Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines

Ekonomou, Lambros; Gonos, Ioannis F.; Iracleous, D.P.; Stathopulos, I.A.

doi:10.1016/j.epsr.2006.01.005

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…Results showed that the best possible configuration for the system is a [8,5,1] configuration with logsig, logsig, purelin as the transfer functions trained using Levenberg Marquardt learning technique. The 8 and 5 represent the number of neurons at the first and second hidden layers respectively; while the 1 represents the single output.…”

Section: B4 Testing Processmentioning

confidence: 99%

“…Various researches have been conducted using ANN due to its computational speed, robustness and its ability to handle complex non-linear functions. Among them were lightning performance evaluation [5], lightning location system [6] and lightning models evaluation [7]. A pilot study [8] to predict lightning using ANN was conducted in 1991 by the National Aeronautics and Space Administration of the United States (NASA).…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Based Technique for Lightning Prediction

Johari¹,

Rahman

Musirin

2007

2007 5th Student Conference on Research and Development

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Malaysia has high lightning and thunderstorm occurrences throughout the year. A vast amount of its data have been recorded which allows various lightning-related studies to be conducted. This paper presents the application of artificial neural network (ANN) in predicting the occurrence of lightning events based on historical lightning and meteorological data. ANN, which was inspired by the way biological nervous systems process information, is utilized in this study due to its strong pattern recognition capabilities; implemented through learning patterns and relationships in data. A two layer back-propagation neural network has been developed to predict the occurrence of lightning at least four hours prior to its arrival. Several network structures, training algorithms and activation functions have been rigorously tested in order to obtain the most suitable network with high accuracy and convergence capability, while the perfection of the developed network was conducted through postprocessing, indicated by the closeness of correlation coefficient to unity. The computation burden experienced in this study in achieving the converged solution has been alleviated by the introduction of indicator module to the original features of the training and testing patterns.

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Section: B4 Testing Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Technique for Lightning Prediction

Johari¹,

Rahman

Musirin

2007

2007 5th Student Conference on Research and Development

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“…Although the impact of lightning overvoltages to the OHLs has been considered since the first AC transmission lines were built, it has also been frequently discussed in recently published scientific papers [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…The lightning performance of an OHL can be evaluated by using simple empirical formulae [4,5], specialized software [2,6] or by direct registration of OHL trip outs caused by lightning [3].…”

Section: Introductionmentioning

confidence: 99%

Lightning protection of overhead transmission lines using external ground wires

Banjanin

Savic

Stojković

2015

Electric Power Systems Research

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“…Nowadays ANNs give solutions with accurate results for almost every power system problem ranging from load forecasting [6] and power system transient stability [7], to the prediction of the magnetic performance of strip-wound magnetic cores [8] and the evaluation of the lightning performance of high-voltage transmission lines [9], because of their computational speed, ability to handle complex non-linear functions, robustness and great efficiency even in cases where full information of the studied problem is absent.…”

Section: Introductionmentioning

confidence: 99%

Effective insulator maintenance scheduling using artificial neural networks

Karamousantas

Chatzarakis

Oikonomou

et al. 2010

IET Gener. Transm. Distrib.

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One of the most frequent causes of failure of overhead high-and medium-voltage transmission and distribution lines is contamination of the insulators with diverse substances such as saline and industrial substances. The contamination mechanically degrades the insulators and affects the electrical characteristics of the insulating material, leading to flashovers. Periodic maintenance of insulators can reduce or even prevent the outages caused by contamination. The maintenance scheduling is planned based either on measurements, which are quite expensive and time consuming processes or on experience, a definitely inaccurate process. The current work presents a new approach for the assessment of contamination of insulators on the basis of artificial intelligence and, more specifically, artificial neural networks (ANNs). An ANN model is defined and when applied on operating voltage insulators it presented results similar to experimental results. The proposed approach can be useful in the work of electrical maintenance engineers, reducing the time and cost of insulator maintenance.

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Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines

Cited by 13 publications

References 26 publications

Artificial Neural Network Based Technique for Lightning Prediction

Artificial Neural Network Based Technique for Lightning Prediction

Lightning protection of overhead transmission lines using external ground wires

Effective insulator maintenance scheduling using artificial neural networks

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