Abstract
In allusion to the widespread anomalous data in substation primary equipment condition monitoring, this paper proposes an OPTICS (Ordering Points To Identify the Clustering Structure) clustering-based condition monitoring anomalous data filtering algorithm. Through the characteristic analysis of historical primary equipment condition monitoring data, an anomalous data filtering mechanism was built based on density clustering. The effectiveness of detecting anomalous data was verified through the experiments on one 110 kV substation equipment transformer oil chromatography and the GIS (Gas Insulated Substation) SF6 density micro water. Compared with traditional anomalous data detection algorithms, the OPTICS Clustering-based algorithm has shown significant performance in identifying the features of anomalous data as well as filtering condition monitoring anomalous data. Noises were reduced effectively and the overall reliability of condition monitoring data was also improved.
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Zhang, Q., Wang, X., Wang, X. (2015). An OPTICS Clustering-Based Anomalous Data Filtering Algorithm for Condition Monitoring of Power Equipment. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2015. Lecture Notes in Computer Science(), vol 9518. Springer, Cham. https://doi.org/10.1007/978-3-319-27430-0_9
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DOI: https://doi.org/10.1007/978-3-319-27430-0_9
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