Big Data Platform for Smart Grids Power Consumption Anomaly Detection

2019 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2019-09-26 DOI:10.15439/2019F210

Jakub Lipcak, M. Macák, B. Rossi

引用次数: 11

Abstract

Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).

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智能电网用电异常检测大数据平台

智能电网背景下的大数据处理有许多需要实时数据分析的大规模应用(例如，入侵和数据注入攻击检测，电气设备健康监测)。本文提出了一个用于电力消耗数据异常检测的大数据平台。该平台基于具有数据致密化选项的摄取层，Apache Flink作为速度层的一部分，HDFS/KairosDB作为数据存储层。我们展示了该平台在功耗异常检测场景中的应用，对速度层使用的不同替代框架(Flink, Storm, Spark)进行基准测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 Federated Conference on Computer Science and Information Systems (FedCSIS)

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