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Deep Neural Network based Wide-Area Event Classification in Power Systems [article]

Iman Niazazari, Amir Ghasemkhani, Yunchuan Liu, Shuchismita Biswas, Hanif Livani, Lei Yang, Virgilio Centeno
2020 arXiv   pre-print
This paper presents a wide-area event classification in transmission power grids.  ...  The deep neural network (DNN) based classifier is developed based on the availability of data from time-synchronized phasor measurement units (PMUs).  ...  This calls for advanced and robust event Deep Neural Network based Wide-Area Event Classification in Power Systems This material is based upon work supported by the Department of Energy National Energy  ... 
arXiv:2008.10151v1 fatcat:24rmq5cxwrehjk4woyemrx3vxe

A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation

Chen Li, Gaoqi Liang, Huan Zhao, Guo Chen
2021 Frontiers in Energy Research  
This paper proposes a novel algorithm for load event detection in smart homes based on wide and deep learning that combines the convolutional neural network (CNN) and the soft-max regression (SMR).  ...  Compared to the standard wide-deep, pure CNN, and SMR models, the hybrid wide-deep model powered by RSB demonstrates its superiority in terms of accuracy, convergence speed, and robustness.  ...  A Novel Event Detection and Classification Scheme Using Wide Area Frequency Measurements. ” IEEE Transactions on Smart Grid.  ... 
doi:10.3389/fenrg.2021.720831 fatcat:krpz5m4egnhtxa6n6g5aco3ol4

Power System Event Identification based on Deep Neural Network with Information Loading [article]

Jie Shi, Brandon Foggo, Nanpeng Yu
2021 arXiv   pre-print
In this paper, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs)  ...  Online power system event identification and classification is crucial to enhancing the reliability of transmission systems.  ...  a deep neural network based framework to identify and classify power system events in real time.  ... 
arXiv:2011.06718v2 fatcat:qsvynpxmcfghpes5etr6plwleq

Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network

Do-In Kim
2021 Energies  
This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification.  ...  , where the measured synchrophasor data in the power systems are allocated by time and space domains.  ...  In addition, for location identification in wide-area power systems, the grouping of the power system area for classification is key.  ... 
doi:10.3390/en14154446 fatcat:l3rwcpbz5bgafayggvqnd46gzm

Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification [article]

Yongli Zhu, Chengxi Liu, Kai Sun
2018 arXiv   pre-print
This paper presents a study on power grid disturbance classification by Deep Learning (DL).  ...  The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.  ...  CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network).  ... 
arXiv:1812.09427v1 fatcat:s6q4u7sok5fklb5uxthcfezq44

Home Appliance Identification for Nilm Systems Based on Deep Neural Networks

Deyvison de Paiva Penha, Adriana Rosa Garcez Castro
2018 International Journal of Artificial Intelligence & Applications  
The system is based on a Convolutional Neural Network to classify residential equipment.  ...  As inputs to the system, transient power signal data obtained at the time an equipment is connected in a residence is used.  ...  Due to this advancement in the area, some researchers have sought to apply as Deep Neural Networks to equipment identification problems in NILM systems.  ... 
doi:10.5121/ijaia.2018.9206 fatcat:n5yx7fizqbejhhk3otj34a6vp4

Dual deep neural network-based classifiers to detect experimental seizures

Hyun-Jong Jang, Kyung-Ok Cho
2019 Korean Journal of Physiology and Pharmacology  
By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events  ...  To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks.  ...  Periodograms were widely used to determine the power density of frequency components in EEG signals.  ... 
doi:10.4196/kjpp.2019.23.2.131 pmid:30820157 pmcid:PMC6384195 fatcat:rpv2lr3qobdebjr7bif6iav2g4

Acoustic Classification using Deep Learning

Muhammad Ahsan Aslam, Muhammad Umer, Muhammad Kashif, Ramzan Talib, Usama Khalid
2018 International Journal of Advanced Computer Science and Applications  
This work focuses on the acoustic classification and improves the performance of deep neural networks by using hybrid feature extraction methods.  ...  Significantly machines in different conditions can have the hearings capability like smartphones, different software or security systems.  ...  The classifications of acoustic events have aimed to the divisions of different acoustic events in target groups for specified area [8] .  ... 
doi:10.14569/ijacsa.2018.090820 fatcat:3y4lbrxzcjgt5bghn2g2hk2cvq

Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Seyed Mahdi Miraftabzadeh, Michela Longo, Federica Foiadelli, Marco Pasetti, Raul Igual
2021 Energies  
of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting.  ...  Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis  ...  concerning overfitting issues; Deep Neural Network (DNN): Deep neural network, or multi-layer neural network, is widely used in several domains.  ... 
doi:10.3390/en14164776 fatcat:cr2j3psazfeztk3qaeu7giw5sa

IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Andrej Zgank
2021 Sensors  
The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.  ...  A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project.  ...  The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/s21030676 pmid:33498163 fatcat:aatsrch3vrfcne7uwoarvk434e

Machine Learning For Distributed Acoustic Sensors, Classic versus Image and Deep Neural Networks Approach [article]

Mugdim Bublin
2019 arXiv   pre-print
In this work, we applied and compared two approaches for event detection using DAS: Classic machine learning approach and the approach based on image processing and deep learning.  ...  Although with both approaches acceptable performance can be achieved, the preliminary results show that image based deep learning is more promising approach, offering six times lower event detection delay  ...  deep neural network to recognize the events in the image.  ... 
arXiv:1904.11546v1 fatcat:7bxwzvattbhtbbojsuw3v6mete

Image classification and comparision of different Convolutional neural network srtuctures based On Keras

Pooja.V.Magdum, Mahadev.S. Patil
2020 Zenodo  
Deep learning is a technology inspired by the functioning of human brain. Convolutional neural networks (CNN) become very popular for image classification in deep learning.  ...  In this paper, discussed about a deep learning convolutional network structures based on keras.  ...  , Mark Parsons have explained a deep learning based algorithm to differentiate photovoltaic events from other grid events, and it conclude that a deep convolutional neural network can achieve higher classification  ... 
doi:10.5281/zenodo.3597134 fatcat:b5ryvznxdffr3euhapwq2icnbq

Video Classification Using Deep Learning

Sheshang Degadwala, Harsh Parekh, Nirav Ghodadra, Harsh Chauhan, Mashkoor Hussaini
2020 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
Video order has been comprehensively investigated in PC vision in view of its wide spread applications.  ...  With the phenomenal accomplishment of significant learning, convolutional neural frameworks (CNNs) and their 3-D varieties have been considered in the video territory for an immense grouping of order assignments  ...  Jothilakshmi portrays Crowd Video Event Classification Using Convolution Neural Network. In that they utilized Deep Learning, CNN, SVM and Deep Neural Network.  ... 
doi:10.32628/cseit2062134 fatcat:spnsm4ea45bq3kcsyevze2nxlu

A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems

Wu Wang, Fouzi Harrou, Benamar Bouyeddou, Sidi-Mohammed Senouci, Ying Sun
2021 Cluster Computing  
Presently, Supervisory Control and Data Acquisition (SCADA) systems are broadly adopted in remote monitoring large-scale production systems and modern power grids.  ...  In this paper, a stacked deep learning method is introduced to identify malicious attacks targeting SCADA systems.  ...  The current SCADA systems are generally distributed, networked, and communicated over wide area network (WAN) systems, such as public IP networks (e.g., internet) and wireless cellular networks (e.g.,  ... 
doi:10.1007/s10586-021-03426-w pmid:34629940 pmcid:PMC8490144 fatcat:wgvodkfndbc2xbb5dr2y3wtgam

A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition

Liston Matindife, Yanxia Sun, Zenghui Wang
2020 Mathematical Problems in Engineering  
In the case where we have limited data, we implement a transfer learning-based appliance classification strategy.  ...  In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and  ...  deep learning framework based on three separate deep learning disaggregation algorithms: the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense  ... 
doi:10.1155/2020/9356165 doaj:c3a0f93e1ed348e091a1a204aab76eae fatcat:x36esidfmzc63eqptcnkqyuf5i
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