Analysis of the on-going Research and demonstration Efforts on Smart Grids in Europe

Losa, Ilaria; Nigris, Michelle De; Van, Thong Vu; Herold, I.; Hribernik, Wolfgang

doi:10.1049/cp.2013.0958

Cited by 4 publications

(2 citation statements)

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“…Researches have shown that ∼80–90% of power outage losses are caused by distribution network fault [2], hence the fault diagnosis of the distributed network has always been a fundamental topic. Especially with the continuous increasing development of the distributed generators (DGs), the dynamic behaviour of the distribution networks become more variable, which brings new challenges to this problem [3, 4]. For example, the distribution network is changing from a single‐directional radial network to a complex topology network with bi‐direction power flow [5].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of the single phase‐to‐ground fault in distribution network based on feature extraction and transformation from the waveforms

Shi

Zhang

et al. 2020

IET Generation Trans & Dist

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The increasing permeation of the distributed generators in the power system brings great challenges for fault diagnosis, especially for the distribution networks with ungrounded neutral or grounded by Peterson coil as the fault current is limited and easily affected by the noises and interferences. A single phase‐to‐ground fault section identification method is proposed based on feature extraction of the synchronous waveforms and the calculation of the eigenvalues for the time‐sequenced features. First, several fault features are defined and extracted from the synchronous current waveforms obtained by the fault recorders. Then, the topology related fault feature matrix is constructed using the time‐series features obtained from different measurement sites, and the eigenvalues of the matrix are calculated based on the random matrix theory. Lastly, using the distribution characteristics of the eigenvalues, improved K‐means clustering algorithm is utilised in classifying the fault cases and identifying the faulty sections. The effectiveness of the proposed scheme is verified by IEEE 34 nodes test system and a multi‐feeder distribution network.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of the single phase‐to‐ground fault in distribution network based on feature extraction and transformation from the waveforms

Shi

Zhang

et al. 2020

IET Generation Trans & Dist

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“…e EEGI Roadmap denes the research, development and demonstration challenges that both European transmission and distribution system operators should address in the next years with the aim to face the requirements linked to the evolution of power systems and to respond to different external factors. For this reason, smart-grid projects are receiving a lot of attention (Hübner & Prüggler, 2011;Giordano et al, 2011;Losa, De Nigris & Van, 2013). New perspectives emerge for energy management.…”

Section: Introductionmentioning

confidence: 99%

Two-level clustering methodology for smart metering data

2020

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Energy efficiency and sustainability are important factors to address in the context of smart cities. In this sense, a necessary functionality is to reveal various preferences, behaviors, and characteristics of individual consumers, considering the energy consumption information from smart meters. In this paper, we introduce a general methodology and a specific two-level clustering approach that can be used to group, considering global and local features, energy consumptions and productions of households. Thus, characteristic load and production profiles can be determined for each consumer and prosumer, respectively. The obtained results will be generally applicable and will be useful in a general business analytics context.

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Consumer Segmentation Through Multi-instance Clustering Time-Series Energy Data from Smart Meters

Gómez-Boix

Arco

Nowé

2017

Soft Computing for Sustainability Science

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Analysis of the on-going Research and demonstration Efforts on Smart Grids in Europe

Cited by 4 publications

References 1 publication

Diagnosis of the single phase‐to‐ground fault in distribution network based on feature extraction and transformation from the waveforms

Diagnosis of the single phase‐to‐ground fault in distribution network based on feature extraction and transformation from the waveforms

Two-level clustering methodology for smart metering data

Consumer Segmentation Through Multi-instance Clustering Time-Series Energy Data from Smart Meters

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