Fault Diagnosis for Smart Grids in Pragmatic Conditions

Ntalampiras, Stavros

doi:10.1109/tsg.2016.2604120

Cited by 24 publications

(12 citation statements)

References 32 publications

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“…3) ML Techniques: ML-based data-driven methods have also been previously used for cable diagnostics, albeit without using PLC. [48] provides a substantial literature review on fault diagnosis using data-driven methods. Further, [39] and [49] also provide techniques to use SVM for cable diagnostics.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

Cable Diagnostics With Power Line Modems for Smart Grid Monitoring

et al. 2019

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“…Ntalampiras [57] proposes management of the detection of faults by means of Markov chains. Faults are localized by modeling the data to the invariant plane over time.…”

Section: Fault Detectionmentioning

confidence: 99%

A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids

Hernández-Callejo

2019

Energies

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New technological advances based on software, hardware and new materials must be implemented in smart grids. In addition, these advanced electrical grids must incorporate elements of artificial intelligence. Advances in software development must be complemented with the development of new hardware (power electronics and active distribution among others). The aforementioned must rely on the development of new materials and sensors, which should be integrated into the smart grid. Therefore, the four areas of research based on the technologies are: Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering. The review presents the algorithms, materials, devices and others paradigms applied to smart grids, classifying the works according to the four areas of research. This review has focused on the four fundamental pillars of smart grids, on the one hand, the need for more efficient operation and control, followed by advanced maintenance management, to continue planning and designing the new grids, and for conclude with the advanced measurement. As you will see in the article, new devices, new techniques, and future scenarios will make possible the transition from the current grid to the future smart grid.

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“…Fault Diagnosis Systems (FDSs), play the role of fault diagnosis, aiming to detect and identify faults, characterized when a behavior or system parameter is out of acceptable conditions [25,26,27]. This type of system was studied in both small and local applications [25] as well as larger systems [26,27]. FDSs can be classified into two main groups, those using model-based techniques and those using model-free techniques.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Motor Failure Time Using An Artificial Neural Network

Sampaio

Filho

Silva³

et al. 2019

Sensors

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Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

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Fault Diagnosis for Smart Grids in Pragmatic Conditions

Cited by 24 publications

References 32 publications

Cable Diagnostics With Power Line Modems for Smart Grid Monitoring

Cable Diagnostics With Power Line Modems for Smart Grid Monitoring

A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids

Prediction of Motor Failure Time Using An Artificial Neural Network

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