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Failure Analysis and Optimization of High Mechanical Strength Surge Arresters Using Finite Element Analysis

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Abstract

High mechanical strength surge arresters (HPCAs) are essential components for protecting electrical power systems from overvoltage surges. However, HPCAs can fail under cantilever loading conditions, which is a rare but serious event that can have significant consequences for the reliability of the power system. This paper presents a failure analysis and optimization of HPCAs based on finite element analysis (FEA). A three-dimensional FEA model was developed and used to evaluate the cantilever strength of the arrester under various design configurations. The existing design configuration with 12 insulating rods of 5.8 mm diameter was found to fail to meet the cantilever strength criteria. Therefore, the deflection values of various configurations of insulating rod diameter (5.8, 8, 10, 12, 14, and 16 mm) and number of rods (12, 18, and 14) were evaluated. The depth of the insulating rods inside the aluminum end fittings (EFs) was also varied. This work further employs a ramping method in analysis to reduce processing time and applies adhesion boundary conditions to simulate the behavior of the insulating rods and aluminum EFs. These approaches have allowed us to accurately model the behavior of the HPCA under cantilever loading conditions and investigate the failure mechanisms of the system. The FEA results showed that the cantilever strength of the HPCA could be significantly improved by increasing the diameter of the insulating rods and/or increasing the number of rods. The proposed approach is used to develop a new HPCA design with improved mechanical strength, which is validated through experimental testing.

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  1. Raychem Innovation Centre, Raychem RPG, Halol, Gujarat, India

    Ganesh Bhoye & Ishant Jain

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  1. Ganesh Bhoye
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  2. Ishant Jain
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Correspondence to Ishant Jain.

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Bhoye, G., Jain, I. Failure Analysis and Optimization of High Mechanical Strength Surge Arresters Using Finite Element Analysis. J Fail. Anal. and Preven. (2024). https://doi.org/10.1007/s11668-024-01916-8

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