Deep learning based circuit topology estimation and fault classification in distribution systems

2021 ◽  
Author(s):  
Aswathy Rajendra Kurup ◽  
Manel Martinez-Ramon ◽  
Adam Summers ◽  
Ali Bidram ◽  
Matthew J. Reno
Keyword(s):  
Deep Learning ◽  
Distribution Systems ◽  
Fault Classification ◽  
Circuit Topology ◽  
Topology Estimation

Deep Learning-based Surrogate Models for Water Distribution Systems

2020 ◽  
Author(s):  
Riccardo Taormina ◽  
Mohammad Ashrafi ◽  
Andres Murillo ◽  
Stefano Galelli
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Surrogate Models ◽  
Water Network ◽  
Control Logic ◽  
Simulation Based ◽  
Simulation Based Optimization

<p><span>Simulation-based optimization is widely used for designing and managing water distribution networks. The process involves the use of accurate computational models, such as EPANET, which represent the physical processes taking place in the water network and reproduce the control logic governing its operations. Unfortunately, running such models requires expensive computations, which, in turn, may hinder the application of simulation-based optimization to large and complex problems. This issue can be overcome by resorting to surrogate models, that is, simplified data-driven models that accurately mimic the behaviours of physical-based models at a fraction of the computational costs. In this work, we explore the potential of Deep Learning Neural Networks (DLNN) for building surrogate models for water distribution systems. Different DLNN architectures, including feed-forward and recurrent neural networks, are trained and validated on datasets generated through EPANET simulations. The DLNN models are then used in lieu of the original EPANET model to speed-up the evaluation of the objective function employed in a simulation-based optimization problem. The effectiveness of the proposed technique is assessed on a realistic case-study involving cyber-attacks on a water network. In particular, the DLNN surrogate models are employed by an evolutionary optimization algorithm that schedules the operations of hydraulic actuators in order to best respond to the attacks and facilitate the recovery process.</span></p>

scholarly journals Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ming-Yuan Cho ◽  
Thi Thom Hoang
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Binary Sequence ◽  
Particle Swarm ◽  
Time Domain Reflectometry ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Swarm Optimization

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

scholarly journals Fault classification on transmission line using LSTM network

2020 ◽  
Vol 20 (1) ◽  
pp. 231
Author(s):  
Abdul Malek Saidina Omar ◽  
Muhammad Khusairi Osman ◽  
Mohammad Nizam Ibrahim ◽  
Zakaria Hussain ◽  
Ahmad Farid Abidin
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Classification Accuracy ◽  
Model Simulation ◽  
Fault Classification ◽  
Fault Current ◽  
Transmission Line Model ◽  
Line Model ◽  
Lstm Network ◽  
Types Of Faults

Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called Long Short-Term Memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.

scholarly journals A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5160 ◽  
Author(s):  
Seokju Ham ◽  
Seok-Youn Han ◽  
Seokgoo Kim ◽  
Hyung Jun Park ◽  
Kee-Jun Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Traditional Approach ◽  
Fault Classification ◽  
Learning Approach ◽  
Health State ◽  
Features Selection ◽  
Learning Approaches ◽  
Current Signal ◽  
K Nearest Neighbor

A fault diagnosis of a train door system is carried out using the motor current signal that operates the door. A test rig is prepared, in which various fault modes are examined by applying extreme conditions, as well as the natural and artificial wears of critical components. Two approaches are undertaken toward the fault classification for comparative purposes: one is the traditional feature-based method that requires several steps for the processing features such as signal segmentation, the extraction of time-domain features, selection by Fisher’s discrimination, and K-nearest neighbor. The other is the deep learning approach by employing the convolutional neural network (CNN) to skip the hand-crafted features extraction process. In the traditional approach, good accuracy is found only after the current signal is segmented into the three velocity regimes, which enhances the discrimination capability. In the CNN, superior accuracy is obtained even by the original raw signal, which is more convenient in terms of implementation. However, in view of practical applications, the traditional approach is more useful in that the features processing can be easily applied to assess the health state of each fault and monitor the progression over time in the real operation, which is not enabled by the deep learning approach.

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