A Fault Feature Extraction Method for DC–DC Converters Based on Automatic Hyperparameter-Optimized 1-D Convolution and Long Short-Term Memory Neural Networks

Jiang, Yuanyuan; Xia, Ling; Zhang, Jie

doi:10.1109/jestpe.2021.3131706

Cited by 19 publications

(1 citation statement)

References 42 publications

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“…This process has been compared with the conventional PID control of a DC-DC converter. Figure 20 shows the offline DC-DC conversion process using the neural network method, and Figure 21 shows the offline DC-DC conversion process using the ANN method [62][63][64].…”

Section: Artificial Neural Network Dc-dc Convertermentioning

confidence: 99%

A Comprehensive Study on DC-DC Converter for Equal Current Sharing and Voltage Stability in Renewable Energy Resources

Shankara,

Srikantaswamy,

Nagaraju

2024

JESA

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This paper presents a comprehensive survey on the use of artificial neural networks (ANN) for enhancing DC-DC converters in renewable energy systems, focusing on equal current sharing and voltage stability amidst the growing scarcity of electricity. The survey methodically examines literature on ANN integration with DC-DC converters, selecting studies based on their relevance to managing renewable energy efficiently, improving power distribution, and the effectiveness of ANN in addressing these challenges. The research identifies several gaps, including optimal power distribution, predictive controller limitations, and the instability of proportional-integral (PI) controls due to online training algorithm adjustments. To bridge these gaps, an innovative ANN-based control method for DC-DC converters is proposed, aimed at bolstering power generation quality, enabling flexible power distribution across microgrids, and enhancing the stability, reliability, and cost-effectiveness of renewable energy sources. Moreover, the paper discusses the correction of offline training problems, feedback error signal corrections, and integral error signals of DC-DC converters, offering new insights and solutions to overcome these technical barriers. This study underscores the converter's size and integration significance, juxtaposing traditional methods with ANN-based controls to highlight the latter's performance and efficiency advantages. Through a detailed review and proposed solutions to significant challenges in renewable energy management, this work contributes to the field's advancement by enhancing the efficiency and reliability of power systems through cutting-edge ANN-based control methods.

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Section: Artificial Neural Network Dc-dc Convertermentioning

confidence: 99%

A Comprehensive Study on DC-DC Converter for Equal Current Sharing and Voltage Stability in Renewable Energy Resources

Shankara,

Srikantaswamy,

Nagaraju

2024

JESA

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Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Kaveh

Mesgari

2022

Neural Process Lett

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The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.

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Fault Diagnosis of DCS SMPSs in Nuclear Power Plants Based on Machine Learning

Wang,

Wu,

et al. 2023

Arab J Sci Eng

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A Fault Feature Extraction Method for DC–DC Converters Based on Automatic Hyperparameter-Optimized 1-D Convolution and Long Short-Term Memory Neural Networks

Cited by 19 publications

References 42 publications

A Comprehensive Study on DC-DC Converter for Equal Current Sharing and Voltage Stability in Renewable Energy Resources

A Comprehensive Study on DC-DC Converter for Equal Current Sharing and Voltage Stability in Renewable Energy Resources

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Fault Diagnosis of DCS SMPSs in Nuclear Power Plants Based on Machine Learning

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