A Deep Learning Technique for Detecting High Impedance Faults in Medium Voltage Distribution Networks

Lavanya, S.; Prabakaran, S.; Kumar, N. Ashok

doi:10.48084/etasr.5288

Cited by 5 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DCNN is used to process voltage data in the power system. By learning the pattern of historical voltage fluctuations, the trend of future voltage fluctuations can be predicted (Lavanya et al, 2022). This is crucial for power system stability assessment.…”

Section: Dcnn Modelmentioning

confidence: 99%

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Yang,

Min

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

The booming development of distributed power sources in power systems has drawn attention to the carrying capacity and stability of the power grid, becoming a key challenge for the power industry. This study aims to develop a comprehensive deep learning model by combining deep recurrent double Q network (DR-DQN) and deep convolutional neural network (DCNN), and use meta-learning to optimize the model as a whole to simultaneously optimize the power grid. Distributed power supply carrying capacity and predicting the voltage fluctuations of the grid. The comprehensive model is designed to consider distributed power capacity optimization and voltage fluctuation prediction holistically. Through the DR-DQN model, the maximum distributed power capacity is determined under different grid conditions and the distributed power configuration of the grid is optimized. At the same time, the DCNN model is used to analyze the power grid time series data and predict the voltage fluctuation of the power grid. The results are presented in graph form, showing trends in maximum capacity and voltage fluctuations under different grid conditions. Experimental results show that the overall model achieves satisfactory results in distributed power capacity optimization and voltage fluctuation prediction. Performance evaluation and comparison highlight the comprehensive model’s excellent performance in terms of prediction accuracy and computational efficiency, providing new possibilities for efficient management and reliable operation of power systems. The successful development of the model provides practical and reliable solutions for the future development of power systems.

show abstract

Section: Dcnn Modelmentioning

confidence: 99%

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Yang,

Min

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Most of the time, this training algorithm is significantly faster than the typical steepest descent algorithm. In this algorithm, each variable was computed and adjusted according to equation (17).…”

Section: Resilient Backpropagation [40]mentioning

confidence: 99%

“…An NN can be trained and used for function approximation to replace a conventional controller in a process control system [14]. The relevant recent works in the field of process control systems using neural networks (NN) and deep learning (DL) techniques include [13,[16][17][18][19][20][21], deep reinforcement learning-based controllers [11,18,[22][23][24][25] transfer learning based controller [26]. Although plenty of research has been done to develop deep learning-based controllers using the Deep Learning Toolbox in MATLAB, no research has been done to compare the algorithms' performance and especially in the field of the process control system to the best of the knowledge of the authors.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of various training algorithms of deep learning based controller

2023

View full text Add to dashboard Cite

Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simulated on MATLAB R2021b with license number 1075356. Training data were generated using two benchmark problems of the process control system. The performance, gradient, training error, validation error, testing error, and regression of the different training algorithms were obtained and analysed. The data shows that the Levenberg-Marquardt (LM) algorithm produced the best validation performance with a value of 2.669*10−14 at 2000 epochs, while ‘traingd’ and ‘traingdm’ algorithms did not improve beyond their initial values. The LM algorithm tends to produce better results than other algorithms. These results indicate that the LM backpropagation best suits these types of benchmark problems. The results also suggest that the choice of training algorithm can significantly impact the performance of a neural network.

show abstract

CNN-Based Detection of Welding Crack Defects in Radiographic Non-Destructive Testing

AlShareef,

Alnowaimi,

Siddig

2024

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

A Deep Learning Technique for Detecting High Impedance Faults in Medium Voltage Distribution Networks

Cited by 5 publications

References 28 publications

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

Performance analysis of various training algorithms of deep learning based controller

CNN-Based Detection of Welding Crack Defects in Radiographic Non-Destructive Testing

Contact Info

Product

Resources

About