论文标题
增强的基于梯度的优化控制器，用于两个区域自动生成控制系统的负载频率控制
An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System
论文作者
论文摘要
这项工作提出采用增强的基于梯度的优化器（EGBO）作为在两个区域互连电源系统中的负载频率控制（LFC）问题的新方法。确定LFC问题控制器的最佳参数的重要性不能被夸大，并且估算这些参数需要复杂且非线性计算的事实使优化过程更加独特且具有挑战性。因此，将有效优化算法应用于成功获得最佳控制器参数至关重要。为了完成这项任务，将提出的EGBO算法与基于基本梯度的优化器（GBO），黑猩猩优化算法（CHOA），Sine cesine算法（SCA），灰狼优化（GWO）和粒子群优化（PSO）进行了比较，以优化一个基于综合时间的 - 粒子 - 粒度优化（PSO）。相关的发现表明，与其他优化方法相比，EGBO算法在弹性，精度和潜伏期方面具有优势。最后，统计比较进一步增强了研究的结果。
This work proposes the adoption of Enhanced Gradient-Based Optimizer (EGBO) as a new approach to the Load Frequency Control (LFC) problem in a two-area interconnected power system. The importance of determining the optimal parameters for the controllers for the LFC problem cannot be overstated, and the fact that estimating these parameters require complex and nonlinear computations makes the optimization procedure even more unique and challenging. Consequently, application of an efficient optimization algorithm to successfully attain optimal controller parameters is critical. To accomplish this task, the proposed EGBO algorithm is compared to the fundamental Gradient-Based Optimizer (GBO), Chimp Optimization Algorithm (ChOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) for optimizing an Integral-Time-multiplied-Absolute-Error (ITAE) based objective function. The relevant findings show that the EGBO algorithm is competitively superior in terms of resilience, precision, and latency when compared to other optimization methods. Lastly, the statistical comparison further strengthens the outcome of the study.
