricity markets: Operational problems (Vol. 205). Springer Science & Business Media.
Sulaiman, M., Adnan, T., & Ibrahim, Z. (2013). Using probabilistic neural network for classification high impedance faults on power distribution feeders. World Applied Sciences Journal, 23(10), 1274–1283.
Mor, V., & Vaghamshi, A. (2016). Review on fault detection, identification and localization in electrical networks using fuzzy-logic.
Kagan, N., et al. (2016). Computerized system for detection of high impedance faults in MV overhead distribution lines. In 2016 17th International Conference on. Harmonics and Quality of Power (ICHQP). IEEE.
Dag, O., & Yozgatligil, C. (2016). GMDH: An R package for short term forecasting via GMDH-type neural network algorithms. The R Journal, 8(1), 379–386.
Xiao, J., et al. (2016). Churn prediction in customer relationship management via GMDH-based multiple classifiers ensemble. IEEE Intelligent Systems, 31(2), 37–44.
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Madadi, S., Nazari-Heris, M., Mohammadi-Ivatloo, B., Tohidi, S. (2018). Application of Big Data Analysis to Operation of Smart Power Systems. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_17
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DOI: https://doi.org/10.1007/978-981-10-8476-8_17
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