Protection Scheme using Wavelet-Alienation-Neural Technique for UPFC Compensated Transmission Line

Rathore, Bhuvnesh; Mahela, Om Prakash; Khan, B. Zorina; Padmanaban, Sanjeevikumar

doi:10.1109/access.2021.3052315

Cited by 33 publications

(8 citation statements)

References 25 publications

(25 reference statements)

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“…This study provides an integrated strategy for supplemental damping controlling of UPFCs in PS to alleviate low-frequency fluctuations [90]. For the fault assessment of a UPFC compensated transmission system, a wavelet-alienation-neural (WAN) methodology has been devised [91]. A novel set of timefrequency characteristics is suggested in this research for fault category recognition, fault loop condition monitoring, and fault zone detecting units in a compensating transmission line with UPFC [92].…”

Section: D-facts Planningmentioning

confidence: 99%

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

Mousaei,

Gheisarnejad,

Khooban

2023

Journal of Energy Storage

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Section: D-facts Planningmentioning

confidence: 99%

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

Mousaei,

Gheisarnejad,

Khooban

2023

Journal of Energy Storage

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“…The architecture of the ANN comprises of • Output layer: Usually it has one neuron, and its output ranges from 0 to 1, that is, greater than 0 and less than 1. But multiple outputs can be present [15,[21][22][23][24][25][26][27][28][29][30].…”

Section: Network Structurementioning

confidence: 99%

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Chane¹,

Gebru²,

Khan³

2021

J. Infor. Electr. Electron. Eng.

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This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

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“…Faults can lead to the disastrous collapse of the entire electric power system if they are not discovered and isolated precisely in a very-short time [1]. In recent years, several TL protective techniques and algorithms have been adopted, which are based on trustworthy scientific fault identification theories such as [2][3][4][5]. However, such approaches are seen to be insufficient, particularly the extended time required to detect and isolate faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection on Power Transmission Line Based on Wavelet Transform and Scalogram Image Analysis

Altaie,

Majeed,

Abderrahim

et al. 2023

Energies

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Given the massive increase in demand for electrical energy, particularly owing to global climate change and population expansion, as well as the development of complicated electrical systems due to the urgent need for a sophisticated component to enhance power delivery, it becomes important to adopt a smart and contemporary approach that is also appropriate for the aim of protecting transmission lines (TLs) and ensuring the continuous delivery of electric power to customers. Consequently, a unique and highly reliable approach for identifying faults in TLs is presented in this work, which employs Wavelet Transform and is evaluated using Matlab simulation. Wavelets of various kinds were utilized to demonstrate their dependability. Furthermore, utilizing this approach has shown itself to be highly successful and has yielded spectacular results even when it is used on a complicated electrical network. Moreover, many types of faults were presented and afterward evaluated and verified for the network in various settings, which also demonstrated their potential to recognize faults within a relatively short space of time. This innovation will alter the idea of fault detection by providing a complete and integrated model for detecting faults in a TL, and it may be regarded as a revolution in the renewal of core principles in TL protection.

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Protection Scheme using Wavelet-Alienation-Neural Technique for UPFC Compensated Transmission Line

Cited by 33 publications

References 25 publications

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: A technological review

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Fault Detection on Power Transmission Line Based on Wavelet Transform and Scalogram Image Analysis

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