A hybrid deep learning and ensemble learning mechanism for damaged power line detection in smart grids

Tian, Yangyang; Wang, Qi; Guo, Zhimin; Zhao, Huitong; Khan, Sulaiman; Mao, Wandeng; Yasir, Muhammad; Zhao, Jian

doi:10.1007/s00500-021-06482-x

Cited by 14 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Comprehensive assessments have been performed to specify the parameter sensitivity over the presented structure. Tian et al [23] offered a new mixed deep learning instrument to determine the damages in the communication cables. This structure included CNN and SVM that CNN is used for the category of damaged power cables pictures, and SVM is for the recognition and estimating the intensity of damaged power cables using statistical information.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

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

View full text Add to dashboard Cite

show abstract

“…To distinguish between the characteristics of trustworthy and dishonest customers, deep siamese network DSN and CNN were combined [116]. SVM is used to identify damaged power lines and determine their severity using statistical data, and CNN is used to classify photos of damaged power lines [117]. The class imbalance problem is resolved by the generative adversarial network with time least squares.…”

Section: References Yearmentioning

confidence: 99%

A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids

Rituraj

Ecker²

2022

SSRN Journal

View full text Add to dashboard Cite

“…Around the world, 80% of the population uses electricity as a primary source of energy. Both government and non-government organizations contribute their full efforts to ensure non-interrupted power to their customers but the over-population, exponential growth in technologies, modernday societies, and high reliance on electricity have baffled the researcher and power suppliers, in monitoring, managing, and analyzing the power grids to ensure non-stop power flow (Tian et al, 2021). The unavailability of sufficient incentives to upgrade the transmission infrastructure for example, in the US and Europe power cuts have become more frequent (Bruch et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…The power outage is divided into two different types of short-term power failure and long-term power failure. In the first failure form, the electricity mostly lasts part of an hour or for a few hours, but the long-term blackout ensues in enduring the electricity for days or even weeks (Tian et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Efficient Power Grid Management Using Sliced-Based Mechanism for Systematic Allocation of Solar Energy Resources: A Conceptual Framework

Khan

Nazir

2022

Front. Energy Res.

Self Cite

View full text Add to dashboard Cite

The over-population and the limited energy resources have puzzled the government and private organizations to think about the providence of non-stop energy resources to the industries, hospitals, smart homes, and shopping malls to ensure normal routine activities. To accept this challenge, some researchers put their efforts into generating energy from renewable energy resources (solar, fossil fuels, wind turbines, geothermal energy, and many others) to fulfill the needs of life. While some researchers worked on the efficient utilization of the available energy resources to save the energy for future generations. Inspiring from the second approach, this research work has proposed a systematic allocation of energy resources using the slice-based mechanism in a smart grid environment. This research framework using a hybrid model comprises long short-term memory (LSTM), and a support vector machine (SVM), where the LSTM classifies different energy requests (for allocation of energy resources) while the SVM accomplishes the statistical analysis (to estimate the number of solar energy resources allocated and for a specific interval of time). This need-based allocation of energy resources will not only assist in saving energy resources for future use, but will also improve the life of the power grid and other electric appliances (due to over-usage and burning). The applicability of this model is validated by testing it on a real-time scenario like slice failure conditions, slice overflow conditions, a huge number of requests, and alternate slice allocation conditions. Furthermore, the incoming request classification is also validated based on its accurate identification using a confusion matrix, varying number of hidden layers, accuracy, and time consumption. The outperformance of the selected based on these scenarios and validation metrics reflects the applicability of this framework. Moreover, this framework will assist in reducing overbilling charges and energy savage for future generations due to its need-based allocation of energy resources assignment capabilities.

show abstract

A hybrid deep learning and ensemble learning mechanism for damaged power line detection in smart grids

Cited by 14 publications

References 22 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

A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids

Efficient Power Grid Management Using Sliced-Based Mechanism for Systematic Allocation of Solar Energy Resources: A Conceptual Framework

Contact Info

Product

Resources

About