Abstract
Aiming at the problems of long time consuming and low accuracy in traditional methods of abnormal data detection in power measurement automation system, this paper studies the methods of abnormal data detection in power measurement automation system. Design the data storage structure table of the electric power metering automation system database, and repair the missing data and denoise the data in the data table. Perform PAA calculation on the data to get the data feature sequence. After the P clustering algorithm pre-clusters the data, the iForest model is used to detect abnormal data to complete the research on the method. The experimental results show that the proposed detection method has the advantages of short detection time and high precision of 91.26–95.67%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, X., Qu, Y., Pang, H., et al.: Power metering pipeline fault warning technology based on deep learning algorithm. Electron. Des. Eng. 28(04), 153–157 (2020)
Gao, S., Li, C.: An improved spectral clustering algorithm for anomaly detection of power data. Comput. Simul. 36(11), 239–242 + 304 (2019)
Yang, J., Zeng, X., Yao, L., et al.: Research on abnormal electricity monitoring based on large data mining. Autom. Instrum. 08, 219–222 (2019)
Tong, X., Yu, S.: Fault detection algorithm for transmission lines based on random matrix spectrum analysis. Autom. Electr. Power Syst. 43(10), 101–115 (2019)
Xu, G., Ning, B., Zhong, Y.: Automatic matching of voltage blackout events in metering automation system. Electron. Test 04, 111–112 (2019)
Huang, J., Dai, B., Zhang, L., et al.: Study on dynamic identification of abnormal data of electric energy measurement device. Guangxi Electr. Power 41(04), 53–55 + 64 (2018)
Chen, Q., Zheng, K., Kang, C., et al.: Detection methods of abnormal electricity consumption behaviors: review and prospect. Autom. Electri. Power Syst. 42(17), 189–199 (2018)
Liu, S., Glowatz, M., Zappatore, M., et al. (eds.): e-Learning, e-Education, and Online Training, pp. 1–374. Springer, Heidelberg (2018)
Zhang, J., Chen, F., Li, B., et al.: Research on energy metering abnormity and fault analysis technology based on metrological automation system. Yunnan Electr. Power 46(02), 63–65 (2018)
Fu, W., Liu, S., Srivastava, G.: Optimization of big data scheduling in social networks. Entropy. 21(9), 902 (2019)
Liu, S., Li, Z., Zhang, Y., et al.: Introduction of key problems in long-distance learning and training. Mob. Netw. Appl. 24(1), 1–4 (2019)
Ding, M., Li., C., Li, H., et al.: Fault detection method of photovoltaic inverter based on massive data mining. Electr. Autom. 40(03), 30–32 (2018)
Shuai, L., Gelan, Y.: Advanced Hybrid Information Processing, pp. 1–594. Springer, Heidelberg
Funding
Fund projects
Design and implementation of new energy inverter based on MCU control
(CJGX2016-KY-YZK034)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Qu, Mf., Chen, N. (2021). Research on Abnormal Data Detection Method of Power Measurement Automation System. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-030-67871-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-67871-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67870-8
Online ISBN: 978-3-030-67871-5
eBook Packages: Computer ScienceComputer Science (R0)