3d Computation of the Overhead Power Lines Electric Field

Modrić, Tonći; Vujević, Slavko; Paladin, Ivan

doi:10.2528/pierm16110309

Cited by 10 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Complex image method, is used to take into account the impact of the ground surface. This further implies that the influence of the soil surface can be taken into account by reflecting the point fictitious charge below the soil surface at a depth equal to the product of the height of the conductor above the surface and the complex coefficient Γ [18], [36]. Complex coefficient Γ is defined by the following equation:…”

Section: Electric Field Intensity and Magnetic Induction Calculationmentioning

confidence: 99%

Electric and Magnetic Field Estimation Under Overhead Transmission Lines Using Artificial Neural Networks

2021

View full text Add to dashboard Cite

In this paper, a novel method for electric field intensity and magnetic induction 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 induction. 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 induction 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 induction 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 induction 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 induction estimation results for different overhead transmission line configurations. INDEX TERMSArtificial neural networks (ANN), Biot-Savart (BS) law based method, Charge simulation method (CSM), Electric field intensity, Magnetic induction, Scaled Conjugated Gradient (SCG)

show abstract

Section: Electric Field Intensity and Magnetic Induction Calculationmentioning

confidence: 99%

Electric and Magnetic Field Estimation Under Overhead Transmission Lines Using Artificial Neural Networks

2021

View full text Add to dashboard Cite

show abstract

“…The electric field created by a catenary single conductor line on the air above ground is calculated by the superposition of the electric field created by the real catenary's line C 1 r′ plus the electric field created by the image of the line C 1 r′ , as it is depicted in Fig. 4 [20,[32][33][34][35][36]…”

Section: D Integration Techniquementioning

confidence: 99%

“…The electric field created by a catenary single conductor line on the air above ground is calculated by the superposition of the electric field created by the real catenary's line

C_{1} ()(, r^{normal′})

plus the electric field created by the image of the line

C_{1} ()(, r^{normal′})

, as it is depicted in Fig. 4 [20, 32–36 ]

E ()(, r) = \frac{q ()(, r^{normal′})}{4 \cdot π \cdot ε_{0}} \cdot [\int_{C_{1} (r^{'})} \frac{{normalR}_{1}}{M_{1}^{3}} \cdot normald {l^{'}}_{1} - \int_{C_{2} (r^{'})} \frac{{normalR}_{2}}{M_{2}^{3}} \cdot normald {l^{'}}_{2}]

With

M = (||, R) = (||, r - r^{normal′})

Where

r^{normal′}

is the position of a point on the curve, and r is the position of the point in space where the field is to be calculated. So there will be two vectors that define the position of electric field calculation, which are given by the following equations:

\begin{matrix} right leftthickmathspace.5em \\ M_{1} & = |R_{1}|, R_{1} = (x - x^{'}) bold-italicX + (y - y^{'}) bold-italicY + (...</p> \end{matrix}

…”

Section: 3d Integration Techniquementioning

confidence: 99%

3D Modelling and simulation analysis of electric field under HV overhead line using improved optimisation method

Djekidel

Bessedik

Akef

2020

IET sci. meas. technol.

View full text Add to dashboard Cite

This study proposes a three‐dimensional (3D) quasi‐static modelling of the electric field produced by high voltage (HV) overhead power lines using charge simulation method combined with intelligent optimisation algorithms, such as particle swarm optimisation, genetic algorithm and grey Wolf optimiser (GWO), to find the optimal values of parameters for an accurate calculation. Results show that the GWO works better than other algorithms. Several parameters affecting the electric field have been studied; it is observed that taking into account the effect of the exact catenary curve of the power line conductors is much more interesting particularly at the mid‐span level where the electric field becomes very significant. According to these values, the limits set by the International Commission on Non‐Ionising Radiation Protection guidelines for electric field strength are met for occupational and public exposure. The simulation results are compared with those obtained by the 3D Integration method, a fairly good agreement is found. To confirm the performance and effectiveness of the proposed method, the results obtained are also verified with measurement values available in the literature, a good similarity is achieved.

show abstract

“…These fields are quasistatic and oscillate at the operating frequency of the power grid, either 50 or 60 Hz depending on the part of the world. They have been measured experimentally or modeled in software by many authors (Adelman and Hull, 2015; Lambdin, 1978; Modric et al, 2015, 2017; Olsen and Wong, 1992; Tell et al, 1977; Wigdor, 1980). High-quality electric- and magnetic-field sensors are readily available, either in research form (Heintzelman, 2015) or as commercial products (NARDA Safety Test Solutions, 2020; Quasar Federal Systems, 2017).…”

Section: Introductionmentioning

confidence: 99%

Highly parallel boundary element method for solving extremely large, wide-area power-line models

Adelman

2020

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

The electric and magnetic fields around power lines carry an immense amount of information about the power grid and can be used to improve stability, balance loads, conserve power, and reduce outages. To study this, an extremely large model of transmission lines over a 70-km2 tract of land near Washington, DC, has been built. The terrain was modeled accurately using 1-m-resolution LIDAR data. The 140-million-element power-line model was solved using the boundary element method, and the solvers were parallelized across DEVCOM Army Research Laboratory’s Centennial supercomputer using a modified version of the domain decomposition method. The code on each node was accelerated using the fast multipole method and, when available, GPUs. Additionally, larger test models were used to characterize the scalability of the code. The largest test model had 10,010,944,000 elements, and was solved on 1,024 nodes in 4.3 hours.

show abstract

3d Computation of the Overhead Power Lines Electric Field

Cited by 10 publications

References 21 publications

Electric and Magnetic Field Estimation Under Overhead Transmission Lines Using Artificial Neural Networks

Electric and Magnetic Field Estimation Under Overhead Transmission Lines Using Artificial Neural Networks

3D Modelling and simulation analysis of electric field under HV overhead line using improved optimisation method

Highly parallel boundary element method for solving extremely large, wide-area power-line models

Contact Info

Product

Resources

About