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<p>Although optimization algorithms have made significant progress in a wide range of power system applications, many research areas and case studies remain unexplored and require additional investigation. Therefore, this paper tackles the coordination process of overcurrent relays (OCRs) in large-scale and complicated networks comprising hundreds of decision variables and constraints. In this context, savannah bengal tiger optimizer (SBTO) is employed to solve the OCR’s coordination of the 42-bus and 30-bus power grids by optimizing pickup current, time dial, and operating curve. It is worth noting that the 42-bus network comprises 291 decision variables and 713 constraints while the 30-bus incorporates 333 decision variables and 1119 constraints: indicating challenging problem formulation to be optimized. Thus, high-performance computing is necessary to provide the necessary computational efficiency and real-time results since the suggested method requires parallel processing capabilities for large-scale simulations and high-resolution models. The objective function (OF) is adapted to have a minimal total operating time (TOT) along with fulfilling problem constraints. Exceptional results of the utilized SBTO are validated against other benchmark algorithms such as genetic algorithm, particle swarm optimizer, water cycle algorithm, and gray wolf optimizer. Obviously, SBTO effectively meets the coordination criteria by achieving a minimal OF and incurring only a limited number of constraint violations. Furthermore, SBTO attains the minimum possible TOT of 19.66&#xa0;s among other competitors for the 42-bus study case. This approach highlights SBTO’s capability in effectively addressing highly complex optimization problems with numerous constraints.</p>

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Optimal relays coordination in large-scale networks with hundreds of constraints using savannah bengal tiger optimizer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月21日

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  • Hossam Ashraf,
  • Abdelmonem Draz,
  • Attia El-Fergany,
  • Ashraf Samir

摘要

Although optimization algorithms have made significant progress in a wide range of power system applications, many research areas and case studies remain unexplored and require additional investigation. Therefore, this paper tackles the coordination process of overcurrent relays (OCRs) in large-scale and complicated networks comprising hundreds of decision variables and constraints. In this context, savannah bengal tiger optimizer (SBTO) is employed to solve the OCR’s coordination of the 42-bus and 30-bus power grids by optimizing pickup current, time dial, and operating curve. It is worth noting that the 42-bus network comprises 291 decision variables and 713 constraints while the 30-bus incorporates 333 decision variables and 1119 constraints: indicating challenging problem formulation to be optimized. Thus, high-performance computing is necessary to provide the necessary computational efficiency and real-time results since the suggested method requires parallel processing capabilities for large-scale simulations and high-resolution models. The objective function (OF) is adapted to have a minimal total operating time (TOT) along with fulfilling problem constraints. Exceptional results of the utilized SBTO are validated against other benchmark algorithms such as genetic algorithm, particle swarm optimizer, water cycle algorithm, and gray wolf optimizer. Obviously, SBTO effectively meets the coordination criteria by achieving a minimal OF and incurring only a limited number of constraint violations. Furthermore, SBTO attains the minimum possible TOT of 19.66 s among other competitors for the 42-bus study case. This approach highlights SBTO’s capability in effectively addressing highly complex optimization problems with numerous constraints.