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<p>The modern, restructured power system's planning, operation, and control have become significantly more complex, leading to an extensive, widespread use of power electronic devices and distributed generation sources powered by renewable energy. A large-scale linked power system's ability to function effectively and dependably depends on all operating constraints remaining within the pre-established bounds. A crucial method for evaluating system security under emergency and critical loading scenarios is the N-1 contingency analysis. This work uses the learning-based chicken swarm optimization (L-CSO) method to plan transmission networks under normal, emergency, and critical loading scenarios in a reliable, safe, and cost-effective manner. The recommended L-CSO algorithm has been applied to 13 mathematical (benchmark) functions and the IEEE 30-bus system to demonstrate its efficacy. Based on computational analysis, the suggested L-CSO method produces results that are comparable to those of previous algorithms for optimal power flow (OPF) problems without exceeding operational restriction limitations in both normal and emergency. Based on numerical results and statistical analysis, the suggested L-CSO technique is found to be more robust and successful (in terms of outcome quality) than the CSO and other meta-heuristic algorithms communicated in current literature.</p>

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An efficient learning-based chicken swarm optimization algorithm for power system planning under critical load conditions

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Nisha Singh,
  • J N Rai,
  • Uma Nangia

摘要

The modern, restructured power system's planning, operation, and control have become significantly more complex, leading to an extensive, widespread use of power electronic devices and distributed generation sources powered by renewable energy. A large-scale linked power system's ability to function effectively and dependably depends on all operating constraints remaining within the pre-established bounds. A crucial method for evaluating system security under emergency and critical loading scenarios is the N-1 contingency analysis. This work uses the learning-based chicken swarm optimization (L-CSO) method to plan transmission networks under normal, emergency, and critical loading scenarios in a reliable, safe, and cost-effective manner. The recommended L-CSO algorithm has been applied to 13 mathematical (benchmark) functions and the IEEE 30-bus system to demonstrate its efficacy. Based on computational analysis, the suggested L-CSO method produces results that are comparable to those of previous algorithms for optimal power flow (OPF) problems without exceeding operational restriction limitations in both normal and emergency. Based on numerical results and statistical analysis, the suggested L-CSO technique is found to be more robust and successful (in terms of outcome quality) than the CSO and other meta-heuristic algorithms communicated in current literature.