Anomaly detection in Smart Grid data: An experience report

Rossi, Bruno; Chren, Stanislav; Bühnová, Barbora; Pitner, Tomáš

doi:10.1109/smc.2016.7844583

Cited by 49 publications

(26 citation statements)

References 20 publications

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“…The prediction can be used to intelligently change the power production of non-renewable energy power plants to meet the current and near-future energy demand, which will help to save resources and at the same time prevent potential blackouts [22]. With the help of SM deployments, the time required to detect these abnormalities is significantly reduced when compared to a traditional power distribution grid [4,23,24].…”

Section: Smart Grid (Sg)mentioning

confidence: 99%

Smart Grid Testing Management Platform (SGTMP)

et al. 2018

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The Smart Grid (SG) is nowadays an essential part of modern society, providing two-way energy flow and smart services between providers and customers. The main drawback is the SG complexity, with an SG composed of multiple layers, with devices and components that have to communicate, integrate, and cooperate as a unified system. Such complexity brings challenges for ensuring proper reliability, resilience, availability, integration, and security of the overall infrastructure. In this paper, we introduce a new smart grid testing management platform (herein called SGTMP) for executing real-time hardware-in-the-loop SG tests and experiments that can simplify the testing process in the context of interconnected SG devices. We discuss the context of usage, the system architecture, the interactive web-based interface, the provided API, and the integration with co-simulations frameworks to provide virtualized environments for testing. Furthermore, we present one main scenario about the stress-testing of SG devices that can showcase the applicability of the platform.

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Section: Smart Grid (Sg)mentioning

confidence: 99%

Smart Grid Testing Management Platform (SGTMP)

et al. 2018

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“…This article is focused on this last challenge, as SGs gave rise to large amount of opportunities in terms of data analytics initiatives: the large availability of data from the smart infrastructure allows many decision support initiatives, but also the implementation of predictive algorithms to improve the provided services [309]. Typical examples involve power load forecasting predicting the possible load curve that represents the electricity consumed by customers over time [132], or Demand Response (DR) representing load balancing of energy supply and demand during peak hours [86].…”

Section: Introductionmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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“…There are a plethora of use cases for the application of big data analysis in the context of SGs [5], [6], like anomaly detection methods to detect power consumption anomalous behaviours [7], [8], the analysis of false data injection attacks [9], load forecasting for efficient energy management [10], among others. Such data analysis requirements create needs to define architectures and platforms to support large scale data analysis.…”

Section: Introductionmentioning

confidence: 99%

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

Lipcak¹,

Macák²,

Rossi³

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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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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Anomaly detection in Smart Grid data: An experience report

Cited by 49 publications

References 20 publications

Smart Grid Testing Management Platform (SGTMP)

Smart Grid Testing Management Platform (SGTMP)

Smart Grids Data Analysis: A Systematic Mapping Study

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

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