Enhancing Resiliency Feature in Smart Grids through a Deep Learning Based Prediction Model

2020 ◽  
Vol 13 (3) ◽  
pp. 508-518
Author(s):  
Abderrazak Khediri ◽  
Mohamed Ridda Laouar ◽  
Sean B. Eom
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Smart Grids ◽  
Deep Belief Network ◽  
Power Grids ◽  
Economic Losses ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Belief Network ◽  
Proposed Model

Background: Enhancing the resiliency of electric power grids is becoming a crucial issue due to the outages that have recently occurred. One solution could be the prediction of imminent failure that is engendered by line contingency or grid disturbances. Therefore, a number of researchers have initiated investigations to generate techniques for predicting outages. However, extended blackouts can still occur due to the frailty of distribution power grids. Objective: This paper implements a proactive prediction model based on deep-belief networks to predict the imminent outages using previous historical blackouts, trigger alarms, and suggest solutions for blackouts. These actions can prevent outages, stop cascading failures and diminish the resulting economic losses. Methods: The proposed model is divided into three phases: A, B and C. The first phase (A) represents the initial segment that collects and extracts data and trains the deep belief network using the collected data. Phase B defines the Power outage threshold and determines whether the grid is in a normal state. Phase C involves detecting potential unsafe events, triggering alarms and proposing emergency action plans for restoration. Results: Different machine learning and deep learning algorithms are used in our experiments to validate our proposition, such as Random forest, Bayesian nets and others. Deep belief Networks can achieve 97.30% accuracy and 97.06% precision. Conclusion: The obtained findings demonstrate that the proposed model would be convenient for blackouts’ prediction and that the deep-belief network represents a powerful deep learning tool that can offer plausible results.

scholarly journals A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps

2020 ◽  
Vol 25 (3) ◽  
pp. 373-382
Author(s):  
He Yu ◽  
Zaike Tian ◽  
Hongru Li ◽  
Baohua Xu ◽  
Guoqing An
Keyword(s):  
Deep Learning ◽  
Graph Model ◽  
Parameter Tuning ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Deep Feature ◽  
Proposed Model ◽  
Vector Machines ◽  
Deep Feature Extraction

Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depend on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.

Sign Language Fingerspelling Recognition Using Depth Information and Deep Belief Networks

2018 ◽  
Vol 32 (06) ◽  
pp. 1850018 ◽  
Author(s):  
Yong Hu ◽  
Hai-Feng Zhao ◽  
Zhi-Gang Wang
Keyword(s):  
Sign Language ◽  
Deep Belief Network ◽  
Depth Information ◽  
Histogram Of Oriented Gradients ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Belief Network ◽  
Average Accuracy

In the sign language fingerspelling scheme, letters in the alphabet are presented by a distinctive finger shape or movement. The presented work is conducted for autokinetic translating fingerspelling signs to text. A recognition framework by using intensity and depth information is proposed and compared with some distinguished works. Histogram of Oriented Gradients (HOG) and Zernike moments are used as discriminative features due to their simplicity and good performance. A Deep Belief Network (DBN) composed of three Restricted Boltzmann Machines (RBMs) is used as a classifier. Experiments are executed on a challenging database, which consists of 120,000 pictures representing 24 alphabet letters over five different users. The proposed approach obtained higher average accuracy, outperforming all other methods. This indicates the effectiveness and the abilities of the proposed framework.

Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Structures ◽  
2021 ◽  
Vol 33 ◽  
pp. 2792-2802
Author(s):  
Zhiyuan Fang ◽  
Krishanu Roy ◽  
Jiri Mares ◽  
Chiu-Wing Sham ◽  
Boshan Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Deep Belief Network ◽  
Belief Network ◽  
Axial Capacity ◽  
Cold Formed Steel ◽  
Capacity Prediction

scholarly journals Sentiment Analysis using Deep Belief Network for User Rating Classification

2021 ◽  
Vol 10 (8) ◽  
pp. 87-91
Author(s):  
Ravi Chandra ◽  
Basavaraj Vaddatti
Keyword(s):  
Sentiment Analysis ◽  
Feature Vector ◽  
Deep Belief Network ◽  
Deep Belief Networks ◽  
User Reviews ◽  
Belief Network ◽  
Precision And Accuracy ◽  
People’S Attitudes ◽  
User Assessment ◽  
Different Levels

People’s attitudes, opinions, feelings and sentiments which are usually expressed in the written languages are studied by using a well known concept called the sentiment analysis. The emotions are expressed at various different levels like document, sentence and phrase level are studied by using the sentiment analysis approach. The sentiment analysis combined with the Deep learning methodologies achieves the greater classification in a larger dataset. The proposed approach and methods are Sentiment Analysis and deep belief networks, these are used to process the user reviews and to give rise to a possible classification for recommendations system for the user. The user assessment classification can be progressed by applying noise reduction or pre-processing to the system dataset. Further by the input nodes the system uses an exploration of user’s sentiments to build a feature vector. Finally, the data learning is achieved for the suggestions; by using deep belief network. The prototypical achieves superior precision and accuracy when compared with the LSTM and SVM algorithms.

scholarly journals Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network

2020 ◽  
Vol 10 (18) ◽  
pp. 6359 ◽  
Author(s):  
Shuangjie Liu ◽  
Jiaqi Xie ◽  
Changqing Shen ◽  
Xiaofeng Shang ◽  
Dong Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Vibration Signal ◽  
Deep Belief Network ◽  
Mechanical Equipment ◽  
Belief Network ◽  
Bearing Faults ◽  
Qualitative And Quantitative ◽  
Proposed Model ◽  
Different Types

Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults.

Deep Learning-based Framework for Smart Sustainable Cities

2019 ◽  
Vol 15 (4) ◽  
pp. 76-107
Author(s):  
Nagarathna Ravi ◽  
Vimala Rani P ◽  
Rajesh Alias Harinarayan R ◽  
Mercy Shalinie S ◽  
Karthick Seshadri ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Deep Learning ◽  
Air Pollutants ◽  
Human Life ◽  
Deep Belief Network ◽  
Motor Vehicles ◽  
Human Beings ◽  
Long Term Effects ◽  
Belief Network

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

scholarly journals Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Xu ◽  
Yan jun Fang ◽  
Dong Wang ◽  
Jia qi Liang ◽  
Kwok Leung Tsui
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Roller Bearings ◽  
Mode Decomposition

Because deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can classify the different fault types without data label. Therefore, a method based on deep belief networks (DBNs) in deep learning (DL) and fuzzy C-means (FCM) clustering algorithm for roller bearings fault diagnosis without a data label is presented in this paper. Firstly, the roller bearings vibration signals are extracted by using DBN, and then principal component analysis (PCA) is used to reduce the dimension of the vibration signal features. Secondly, the first two principal components (PCs) are selected as the input of fuzzy C-means (FCM) for roller bearings fault identification. Finally, the experimental results show that the fault diagnosis of the method presented is better than that of other combination models, such as variation mode decomposition- (VMD-) singular value decomposition- (SVD-) FCM, and ensemble empirical mode decomposition- (EEMD-) fuzzy entropy- (FE-) PCA-FCM.

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