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

Ogar, Vincent Nsed; Hussain, Sajjad; Gamage, Kelum A. A.

doi:10.1016/j.heliyon.2023.e13376

Cited by 12 publications

(2 citation statements)

References 47 publications

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“…So, minimizing their outage periods is essential to ensure power system reliability and continuity. Thus, fast, and accurate fault detection, identification, and location should be considered as vital targets for power system operators [ [8] , [9] , [10] , [11] , [12] ]. Nowadays, several algorithms of fault location based on the PMU techniques for transmission systems have been conducted [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

Alghassab,

Hatata,

Sokrana

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

Alghassab,

Hatata,

Sokrana

et al. 2024

Heliyon

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“…Previous research has explored the use of ANNs for various applications in electronics and mechatronics, including voltage distribution prediction [1,2], sound noise classification [3,4], and parameter estimation in Industry 4.0 scenarios [5]. ANNs have also been employed for power prediction in renewable energy sources [6], power diagnostics [7], and fault detection systems [8]. However, the specific application of ANNs to predict noisy signals in amplification circuits remains an area that warrants further investigation.…”

Section: Introductionmentioning

confidence: 99%

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

Massaro

2024

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In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.

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Scaled Conjugate Gradient-Based Intelligent Microgrid Fault Analysis System

Chaudhari,

Dhamale,

Mahajan

et al. 2024

Engineering Optimization: Methods and Applications

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

Cited by 12 publications

References 47 publications

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

Scaled Conjugate Gradient-Based Intelligent Microgrid Fault Analysis System

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