scholarly journals Optimal placement of surge arresters for transmission lines lightning performance improvement

2022 ◽  
Vol 202 ◽  
pp. 107583
Author(s):  
Walace S. Castro ◽  
Ivan J.S. Lopes ◽  
Silvio L.V. Missé ◽  
João A. Vasconcelos
Keyword(s):  
Performance Improvement ◽  
Transmission Lines ◽  
Optimal Placement ◽  
Surge Arresters

scholarly journals A Methodology for Optimal Design of Transmission Lines to Protection against Lightning Surges in Presence of Arresters

2020 ◽  
Vol 9 (1) ◽  
pp. 105-110
Author(s):  
R. Shariatinasab ◽  
R. Azimi
Keyword(s):  
Transmission Lines ◽  
Geometric Model ◽  
Design Parameters ◽  
Target Number ◽  
Planning Stage ◽  
Shielding Failure ◽  
Surge Arresters ◽  
The Monte Carlo Method ◽  
Optimal Value

In this paper, a methodology for determination of the optimal value of protection design parameters, i.e. tower footing resistance, insulation strength, and surge arresters’ rating in the planning stage of transmission lines (TLs) is presented. This method calculates the shielding failure flashover rate (SFFOR) of TLs, based on Electro-geometric model (EGM) of TLs, and the back flashover rate (BFR) of TLs, based on the Monte Carlo method, in which the accuracy of the proposed methodology has been verified by comparing the resultant results with those obtained with the use of the IEEE FLASH program. The proposed method can be directly used to achieve the minimum lightning flashover rate (LFOR) of TLs by the minimum investment cost. Also, it can be used, indirectly, for determination of the appropriate value of the footing resistance, insulation strength and arresters’ rating to satisfy any target number of LFOR that might be specified by the utilities or standards.

Neural Network Model to Estimate Resistivity of Ground Enhancers Reinforced with Graphene Nano Particles for Transmission Lines

2019 ◽  
Vol 58 ◽  
pp. 139-150
Author(s):  
Isaias Ramirez-Vazquez ◽  
Miguel Ramirez-Gonzalez ◽  
J. Eduardo Salgado-Talavera
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Transmission Lines ◽  
Nano Particles ◽  
Grounding System ◽  
Surge Arresters ◽  
Lightning Impulse ◽  
Graphene Nanoparticles ◽  
Impulse Current

For many high voltage transmission lines, lightning is the first cause of outages. Different alternatives have been used to diminish these outages, like the use of counterpoise wires, installation of surge arresters, and the improvement of the grounding system using ground enhancers or chemical enhancers. In this paper, graphene nanoparticles were used to reformulate commercial ground enhancers. The results of this research end up in an improvement factor of up to 100 times the reduction in resistivity, when graphene nanoparticles are used. After lightning current impulse tests done on both types of samples, the performance of the un-reformulated ground enhancer samples shows a faster deterioration than the graphene reinforced ground enhancer samples. In order to establish a criterion to quantitatively rank the chemical ground enhancers ́ performance after the lightning impulse current tests, a neural network model was developed.

Effect of the grounding resistance to the behaviour of high-voltage transmission lines’ surge arresters

2014 ◽  
Vol 8 (6) ◽  
pp. 470-478 ◽  
Author(s):  
Christos A. Christodoulou ◽  
Lambros Ekonomou ◽  
Nikolaos Papanikolaou ◽  
Ioannis F. Gonos
Keyword(s):  
High Voltage ◽  
Transmission Lines ◽  
Grounding Resistance ◽  
Surge Arresters ◽  
High Voltage Transmission

scholarly journals Comparative Analysis on Optimal Placement of TCSC under Single Line Contingency by Using PSO and GA

2021 ◽  
pp. 217-224
Author(s):  
Su Myat Noe Oo ◽  
◽  
Swe Swe Myint ◽  
Shouji Usuda
Keyword(s):  
Transmission Lines ◽  
Electric Power System ◽  
Optimal Placement ◽  
Single Line ◽  
Stability Performance ◽  
Voltage Drops ◽  
Line Flow ◽  
Thyristor Controlled Series Capacitor ◽  
The Stability ◽  
Flow Improvement

In the network contingencies, the branch overloading and voltage violation are the most serious conditions and may lead to security problems. The application of Thyristor Controlled Series Capacitor (TCSC) can provide the required apparent reactance smoothly and rapidly and can reduce network contingency problems. This paper focuses an application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to find out the optimal locations of TCSC devices under single contingency to reduce the voltage drops at system buses and line flow improvement on transmission lines. The suitability of the proposed technique is examined on Myanmar Electric Power System. The optimized location provided by each method is applied to single line contingency condition and the responses are observed. According to the simulation results, PSO method can provide the better the stability performance under single line contingency.

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