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  • 1.
    Mohammadi, Younes
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Polajžer, Boštjan
    Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, 2000, Slovenia.
    Leborgne, Roberto Chouhy
    Universidade Federal do Rio Grande do Sul, Osvaldo Aranha, 99, RS, Porto Alegre, 90035-190, Brazil.
    Khodadad, Davood
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Most influential feature form for supervised learning in voltage sag source localization2024Ingår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 133, nr Part D, artikel-id 108331Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The paper investigates the application of machine learning (ML) for voltage sag source localization (VSSL) in electrical power systems. To overcome feature-selection challenges for traditional ML methods and provide more meaningful sequential features for deep learning methods, the paper proposes three time-sample-based feature forms, and evaluates an existing feature form. The effectiveness of these feature forms is assessed using k-means clustering with k = 2 referred to as downstream and upstream classes, according to the direction of voltage sag origins. Through extensive voltage sag simulations, including noises in a regional electrical power network, k-means identifies a sequence involving the multiplication of positive-sequence current magnitude with the sine of its angle as the most prominent feature form. The study develops further traditional ML methods such as decision trees (DT), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), an ensemble learning (EL), and a designed one-dimensional convolutional neural network (1D-CNN). The results found that the combination of 1D-CNN or SVM with the most prominent feature achieved the highest accuracies of 99.37% and 99.13%, respectively, with acceptable/fast prediction times, enhancing VSSL. The exceptional performance of the CNN was also approved by field measurements in a real power network. However, selecting the best ML methods for deployment requires a trade-off between accuracy and real-time implementation requirements. The research findings benefit network operators, large factory owners, and renewable energy park producers. They enable preventive maintenance, reduce equipment downtime/damage in industry and electrical power systems, mitigate financial losses, and facilitate the assignment of power-quality penalties to responsible parties.

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  • 2.
    Nieves, Juan Carlos
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Espinoza, Angelina
    Penya, Yoseba K.
    Ortega de Mues, Mariano
    Pena, Aitor
    Intelligence distribution for data processing in smart grids: a semantic approach2013Ingår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 26, nr 8, s. 1841-1853Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The smart grid vision demands both syntactic interoperability in order to physically be able to interchange data and semantic interoperability to properly understand and interpret its meaning. The IEC and the EPRI have backed to this end the harmonization of two widely used industrial standards, the CIM and the IEC 61850, as the global unified ontology in the smart grid scenario. Still, persisting such a huge general ontology in each and every one of the members of a distributed system is neither practical nor feasible. Moreover, the smart grid will be a heterogeneous conglomerate of legacy and upcoming architectures that will require first the possibility of representing all the existing assets in the power network as well as new unknown ones, and second, the collaboration of different entities of the system in order to deploy complex activities. Finally, the smart grid presents diverse time span requirements, such as real-time, and all of them must be addressed efficiently but use resources sparingly. Against this background, we put forward an architecture of intelligent nodes spread all over the smart grid structure. Each intelligent node only has a profile of the global ontology. Moreover, adding reasoning abilities, we achieve simultaneously the required intelligence distribution and local decision making. Furthermore, we address the aforementioned real-time and quasi-real-time requirements by integrating stream data processing tools within the intelligent node. Combined with the knowledge base profile and the reasoning capability, our intelligent architecture supports semantic stream reasoning. We have illustrated the feasibility of this approach with a prototype composed of three substations and the description of several complex activities involving a number of different entities of the smart grid. Moreover, we have also addressed the potential extension of the unified ontology. (C) 2013 Elsevier Ltd. All rights reserved.

  • 3. Tchappi, Igor H.
    et al.
    Galland, Stephane
    Kamla, Vivient Corneille
    Kamgang, Jean Claude
    Mualla, Yazan
    Najjar, Amro
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hilaire, Vincent
    A critical review of the use of holonic paradigm in traffic and transportation systems2020Ingår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 90, artikel-id 103503Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    The paper presents a critical review of the use of holonic paradigm in order to model and simulate traffic and transportation systems. After an introduction presenting the principles of this paradigm as well as its frameworks and concepts, the paper surveys existing works using the holonic paradigm for traffic and transportation applications. This is followed by a detailed analysis of the results of the survey. In particular, the relevance, the design approaches and the holonification orientation methodologies are investigated. Finally, based on this extensive review, open issues of holonic paradigm in modeling and simulation of traffic and transportation models are highlighted.

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