| 基于Spark的输变电线路实时故障监测研究 |
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| 引用本文： | 陈建峡,朱季骐,张 月,张晓星,吕俊涛,白德盟. 基于Spark的输变电线路实时故障监测研究[J]. 计算机工程与应用, 2018, 54(5): 265-270. DOI: 10.3778/j.issn.1002-8331.1609-0025 |
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| 作者姓名： | 陈建峡 朱季骐 张 月 张晓星 吕俊涛 白德盟 |
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| 作者单位： | 1.湖北工业大学 计算机学院，武汉 4300682.武汉大学 电气工程学院，武汉 4300723.国网山东省电力公司 电力科学研究院，济南 250002 |
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| 摘 要： | 输变电线路状态监测数据是智能电网中数据量很大的一部分，不仅包括在线的状态监测数据，还包括设备的基本信息、实验数据、缺陷记录等，在数据处理的可靠性和实时性方面的要求都很高。根据实际应用中输变电线路的故障类型，设计并实现了输变电线路实时数据故障监测模型。其中，利用高效处理实时数据的Spark系统，研发出基于Spark的分布式ISODATA和模糊KNN大数据分析算法，与单机KNN算法相比，在时间性能上提高了70.75%效率，具有明显的计算效率优势。
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| 关 键 词： | 实时大数据 输变电线路 故障监测 分布式迭代自组织数据分析算法（ISODATA） 分布式模糊k最近邻分类算法（KNN） |
Real-time fault monitoring of transmission lines based on Spark |
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| CHEN Jianxia,ZHU Jiqi,ZHANG Yue,ZHANG Xiaoxing,LV Juntao,BAI Demeng. Real-time fault monitoring of transmission lines based on Spark[J]. Computer Engineering and Applications, 2018, 54(5): 265-270. DOI: 10.3778/j.issn.1002-8331.1609-0025 |
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| Authors: | CHEN Jianxia ZHU Jiqi ZHANG Yue ZHANG Xiaoxing LV Juntao BAI Demeng |
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| Affiliation: | 1.School of Computer Science, Hubei University of Technology, Wuhan 430068, China2.School of Electrical Engineering, Wuhan University, Wuhan 430072, China3.State Grid Shandong Electric Power Research Institute, Jinan 250002, China |
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| Abstract: | Since the monitoring data of transmission lines are the largest part of the amount of data in the smart grid, including not only the online condition monitoring data, but also the basic information of the devices, the experimental data, defect records, it requires a higher performance of the reliability and real-time in the data processing. The paper designs and realizes a novel model to solve the real-time fault monitoring of transmission lines according to the practical application of power transmission line faults’ types. In particular, the paper constructs a distributed cluster based on Spark, an efficient real-time data processing system, for the transmission line fault real-time monitoring, develops a distributed ISODATA and fuzzy KNN big data analysis algorithm. Compared with standalone KNN algorithm, it improves 70.75% efficiency of the time performance. Experimental results demonstrate the proposed approach has the obvious advantages of the computational efficiency. |
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| Keywords: | real-time big data transmission lines fault monitoring distributed Iterative Self Organizing Data Analysis Techniques Algorithm（ISODATA） algorithm distributed fuzzy k-Nearest Neighbor（KNN） algorithm |
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