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Data Mining in Smart Grids
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Data Mining in Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (30 March 2020) | Viewed by 16041

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Alfredo Vaccaro

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy
Interests: power systems analysis; reliable computing; decentralized optimization; self-organizing sensor networks; renewable power generators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Effective smart grid operation requires rapid decisions in a data-rich, but information limited environment. In this context, the grid sensors data-streaming could not provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence.

To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision maker.

This Special Issue will be focused on the emerging methodologies for data mining in Smart Grids. In this area, it will address many relevant topics, ranging from methods for uncertainty management, to advanced dispatching.

This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices.

Potential topics include, but are not limited to, the following:

- Fuzziness in smart grids computing

- Emerging techniques for renewable energy forecasting

- Robust and proactive solution of optimal smart grids operation

- Fuzzy-based smart grids monitoring and control frameworks

- Granular computing for uncertainty management in smart grids

- Self-organizing and decentralized paradigms for information processing

 

Dr. Alfredo Vaccaro
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

18 pages, 955 KiB  
Article
Enabling Methodologies for Predictive Power System Resilience Analysis in the Presence of Extreme Wind Gusts
by Ennio Brugnetti, Guido Coletta, Fabrizio De Caro, Alfredo Vaccaro and Domenico Villacci
Energies 2020, 13(13), 3501; https://0-doi-org.brum.beds.ac.uk/10.3390/en13133501 - 07 Jul 2020
Cited by 7 | Viewed by 1970
Abstract
Modern power system operation should comply with strictly reliability and security constraints, which aim at guarantee the correct system operation also in the presence of severe internal and external disturbances. Amongst the possible phenomena perturbing correct system operation, the predictive assessment of the [...] Read more.
Modern power system operation should comply with strictly reliability and security constraints, which aim at guarantee the correct system operation also in the presence of severe internal and external disturbances. Amongst the possible phenomena perturbing correct system operation, the predictive assessment of the impacts induced by extreme weather events has been considered as one of the most critical issues to address, since they can induce multiple, and large-scale system contingencies. In this context, the development of new computing paradigms for resilience analysis has been recognized as a very promising research direction. To address this issue, this paper proposes two methodologies, which are based on Time Varying Markov Chain and Dynamic Bayesian Network, for assessing the system resilience against extreme wind gusts. The main difference between the proposed methodologies and the traditional solution techniques is the improved capability in modelling the occurrence of multiple component faults and repairing, which cannot be neglected in the presence of extreme events, as experienced worldwide by several Transmission System Operators. Several cases studies and benchmark comparisons are presented and discussed in order to demonstrate the effectiveness of the proposed methods in the task of assessing the power system resilience in realistic operation scenarios. Full article
(This article belongs to the Special Issue Data Mining in Smart Grids)
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24 pages, 640 KiB  
Article
A Modern Data-Mining Approach Based on Genetically Optimized Fuzzy Systems for Interpretable and Accurate Smart-Grid Stability Prediction
by Marian B. Gorzałczany, Jakub Piekoszewski and Filip Rudziński
Energies 2020, 13(10), 2559; https://0-doi-org.brum.beds.ac.uk/10.3390/en13102559 - 18 May 2020
Cited by 11 | Viewed by 2684
Abstract
The main objective and contribution of this paper was/is the application of our knowledge-based data-mining approach (a fuzzy rule-based classification system) characterized by a genetically optimized interpretability-accuracy trade-off (by means of multi-objective evolutionary optimization algorithms) for transparent and accurate prediction of decentral smart [...] Read more.
The main objective and contribution of this paper was/is the application of our knowledge-based data-mining approach (a fuzzy rule-based classification system) characterized by a genetically optimized interpretability-accuracy trade-off (by means of multi-objective evolutionary optimization algorithms) for transparent and accurate prediction of decentral smart grid control (DSGC) stability. In particular, we aim at uncovering the hierarchy of influence of particular input attributes upon the DSGC stability. Moreover, we also analyze the effect of possible "overlapping" of some input attributes over the other ones from the DSGC-stability perspective. The recently published and available at the UCI Database Repository Electrical Grid Stability Simulated Data Set and its input-aggregate-based concise version were used in our experiments. A comparison with 39 alternative approaches was also performed, demonstrating the advantages of our approach in terms of: (i) interpretable and accurate fuzzy rule-based DSGC-stability prediction and (ii) uncovering the hierarchy of DSGC-system’s attribute significance. Full article
