Predictions of the Electric Field Emissions around Power Transmission Lines by Using Artificial Neural Network Methods (original) (raw)
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Proceedings of the 2nd World Congress on Electrical Engineering and Computer Systems and Science, 2016
In this study, Artificial Neural Network (ANN) Algorithms are used to estimate the electric field around the power transmission lines as an alternative approach. Firstly, electric field levels around the high voltage power transmission lines are measured, and then analytically calculated. Moreover, the field levels around these power lines have been predicted by using multilayer perceptron artificial neural network, radial basis function, and generalized regression neural network models. Electric field levels occurrence around the power transmission lines have been predicted with ANN models with high accuracy.
Electric Field Estimation around an Overhead Power Transmission Line using Neural Network Model
2007
This paper presents the use of artificial neural ne tworks (ANN) to estimate electric fields around an overhead power transmission line. Although there ex ist many efficient numerical methods, e.g. finite difference method (FDM), finite element method (FEM), boundary element method (BEM), etc, to estimate electric field distribution caused by live conducto rs, it typically consumes substantial execution tim e
IEEE Access
In this paper, a novel method for electric field intensity and magnetic flux density estimation in the vicinity of the high voltage overhead transmission lines is proposed. The proposed method is based on two fully connected feed-forward neural networks to independently estimate electric field intensity and magnetic flux density. The artificial neural networks are trained using the scaled conjugate gradient algorithm. Training datasets corresponds to different overhead transmission line configurations that are generated using an algorithm that is especially developed for this purpose. The target values for the electric field intensity and magnetic flux density datasets are calculated using the charge simulation method and Biot-Savart law based method, respectively. This data is generated for fixed applied voltage and current intensity values. In instances when the applied voltage and current intensity values differ from those used in the artificial neural network training, the electric field intensity and magnetic flux density results are appropriately scaled. In order to verify the validity of the proposed method, a comparative analysis of the proposed method with the charge simulation method for electric field intensity calculation and Biot-Savart law-based method for magnetic flux density calculation is presented. Furthermore, the results of the proposed method are compared to measurement results obtained in the vicinity of two 400 kV transmission lines. The performance analysis results showed that proposed method can produce accurate electric field intensity and magnetic flux density estimation results for different overhead transmission line configurations. INDEX TERMS Artificial neural networks (ANN), Biot-Savart (BS) law based method, charge simulation method (CSM), electric field intensity, magnetic flux density, scaled conjugated gradient (SCG).
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2017
In recent years, increasing energy demand has resulted in more loading in power transmission lines and correspondingly more electric and magnetic field occurrence. In this paper, electric and magnetic field levels around high voltage power transmission lines are measured and then calculated analytically by simulating the realistic case. Moreover, the field levels around these power lines have been predicted using multilayer perceptron artificial neural network (ANN) and generalized regression neural network models. Typical 154 kV power transmission line facilities of Turkey have been studied. Electric and magnetic field levels in the proximity of power transmission lines have been predicted with ANN models with high accuracies. Particularly, the MLPNN algorithm predicts the electric and magnetic field intensities with very high precision. The aim of this research is to create a reference for researchers focused on topics of occupational and general public exposures and also biological effects of electric and magnetic fields emitted from low-frequency power transmission lines.
Estimation of Magnetic Field Strength near Substation Using Artificial Neural Network
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Estimating Base Station-Based Indoor and Outdoor Electric Field Levels by Artificial Neural Networks
European Journal of Technic, 2019
The measurement of the electric field and the magnetic field is significant in order to determine electromagnetic pollution level compared to standards. In Turkey, electric field limit value, which is emitted by base station is 13.5 V/m for one mobile communication operator. In addition, according to the regulation of medical devices, the limit of electric field value inside the hospital, where the medical devices are located, is 3 V/m. In this study, the measurement and evaluation of electromagnetic pollution inside and outside the Bursa Uludag University hospital building are performed and its compliance with national and international standards is examined. Moreover, the distribution of electric field in the environment is estimated by the artificial neural network and fuzzy logic methods considering the measurement results. The measured electric field values, estimated electric field values, and national standard values are compared.
Electric Power Systems Research, 2007
Feed-forward (FF) artificial neural networks (ANN) and radial basis function (RBF) ANN methods were addressed for evaluating the lightning performance of high voltage transmission lines. Several structures, learning algorithms and transfer functions were tested in order to produce a model with the best generalizing ability. Actual input and output data, collected from operating Hellenic high voltage transmission lines, as well as simulated output data were used in the training, validation and testing process. The aims of the paper are to describe in detail and compare the proposed FF and RBF ANN models, to state their advantages and disadvantages and to present results obtained by their application on operating Hellenic transmission lines of 150 kV and 400 kV. The ANN results are also compared with results obtained using conventional methods and real records of outage rate showing a quite satisfactory agreement. The proposed ANN methods can be used by electric power utilities as useful tools for the design of electric power systems, alternative to the conventional analytical methods.
The reliability of the power system mainly depends on the environmental and weather conditions which cause flashover on polluted insulators leading to system outages. It is generally recognized that the main causes leading to the contamination of insulators are marine pollution-found in the immediate neighborhood of the coastal regions and solid pollution-found in the dense industrial areas. This research is directed towards the study of contamination of insulator under marine pollution. The effects of various meteorological factors on the pollution severity have been investigated thoroughly. In the present paper an attempt has been made to estimate the pollution severity under various weather conditions using Artificial Neural Network. A new approach using ANN as a function estimator has been developed and used to model accurately the relationship between ESDD with temperature T, humidity H, pressure P, rainfall R and wind velocity WV.
2009
In this work an attempt has been made to estimate the pollution flashover voltage under various meteorological factors using radial basis function (RBF) neural networks. Orthogonal least squares (OLS) learning method is used in order to improve the lines performance against the pollution flashover of the post insulators. The technique of RBF neural network is employed to model the relationship between pollution flashover voltage and the line parameters: diameter of shed, distance of leakage, pressure at different altitudes and salt deposit densities ESDD. The results show that a well trained RBF neural network achieved a better modelling accuracy and performance.
Protecting overhead high voltage transmission lines from lightning strokes is one of the most important tasks to safeguard electric power systems. In order to achieve this effectively, the lightning performance of the lines has to be evaluated accurately. In the recent years Artificial Neural Networks (ANN) have attracted much attention and many interesting ANN applications have been reported in power system areas, due to their computational speed, the ability to handle complex non-linear functions, robustness and great efficiency, even in cases where full information for the studied problem is absent. In this paper, several ANN were addressed to identify the lightning performance of high voltage transmission lines. Each network has been constructed using different structures, learning algorithms and transfer functions in order best generalizing ability to be achieved. Actual input and output data, collected from Hellenic high voltage transmission lines, were used in the training, validation and testing process. A comparison among the developed neural networks was performed in order the most suitable network to be selected. Finally the selected ANN was applied on Hellenic transmission lines and the obtained results were compared with conventional methods' results and real records of outage rate.