Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults

Moradzadeh, Arash; Teimourzadeh, Hamid; Mohammadi‐Ivatloo, Behnam; Pourhossein, Kazem

doi:10.1016/j.ijepes.2021.107563

Cited by 78 publications

(23 citation statements)

References 35 publications

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“…Furthermore, information from fault categorisation can enhance the prompt discovery of a faulty point, minimising the time necessary to remove faults and swiftly restore electricity service. As a result, many scientific investigations are being conducted to develop a robust, accurate, rapid and timely strategy for the detection and localisation of the faults on transmission lines [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

The use of artificial neural network for low latency of fault detection and localisation in transmission line

Ogar¹,

Hussain²,

Gamage³

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

The use of artificial neural network for low latency of fault detection and localisation in transmission line

Ogar¹,

Hussain²,

Gamage³

2023

Heliyon

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“…Different methods can be used to ensure the accuracy of results and their evaluation. The correlation coefficient of determination (R), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) are statistical performance metrics used in this paper to assess the performance of the proposed methods [123], [124].…”

Section: B Evaluation Of Model Performancementioning

confidence: 99%

Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis

et al. 2022

Self Cite

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“…Generally, feature extraction consumes substantial time and calculation. Down-sampling can reduce computational complexity, so a CNN is constructed by using the supervised learning algorithm [29][30][31]. Then, Gradient Descent Method (GDM) is used to correct the error; each step of the gradient needs to move forward and backward to complete so that it takes a lot of time to train.…”

Section: B Functional Modules Of the Traffic Iasmentioning

confidence: 99%

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

Han

2022

IEEE Access

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In implementing the Intelligent Traffic Monitoring System (ITMS), timely and effective access to road traffic information is an essential link. It requires an effective traffic Information Acquisition System (IAS) to collect real-time data and transmit the collected information to the background for processing. Therefore, this paper studies on-road vehicle information recognition based on Deep Learning (DL). Firstly, a framework of traffic IAS is proposed. Then, an improved MT-GooGleNet model based on Convolutional Neural Network (CNN) is proposed to locate and recognize vehicles in traffic images. Finally, the performance of the model is analyzed by simulation. The experimental results of vehicle position recognition show that the classification accuracy of Multi-Task (MT)-GooGleNet after fine-tuning is 99.5%. Compared with other models, the MT-GooGleNet model proposed is the best in vehicle position recognition, and its positioning accuracy is very high. The results of vehicle identification show that after data enhancement and pre-training, the testing set accuracy of the MT-GooGleNet model is 79.96%. The results show that the model's accuracy has been dramatically improved after processing. The research provides a reference for establishing IAS in the future INDEX TERMS deep learning; Internet of Things; signal acquisition; system design; vehicle identification and positioning

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Hybrid CNN-LSTM approaches for identification of type and locations of transmission line faults

Cited by 78 publications

References 35 publications

The use of artificial neural network for low latency of fault detection and localisation in transmission line

The use of artificial neural network for low latency of fault detection and localisation in transmission line

Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

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