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[HTML][HTML] Deep learning for power quality
This paper aims to introduce deep learning to the power quality community by reviewing the
latest applications and discussing the open challenges of this technology. Publications …
latest applications and discussing the open challenges of this technology. Publications …
A comprehensive review of deep-learning applications to power quality analysis
Power quality (PQ) monitoring and detection has emerged as an essential requirement due
to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging …
to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging …
Two-terminal fault location method of distribution network based on adaptive convolution neural network
J Liang, T **g, H Niu, J Wang - IEEE Access, 2020 - ieeexplore.ieee.org
When a single-phase ground fault occurs in a distribution network, it is generally allowed to
operate with faults for one to two hours, which may lead to further development of the fault …
operate with faults for one to two hours, which may lead to further development of the fault …
[HTML][HTML] The use of deep learning and 2-D wavelet scalograms for power quality disturbances classification
This work investigates the use of advanced signal processing and deep Learning for pattern
recognition and classification of signals with power quality disturbances. For this purpose …
recognition and classification of signals with power quality disturbances. For this purpose …
Measuring explainability and trustworthiness of power quality disturbances classifiers using XAI—Explainable artificial intelligence
Advanced machine learning techniques have recently demonstrated outstanding
performance when applied to power quality disturbance (PQD) classification. Nevertheless …
performance when applied to power quality disturbance (PQD) classification. Nevertheless …
Frequency disturbance event detection based on synchrophasors and deep learning
Power system frequency disturbances are caused by various generation and transmission
events including generator trips, load disconnections, line trips, etc. Accurate detections of …
events including generator trips, load disconnections, line trips, etc. Accurate detections of …
Adversarial attacks and defense for CNN based power quality recognition in smart grid
Vulnerability of various machine learning methods to adversarial examples has been
recently explored in the literature. Power systems which use these vulnerable methods face …
recently explored in the literature. Power systems which use these vulnerable methods face …
An improved automated PQD classification method for distributed generators with hybrid SVM-based approach using un-decimated wavelet transform
Artificial intelligence (AI) approaches are usually coupled with the wavelet transform (WT) for
feature extraction to classify the power quality disturbances (PQDs). Therefore, selecting a …
feature extraction to classify the power quality disturbances (PQDs). Therefore, selecting a …
Classification of power quality disturbance using segmented and modified S-transform and DCNN-MSVM hybrid model
M Liu, Y Chen, Z Zhang, S Deng - IEEe Access, 2023 - ieeexplore.ieee.org
In this paper, a novel approach to classify the signals of power quality (PQ) disturbance is
proposed based on segmented and modified S-transform (SMST), deep convolutional …
proposed based on segmented and modified S-transform (SMST), deep convolutional …
A novel dual-attention optimization model for points classification of power quality disturbances
The rapid development of the power grid makes power quality disturbances (PQDs) more
complex. Accurately classifying PQDs and measuring the duration of each disturbance …
complex. Accurately classifying PQDs and measuring the duration of each disturbance …