This paper proposes a maiden application of a two dimensional Sine Logistic map based chaotic sine cosine algorithm (2D-SLCSCA) optimized classical PID controller for load frequency control (LFC) of an islanded microgrid (MG). In comparison to random variables and 1D chaotic sequences, the 2D chaotic sequences are more ergodic and possess a wider chaotic range, thereby enhancing the global convergence speed and search capability of an algorithm. Initially, the proposed 2D-SLCSCA is tested on eight classical benchmark test functions and its performance is compared with 1D Logistic map based chaotic SCA (1D-LCSCA), 1D Sine map based chaotic SCA (1D-SCSCA), and the SCA incorporating random variables. Test results reveal that the proposed algorithm exhibits better convergence characteristics, statistics, and execution time. Finally, the proposed 2D-SLCSCA is implemented for the LFC analysis of the islanded MG. To establish the competence of the proposed algorithm in this regard, its performance is compared with 2D Hénon map based chaotic SCA, 2D Lozi map based chaotic SCA, improved salp swarm algorithm (ISSA), SCA, grey wolf optimizer (GWO), and particle swarm optimization (PSO) algorithm considering diverse load disturbance patterns in the MG. Simulation results affirm that the proposed control scheme augments the frequency response of the MG exhibiting a maximum percentage improvement of 78.89%, 78.86%, and 96.51% in peak overshoot ($O{S}_{peak}$), peak undershoot ($U{S}_{peak}$) and objective function ($O{F}_{ITSE}$) value, respectively as compared to the other algorithms. Furthermore, sensitivity of the proposed 2D-SLCSCA is validated considering $\pm$ 30% variation in the MG parameters.

本文提出了基于二维正弦逻辑映射的混沌正弦余弦算法 (2D-SLCSCA) 优化经典 PID 控制器的首次应用，用于孤岛微电网 (MG) 的负载频率控制 (LFC)。与随机变量和一维混沌序列相比，二维混沌序列遍历性更强，混沌范围更广，从而提高了算法的全局收敛速度和搜索能力。最初，提出的 2D-SLCSCA 在八个经典的基准测试函数上进行了测试，并将其性能与基于一维逻辑图的混沌 SCA（1D-LCSCA）、基于一维正弦图的混沌 SCA（1D-SCSCA）和结合随机的 SCA 进行了比较。变量。测试结果表明，所提出的算法表现出更好的收敛特性、统计数据和执行时间。最后，建议的 2D-SLCSCA 用于孤岛 MG 的 LFC 分析。为了建立所提出算法在这方面的能力，将其性能与基于 2D Hénon map 的混沌 SCA、基于 2D Lozi map 的混沌 SCA、改进的 Salp swarm 算法 (ISSA)、SCA、灰狼优化器 (GWO) 和粒子考虑 MG 中不同负载扰动模式的群优化 (PSO) 算法。仿真结果证实，所提出的控制方案增强了 MG 的频率响应，在峰值超调方面表现出最大百分比提高 78.89%、78.86% 和 96.51%。改进的 Salp 群算法 (ISSA)、SCA、灰狼优化器 (GWO) 和粒子群优化 (PSO) 算法考虑到 MG 中的不同负载扰动模式。仿真结果证实，所提出的控制方案增强了 MG 的频率响应，在峰值超调方面表现出最大百分比提高 78.89%、78.86% 和 96.51%。改进的 Salp 群算法 (ISSA)、SCA、灰狼优化器 (GWO) 和粒子群优化 (PSO) 算法考虑到 MG 中的不同负载扰动模式。仿真结果证实，所提出的控制方案增强了 MG 的频率响应，在峰值超调方面表现出最大百分比提高 78.89%、78.86% 和 96.51%。$哦{秒}_{磷\mathrm{电子}\mathrm{一种}克}$), 峰值下冲 ($你{秒}_{磷\mathrm{电子}\mathrm{一种}克}$) 和目标函数 ($哦F_{\mathrm{一世}吨秒乙}$) 值，分别与其他算法相比。此外，考虑到所提出的 2D-SLCSCA 的敏感性得到验证$\pm$ MG 参数有 30% 的变化。