Fault Detection, Classification and Localization Along the Power Grid
The article presents a new method combining fuzzy logic and neural networks to detect, categorize, identify and locate faults based on the data of sensors and smart meters
Fault Detection, Classification And Location In Power Distribution
Keywords: fault classification, fault detection, fuzzy logic, smart meter data, smart grid ©The Author(''s). This is an open access article distributed under the terms of theCreative Commons
High Performance Platform to Detect Faults in the Smart Grid by
Abstract: Inferring faults throughout the power grid involves fast calculation, large scale of data, and low latency. Our heterogeneous architecture in the edge offers such high computing
Fault detection and classification using deep learning method
Fault classification: which is considered as a process including fault detection of various fault types by clustering analysis and classification of detected faults in predefined
Automatic Fault Identification in WSN Based Smart
Recent works related to fault detection in WSN based smart grid environments are mentioned . below . Arifa et al. [21] proposed a wireless sensor based smart grid by using cognitively driven load .
Intelligent Fault Detection and Classification Schemes for Smart
Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault
FUTURE-PROOF ISLANDING DETECTION SCHEMES IN SUNDOM SMART GRID
detection zone (NDZ) near a power balance situation and maloperation due to other network events like, for example, utility grid / parallel MV feeder faults or utility grid frequency
Intelligent Fault Detection and Classification Schemes for Smart
Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault
Intelligent Fault Detection and Classification Schemes
Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics
Intelligent Fault Detection and Classification Schemes
Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from
6 FAQs about [Pitcairn Islands fault detection in smart grid]
Can computational intelligence detect islanding phenomenon in smart distributed grids?
The importance of computational intelligence to detect islanding phenomenon in smart distributed grids , , , . Those works present a probabilistic Neural Network (NN) and Support Vector Machine (SVM) as powerful self-adapted machine learning techniques for fault detection.
Are there any problems in autonomous smart grid fault detection?
There are several studies on im-proving networks for artificial intelligence, but there are still many unresolved issues in autonomous smart grid fault detection, from the aspects of network architecture, network protocol to system optimization objective.
What is fault detection of smart grid?
Fault detection of smart grid is an important research problem that has attracted increasing attention from both academia and industry. It is essential to improve the performance and reduce disruptions of smart power systems.
Can machine learning detect faults of smart grids?
In this paper, a reliable machine learning technique is proposed to detect and classify different faults of smart grids. The proposed technique benefits from the principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA is used to reduce the size of the dataset matrixes.
Can KNN detect faults in a smart grid?
In this paper, the KNN technique augmented with principal component analysis (PCA) and linear discriminant analysis (LDA) is used to detect and classify different faults in a smart grid.
How to classify faults in a smart grid?
A classification technique based-on the conventional K-NN algorithm is proposed to detect and classify different types of fault in a smart grid. In the proposed technique, the PCA method is used to decrease the dataset size while LDA provides online classification before applying the K-NN.