Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm

Zhang, Liang; Gao, Tian; Cai, Guowei; Koh, Leong Hai

doi:10.1016/j.est.2022.104092

Cited by 120 publications

(50 citation statements)

References 13 publications

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“…According to the prediction evaluation, the proposed CNN-RF method is compared to some newly released mechanisms to detect damaged power lines with UAV and the IoT technologies including Convolutional Neural Network (CNN) [20], CNN [37] and Support Vector Machine (CNN-SVM) [23], Focal Phi Loss (FPL) [21,38], and convolutional features and structured constraints (CFSC) [25,39].…”

Section: Resultsmentioning

confidence: 99%

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

Yuqing

2022

Wireless Communications and Mobile Computing

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Today, energy management based on the digitalization of smart grids by the Internet of Things (IoT) is an emerging paradigm for power line systems. There are several environmental hazards to break down high-voltage power cables such as lightning, severe voltage fluctuations, and incorrect design of electric field distribution. So, identifying faulty high-voltage power lines is one of the most emerging challenges in smart grids to avoid disruption of the power distribution networks. This paper presents a new hybrid Convolutional Neural Network and Relief-F (CNN-RF) algorithm for an energy-aware collaborative learning approach to detect power line systems in smart grids. This hybrid approach ensures the stability and reliability of the defective power line system and improves the energy efficiency of the smart grids. This approach can detect the defective power line recognition using damaged power line images concerning automatic monitoring using Unmanned Aerial Vehicle (UAV) control system and IoT communications. By applying UAV control system and IoT communications on gathering damaged power line images, human faults and environmental hazards for extra data transmission are avoided. Experimental results show that the proposed CNN-RF model represents a high accuracy rate of 92.2% for recognizing damaged power lines. Also, the precision of damaged line detection ratio is higher than other prediction methods by the rate of 92.5%. Finally, the performance of the damaged line prediction approach in the CNN-RF method has a daily minimum cost in the IoT-based smart grids.

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Section: Resultsmentioning

confidence: 99%

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

Yuqing

2022

Wireless Communications and Mobile Computing

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“…Using the modified Lamport Merkle Digital Signature method, Mehbodniya et al [25] developed a framework for generating and verifying digital signatures. An improved gray wolf optimization (IGWO) algorithm was used by Zhang et al [26] to develop a charging safety early-warning model for electric vehicles (EV). It is a pioneering attempt to distinguish transferable or untransferable knowledge across domains with the Knowledge Aggregation-induced Transferability Perception (KATP) developed by Dong et al [27].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

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“…In AOMPC, parameters of AMPC including sampling time, prediction horizon, control horizon, weighting factors, and scaling factors can be optimized through a metaheuristic algorithm, namely an improved gray wolf optimization algorithm. For finding more detail about this optimization algorithm, it can be referred to [42,43].…”

Section: B Adaptive Mpcmentioning

confidence: 99%

A Novel Control Strategy Based on an Adaptive Fuzzy Model Predictive Control for Frequency Regulation of a Microgrid With Uncertain and Time-Varying Parameters

et al. 2022

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The intermittent and uncertain behavior of renewable energy sources; moreover, the increase in penetration of these resources causes some drawbacks in power grids, particularly in low-inertia microgrids. In addition to supply-side challenges, changing load rate has a considerable negative effect on small-size microgrids. Therefore, according to these challenges, the lack of balance between generation and demand is a vital issue in microgrids. One way to face these challenges would be using a suitable control strategy. In this research, an adaptive fuzzy model predictive control has been proposed as a novel control approach and has been compared with conventional controllers such as an optimal PI, and an adaptive optimal model predictive control. It is important to mention that, various types of load changing and time-varying parameters have been considered for a new model of small size microgrids in this study. The comparison and simulation results obviously indicate the effectiveness of the proposed control strategy.

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Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm

Cited by 120 publications

References 13 publications

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

A Novel Control Strategy Based on an Adaptive Fuzzy Model Predictive Control for Frequency Regulation of a Microgrid With Uncertain and Time-Varying Parameters

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