Journal ArticleDOI
Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults
Arash Moradzadeh,Hamid Teimourzadeh,Behnam Mohammadi-Ivatloo,Behnam Mohammadi-Ivatloo,Kazem Pourhossein +4 more
TLDR
Evaluations demonstrate the capability of the C-LSTM followed by SVM, DT, k-NN, CNN, and LSTM in categorizing the type and location of transmission line faults.About:
This article is published in International Journal of Electrical Power & Energy Systems.The article was published on 2022-02-01. It has received 33 citations till now. The article focuses on the topics: Computer science & Fault (geology).read more
Citations
More filters
Journal ArticleDOI
Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms
TL;DR: In this article , a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer.
Journal ArticleDOI
Electric load forecasting under False Data Injection Attacks using deep learning
Arash Moradzadeh,Mostafa Mohammadpourfard,Charalambos Konstantinou,Istemihan Genc,Taesic Kim,Behnam Mohammadi-Ivatloo +5 more
TL;DR: In this article , the authors proposed a cyber-secure deep learning framework that accurately predicts electric load in power grids for a time horizon spanning from an hour to a week, which integrates Autoencoder (AE), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models.
Journal ArticleDOI
Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis
TL;DR: In this paper , the authors presented a new improved hybrid model of machine learning application for forecasting the cooling load (CL) and the heating load (HL) of residential buildings after studying and analyzing various types of CL and HL forecasting models.
Journal ArticleDOI
Optimally detecting and classifying the transmission line fault in power system using hybrid technique.
TL;DR: In this article , a hybrid system is proposed to predict and classifies the power system transmission line faults, which is the consolidation of both the truncated singular value decomposition (TSVD) and Human urbanization algorithm (HUA) based Recurrent Perceptron Neural Network (RPNN), and hence it is named as TSVD-HUARPNN technique.
Journal ArticleDOI
A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment
TL;DR: In this paper , the authors presented a novel approach to heating load demand forecasting based on Cyber-Secure Federated Deep Learning (CSFDL), which provides a global super-model for forecasting heating loads demand of different local clients without knowing their location and, most importantly, without revealing their privacy.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Review of Deep Learning Algorithms and Architectures
Ajay Shrestha,Ausif Mahmood +1 more
TL;DR: This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time, and delve into the math behind training algorithms used in recent deep networks.
Journal ArticleDOI
Fault detection and classification in transmission lines based on wavelet transform and ANN
TL;DR: In this article, the fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains, which is able to single out faults from other power quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation.
Related Papers (5)
Wavelet Entropy and Neural Network Based Fault Detection on A Non Radial Power System Network
Soumyadip Jana,Gaurab Dutta +1 more