Towards Data-Driven Malfunctioning Detection in Public and Industrial Power Grids

Fellner, David; Brunner, Helfried; Strasser, Thomas; Kästner, Wolfgang

doi:10.1109/etfa46521.2020.9212178

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…The outcomes of the evaluations on data properties and the data features used for detection are quite promising [6]. Moreover, a solution developed on data collected in a laboratory setting during an H2020 ERIGrid 2.0 Lab Access [7] showed very good results.…”

Section: Validation Resultsmentioning

confidence: 99%

“…Furthermore, data processing methods as well as detection methods were applied on the data so as to assess their performance in the task at hand. Dimensionality reduction methods for processing data as well as supervised learning approaches are employed as their applicability is suggested by previous work [9] as well as literature [10]. This work has the following content: In Section I, a discussion of issues in power distribution grids and monitoring needs is conducted.…”

Section: Introductionmentioning

confidence: 99%

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Data Driven Transformer Level Misconfiguration Detection in Power Distribution Grids

Fellner

Strasser

Kästner

et al. 2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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As more novel devices are integrated into the electricity grid due to the changes taking place in the energy system, ways of detecting deviations from the intended settings are needed. If misconfigurations of, for example, reactive power control curves of inverters go unnoticed, the safe and reliable operation of the power grid can no longer be ensured due to possible voltage violations or overloadings. Therefore, methods of detection of misconfigurations of said inverters using operational data at transformers are presented and compared. These methods include preprocessing by dimensionality reduction as well as detection by supervised learning approaches. The data used is of high reliability as it was collected in a lab setting reenacting typical and relevant grid operation situations. Furthermore, this data was recreated by simulation to validate the simulation data, which could also potentially be used for detection applications on a bigger scale. The results for both data sources were compared and conclusions drawn about applicability and usability for grid operators.

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Section: Validation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Driven Transformer Level Misconfiguration Detection in Power Distribution Grids

Fellner

Strasser

Kästner

et al. 2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…It will seriously endanger the operation safety of the integrated energy system [2]. Therefore, it is of great significance to accurately extract equipment fault information, locate the entity relationship of fault equipment, construct the fault knowledge graph of IIoT communication equipment [3,4] and realize real-time fault analysis and efficient troubleshooting of IIoT communication equipment.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT

et al. 2022

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The Industrial Internet of Things (IIoT) deploys massive communication devices for information collection and process control. Once it reaches failure, it will seriously affect the operation of the industrial system. This paper proposes a new method for multi-entity knowledge joint extraction (MEKJE) of IIoT communication equipment faults. This method constructs a multi-task tightly coupled model of fault entity and relationship extraction. We use it to implement word embedding and bidirectional semantic capture to generate computable text vectors. At the same time, a multi-entity segmentation method is proposed, which uses noise filtering to distinguish the multi-fault relationship of single corpus. We constructed a dataset of communication failures in power IIoT and conducted experiments. The experimental results show that the method performs best in tests with the Faulty Text dataset and the CLUENER dataset. In particular, the model achieves an F1 value of 78.6% in the evaluation of relationship extraction for multiple entities, and a significant improvement of 5–8% in its accuracy and recall. It enables effective mapping and accurate extraction of fault knowledge data.

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Applying Deep Learning-based concepts for the detection of device misconfigurations in power systems

Fellner

Strasser

Kästner

2022

Sustainable Energy, Grids and Networks

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Towards Data-Driven Malfunctioning Detection in Public and Industrial Power Grids

Cited by 5 publications

References 12 publications

Data Driven Transformer Level Misconfiguration Detection in Power Distribution Grids

Data Driven Transformer Level Misconfiguration Detection in Power Distribution Grids

A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT

Applying Deep Learning-based concepts for the detection of device misconfigurations in power systems

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