Risk-Based Arrester Placement Optimization in Substations Using NSGA-II and EM Simulation
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Abstract
This study proposes a risk-based arrester placement optimization method to improve the protection of substation systems against lightning and switching surges, using the NSGA-II (Non-Dominated Sorting Genetic Algorithm II) algorithm and Electromagnetic Transients (EMT) simulation. The main objective of this study is to reduce the Expected Annual Risk (EAR) related to equipment damage and operational costs through optimal arrester placement, while minimizing the Life-Cycle Cost (LCC) of the substation protection system. EMT simulation is used to model the system response to lightning and switching surge events, while NSGA-II is used to solve a multi-objective optimization problem, considering various potential locations and different arrester ratings.
The optimization results show a clear trade-off between cost and risk reduction, with the best solution providing the optimal balance between risk reduction and lifecycle cost. Several critical locations, such as transformer terminals and incomer lines, were identified as priorities for arrester installation, as they offer significant risk reduction at relatively low cost. The reduction in peak equipment-damaging voltage (BIL) after mitigation with arresters also showed substantial improvement. Sensitivity analysis showed that the protection design remained effective despite variations in external parameters such as lightning density and soil resistivity. With this approach, utilities can make more informed decisions about selecting arrester locations and types that meet their budgets and protection needs, significantly reducing the risk of system failure. The results of this study can guide electricity companies in implementing more efficient and economical substation protection policies, while extending equipment life and improving distribution network reliability.
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[2] Ravichandran, N., et al. (2024). "Surge arrester optimal placement in distribution networks." Electric Power Systems Research, 217, 107739
[3] Xia, Q., et al. (2018). "Surge Arrester Placement for Long Transmission Line and Substation Lightning Protection." Proceedings of the Universities Power Engineering Conference.
[4] Kezunovic, M., et al. (2018). "Optimal Placement of Line Surge Arresters Based on Predictive Risk Assessment." Proceedings of the 2018 CIGRÉ Session.
[5] Daly, T., & Wilksch, B. (2016). "Lightning protection of substations using EMT modelling." Down to Earth Conference (DTEC).
[6] hou, J., & Kaffashan, I. (2023). "Increasing substation resiliency with EMT studies." PS Consulting.
[7] Rocchetta, R., et al. (2020). "Risk Assessment and Risk-Cost Optimization of Distributed Power Generation Systems Considering Extreme Weather Conditions." Risk Analysis, 40(7), 1371–1388.
[8] He, R., et al. (2025). "Multi-Objective Collaborative Optimization of Distribution Networks Considering Energy Storage and Demand Response." Energies, 18(19), 5232.
[9] Safaei, F., & Niasati, M. (2024). "A new method for surge arrester placement in high-voltage substations considering environmental effects." IET Science, Measurement & Technology.
[10] Yamanaka, A., et al. (2025). "Lightning overvoltages incoming to a substation: Analysis and mitigation." Electric Power Systems Research, 211, 107581.
[11] Zhao, Y., et al. (2024). "Multi-Objective Optimization Operation of Multi-Agent Active Distribution Network Based on Analytical Target Cascading Method." Energies, 17(20), 5022.
[12] Heyns, A. M., et al. (2021). "Optimisation of surveillance camera site locations and configurations using a multi-objective genetic algorithm." Expert Systems with Applications, 167, 114160.
[13] Patil, M. B., et al. (2019). "Multi-Objective Optimisation of Damper Placement for Improved Seismic Response in Dynamically Similar Adjacent Buildings." Structural Control Health Monitoring, 26(1), e2363.
[14] Hashim, H. A., et al. (2018). "Optimal Placement of Relay Nodes in Wireless Sensor Network Using Artificial Bee Colony Algorithm." Wireless Personal Communications, 100(1), 1–19.
[15] Guerrero, C., et al. (2025). "Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog Architectures." arXiv preprint arXiv:2501.09958.