Abstract
Compliance verification and performance analysis of grid power quality are the main targets of state-wide power quality monitoring and analysis system in China. Real-time aggregation of power quality data is a prerequisite to achieve the targets. Since power quality data generated by over 10,000 monitors are extremely massive, data aggregations of different indicators meet great challenges for time-consuming. An aggregation framework with the incremental computing and approximate computing engine is proposed. The incremental computing methods of maximum, minimum, mean and variance functions are presented, as well as two different approximate computing methods for 95% probability value function. Performance analyses are carried out with real data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kilter, J., Meyer, J., Elphick, S., et al.: Guidelines for power quality monitoring - results from CIGRE/CIRED JWG C4.112, In: International Conference on Harmonics and Quality of Power, pp. 703–707 (2014)
Wang, T., Li, Y., Deng, Z., et al.: Implementation of state-wide power quality monitoring and analysis system in China. In: 2018 IEEE Power & Energy Society General Meeting, pp.57–61 (2018)
Liu, S., Fang, J.: Fast dynamic density outlier detection algorithm for power quality disturbance data. In: Wang, G., Lin, X., Hendler, J., Song, W., Zhuoming, X., Liu, G. (eds.) Web Information Systems and Applications: 17th International Conference, WISA 2020, Guangzhou, China, September 23–25, 2020, Proceedings, pp. 194–201. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-60029-7_18
SGCC.: requirements for vertical exchange interface of power quality monitoring and analysis system (2016)
Ning, L., Xin, W., Haorui, Y.: Research on 95 probability value of power quality index based on normal distribution theory. Guizhou Electr. Power Technol. 17(4), 28–30 (2014)
Wen, H., Kou, M., et al.: A spark-based incremental algorithm for frequent itemset mining. In: Proceedings of the 2nd International Conference on Big Data and Internet of Things, pp. 53–58 (2018)
Mittal, S.: A survey of techniques for approximate computing, ACM Comput. Surv. 48(4), 62:1–62:33 (2016)
Krishnan, D.R., Quoc, D.L., Bhatotia, P., et al.: IncApprox: a data analytics system for incremental approximate computing. In: Proceedings of the 25th International Conference on World Wide Web (WWW 2016). International World Wide Web Conferences Steering Committee (2016)
Ma, S., Huai, J.: Approximate computation for big data analytics. ACM SIGWEB Newsl. 1–8 (2021)
Sheng, J., Fang, J., et al.: Implementation of multidimensional aggregate query service for time series data. J. Chongqing Univ. 43(7), 121–128 (2020)
Cormode, G., Garofalakis, M., Haas, P.J., et al.: Synopses for massive data: samples, histograms, wavelets, sketches. Found. Trends Databases 4(1–3), 1–294 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Fang, J., Bai, W., Xue, X. (2021). Real-Time Aggregation Approach for Power Quality Data. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-87571-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87570-1
Online ISBN: 978-3-030-87571-8
eBook Packages: Computer ScienceComputer Science (R0)
