Grid surveillance and diagnostics using power line communications

Huo, Yinjia; Prasad, Gautham; Atanackovic, Lazar; Lampe, Lutz; Leung, Victor C. M.

doi:10.1109/isplc.2018.8360200

Cited by 30 publications

(25 citation statements)

References 19 publications

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“…10(b). Similar to the performance gains seen in Section V-C, our results show significant improvement in prediction accuracy when compared to the state-of-the-art [1], due to the additional insight obtained from the waveform of PLM-JTFDR and other features extracted from H ref .…”

Section: Ld Severity Assessmentsupporting

confidence: 75%

“…For both classification and regression, we use two sets of ML techniques, namely SVM and boosting, following their success both in previous cable diagnostics evaluations as well as in other domains [1], [11], [40]. SVM is a classical and popular ML technique, which constructs support vectors of hyperplanes, from a subset of the training data, for predictions.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

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Cable Diagnostics With Power Line Modems for Smart Grid Monitoring

et al. 2019

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Remote monitoring of electrical cable conditions is an essential characteristic of the next-generation smart grid, which features the ability to consistently surveil and control the grid infrastructure. In this paper, we propose a technique that harnesses power line modems (PLMs) as sensors for monitoring cable health. We envisage that all or most of these PLMs have already been deployed for data communication purposes and focus on the distribution grid or neighborhood area networks in the smart grid. For such a setting, we propose a machine learning (ML) based framework for automatic cable diagnostics by continuously monitoring the cable status to identify, assess, and locate possible degradations. As part of our technique, we also synthesize state-of-the-art reflectometry methods within the PLMs to extract beneficial features for effective performance of our proposed ML solution. Simulation results demonstrate the effectiveness of our solution under different aging conditions and varying load configurations. Finally, we reflect on our proposed diagnostics method by evaluating its robustness and comparing it with existing alternatives.

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Section: Ld Severity Assessmentsupporting

confidence: 75%

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Cable Diagnostics With Power Line Modems for Smart Grid Monitoring

et al. 2019

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“…As an example, in the context of PLC, SVM was recently explored to monitor and detect cable degradations [20]. 4) Other Tools: Another very simple non-probabilistic supervised learning algorithm is k-nearest neighbors.…”

Section: Input Layer Hidden Layer Hidden Layermentioning

confidence: 99%

Machine Learning Tips and Tricks for Power Line Communications

et al. 2019

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A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields. This paper provides a vision of what ML can do in Power Line Communications (PLC). We firstly and briefly describe classical formulations of ML, and distinguish deterministic from statistical learning models with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for the development of physical layer algorithms, for media access control and networking. Finally, other applications of PLC that can benefit from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical examples are reported to serve the purpose of validating the ideas and motivate future research endeavors in this stimulating signal/data processing field.

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“…In this sense, some authors propose taking advantage of this technology beyond a simple communication. In [35], Huo et al proposes a solution to monitor and detect cable degradations using PLC and machine learning techniques. In [36], Ercan et al discussed a solution to send PLC data through distribution transformers.…”

Section: Underground Primary Distribution Lines Structure and Charmentioning

confidence: 99%

Monitoring and Fault Location Sensor Network for Underground Distribution Lines

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et al. 2019

Sensors

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One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous supply of electricity to their customers. The primary distribution network is a critical part of these facilities because a fault in it could affect thousands of customers. However, the complexity of this network has been increased with the irruption of distributed generation, typical in a Smart Grid and which has significantly complicated some of the analyses, making it impossible to apply traditional techniques. This problem is intensified in underground lines where access is limited. As a possible solution, this paper proposes to make a deployment of a distributed sensor network along the power lines. This network proposes taking advantage of its distributed character to support new approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed network (adapted to the power grid) based on nodes that use power line communication and energy harvesting techniques. In this sense, it also describes the implementation of a real prototype that has been used in some experiments to validate this technological adaptation. Additionally, beyond a simple use for monitoring, this paper also proposes the use of this approach to solve two typical distribution system operator problems, such as: fault location and failure forecasting in power cables.

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Grid surveillance and diagnostics using power line communications

Cited by 30 publications

References 19 publications

Cable Diagnostics With Power Line Modems for Smart Grid Monitoring

Cable Diagnostics With Power Line Modems for Smart Grid Monitoring

Machine Learning Tips and Tricks for Power Line Communications

Monitoring and Fault Location Sensor Network for Underground Distribution Lines

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