(This article belongs to the Special Issue Data Mining in Smart Grids)
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14 pages, 1936 KiB  
Article
Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid
by Heung-gu Son, Yunsun Kim and Sahm Kim
Energies 2020, 13(9), 2377; https://0-doi-org.brum.beds.ac.uk/10.3390/en13092377 - 09 May 2020
Cited by 5 | Viewed by 2520
Abstract
This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical [...] Read more.
This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box–Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt–Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), and neural network nonlinear autoregressive (NN-AR) are used for demand forecasting based on clustering. The results show that the time-series clustering method performs better than the method using the total amount of electricity demand in terms of the mean absolute percentage error (MAPE). Full article
(This article belongs to the Special Issue Data Mining in Smart Grids)
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15 pages, 2331 KiB  
Article
Partial Discharge Data Matching Method for GIS Case-Based Reasoning
by Jiejie Dai, Yingbing Teng, Zhaoqi Zhang, Zhongmin Yu, Gehao Sheng and Xiuchen Jiang
Energies 2019, 12(19), 3677; https://0-doi-org.brum.beds.ac.uk/10.3390/en12193677 - 26 Sep 2019
Cited by 11 | Viewed by 2604
Abstract
With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem [...] Read more.
With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods. Full article
(This article belongs to the Special Issue Data Mining in Smart Grids)
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16 pages, 3226 KiB  
Article
Wind Farm NWP Data Preprocessing Method Based on t-SNE
by Jiu Gu, Yining Wang, Da Xie and Yu Zhang
Energies 2019, 12(19), 3622; https://0-doi-org.brum.beds.ac.uk/10.3390/en12193622 - 23 Sep 2019
Cited by 9 | Viewed by 2980
Abstract
The operation prediction of wind farms will be accompanied by the need for massive data processing, especially the preprocessing of wind farm meteorological data or numerical weather prediction (NWP). Because NWP data are strongly correlated with wind farm operation, proper processing of NWP [...] Read more.
The operation prediction of wind farms will be accompanied by the need for massive data processing, especially the preprocessing of wind farm meteorological data or numerical weather prediction (NWP). Because NWP data are strongly correlated with wind farm operation, proper processing of NWP data could not only reduce data volume but also improve the correlations of wind farm operation predictions. For this purpose, this paper proposes a data preprocessing algorithm based on t-distributed stochastic neighbor embedding (t-SNE). Firstly, the data collected were normalized to eliminate the influence caused by different dimensions. The t-SNE algorithm is then used to reduce the dimensionality of the NWP data related to wind farm operation. Finally, the wind farm data visualization platform is established. In this paper, 22 index variables in NWP data were taken as objects. The t-SNE method was used to preprocess the NWP historical data of a wind farm, and the results were compared with the results of the principal component analysis (PCA) algorithm. It outperformed PCA in error precision; in addition, t-SNE dimension reduction preprocessing also had a visual effect, which could be applied to big data visualization platforms. A long short-term memory network (LSTM) was used to predict the operation of the wind farm by combining the preprocessed NWP data and the operation data. The simulation results proved that the effect of the preprocessed NWP data based on t-SNE on the wind power prediction was significantly improved. Full article
(This article belongs to the Special Issue Data Mining in Smart Grids)
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14 pages, 2157 KiB  
Article
A Decentralized Architecture Based on Cooperative Dynamic Agents for Online Voltage Regulation in Smart Grids
by Amedeo Andreotti, Alberto Petrillo, Stefania Santini, Alfredo Vaccaro and Domenico Villacci
Energies 2019, 12(7), 1386; https://0-doi-org.brum.beds.ac.uk/10.3390/en12071386 - 10 Apr 2019
Cited by 12 | Viewed by 2568
Abstract
The large-scale integration of renewable power generators in power grids may cause complex technical issues, which could hinder their hosting capacity. In this context, the mitigation of the grid voltage fluctuations represents one of the main issues to address. Although different control paradigms, [...] Read more.
The large-scale integration of renewable power generators in power grids may cause complex technical issues, which could hinder their hosting capacity. In this context, the mitigation of the grid voltage fluctuations represents one of the main issues to address. Although different control paradigms, based on both local and global computing, could be deployed for online voltage regulation in active power networks, the identification of the most effective approach, which is influenced by the available computing resources, and the required control performance, is still an open problem. To face this issue, in this paper, the mathematical backbone, the expected performance, and the architectural requirements of a novel decentralized control paradigm based on dynamic agents are analyzed. Detailed simulation results obtained in a realistic case study are presented and discussed to prove the effectiveness and the robustness of the proposed method. Full article
(This article belongs to the Special Issue Data Mining in Smart Grids)
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