Accurate Surge Arrester Modeling for Optimal Risk-Aware Lightning Protection Utilizing a Hybrid Monte Carlo–Particle Swarm Optimization Algorithm

• Published in: Technologies (Basel) 2024-06, Vol.12 (6), p.88
• Main Authors: Asadi, Amir Hossein Kimiai, Eskandari, Mohsen, Delavari, Hadi
• Format: Article
• Language: English
• Subjects: Algorithms; arrester optimal placement; Case studies; Design and construction; Genetic algorithms; Induced voltage; lightning modeling; Lightning protection; lightning surge arrester; Location; Monte Carlo method; Mountainous areas; Optimization algorithms; Overvoltage; Particle swarm optimization; Placement; Power lines; Probability distribution; Risk; smart protection; Surge arresters; Surge suppressors; Swarm intelligence; Testing; Transformers; Zinc oxides
• ISSN: 2227-7080; 2227-7080
• DOI: 10.3390/technologies12060088

The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid’s components. In this light, appropriate models of a grid’s components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid’s components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas.