Cluster Analysis for Long-Term Power Quality Data in Mining Electrical Power Network

机译：矿业电网中长期电能质量数据的聚类分析

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This paper presents the results of using data mining for analysis of long-term power quality (PQ) data recorded in a supply power network of the mining industry. The analyzed network is characterized by prominent load variation caused by welding machines, conveyor belts and drainage pumps. Additionally, a distributed generation (DG, DER) is installed in the network represented by combined heat and power plant (CHP) and steam-gas generation units. In order to explore the PQ data, a cluster analysis (CA) is proposed a prominent representative of data mining (DM). Obtained results allow to indicate CA as a proper method for automatic identification of voltage event that is valuable for flagging concept as well as identification of periods of time when the network reveals different working conditions. It allows finally to investigate and compare the influence of distributed generation on electric power network of the mining industry.

机译：本文介绍了使用数据挖掘对采矿行业的供电网络中记录的长期电能质量（PQ）数据进行分析的结果。所分析的网络的特点是由焊接机，传送带和排水泵引起的明显负载变化。此外，在由热电联产（CHP）和蒸汽产生单元组成的网络中安装了分布式发电（DG，DER）。为了探索PQ数据，提出了聚类分析（CA）作为数据挖掘（DM）的杰出代表。所获得的结果表明，CA是一种自动识别电压事件的适当方法，这对于标记概念以及网络显示不同的工作条件时的时间段识别都很有价值。最后，它允许调查和比较分布式发电对采矿业电网的影响。

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来源
《2018 Progress in Applied Electrical Engineering》|2018年|1-5|共5页
会议地点 Koscielisko(PL)
作者
Michal Jasiński; Klaudiusz Borkowski; Tomasz Sikorski; Pawel Kostvla;
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作者单位

Wroclaw University of Science and Technology Faculty of Electrical Engineering, Wroclaw, Poland;

KHGM Polska Miedz S.A., ZG “Polkowice-Sieroszowice”, Polkowice, Poland;

Wroclaw University of Science and Technology Faculty of Electrical Engineering, Wroclaw, Poland;

Wroclaw University of Science and Technology Faculty of Electrical Engineering, Wroclaw, Poland;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Power quality; Data mining; Voltage measurement; Clustering algorithms; Power measurement; Welding; Distributed power generation;

机译：电能质量;数据挖掘;电压测量;聚类算法;功率测量;焊接;分布式发电;;

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1. Clustering as a tool to support the assessment of power quality in electrical power networks with distributed generation in the mining industry [J] . Jasinski Michal, Sikorski Tomasz, Borkowski Klaudiusz Electric power systems research . 2019,第JANa期

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2. Reliability Analysis of Industrial Electrical Systems Using Bayesian Networks Considering Power Quality and Security Characteristics Applied to the IEEE 493 Standard Network [J] . Ramos L. Gustavo Andres, Torres M. Alvaro, Rognon Jean-Pierre Latin America transactions . 2007,第8期

机译：使用贝叶斯网络的工业电气系统的可靠性分析，其中考虑了适用于IEEE 493标准网络的电能质量和安全特性
3. A Data Mining Approach Combining -Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting [J] . Wenbin Wu, Mugen Peng Internet of Things Journal, IEEE . 2017,第4期

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4. Cluster Analysis for Long-Term Power Quality Data in Mining Electrical Power Network [C] . Michal Jasiński, Klaudiusz Borkowski, Tomasz Sikorski, Conference on Progress in Applied Electrical Engineering . 2018

机译：采矿电力网络中长期电能质量数据的集群分析
5. A Data Analytics Framework for Smart Grids: Spatio-temporal Wind Power Analysis and Synchrophasor Data Mining [D] . He, Miao 2013

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6. Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis [O] . Chao Zhang, Pengcheng Zhang, Weizhan Zhang 2017

机译：无线传感器网络中的集群合作：建模和性能分析
7. Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant [O] . Michał Jasiński 2021

机译：虚拟电厂长期电能质量数据的组合相关与集群分析

Cluster Analysis for Long-Term Power Quality Data in Mining Electrical Power Network

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