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Kautz Function Based Continuous-Time Model Predictive Controller for Load Frequency Control in a Multi-Area Power System

机译:基于KAUTZ功能的连续时间模型预测控制器,用于多区电力系统中的负载频率控制

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摘要

A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control (LFC). A dynamic model of an interconnected power system was used for Model Predictive Controller (MPC) design. MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix. In this paper, the optimal closed loop feedback gain matrix was calculated using Kautz function. Being an Orthonormal Basis Function (OBF), Kautz function has an advantage of solving complex pole-based nonlinear system. Genetic Algorithm (GA) was applied to optimally tune the Kautz function-based MPC. A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC. The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral (PI) controller and Linear Quadratic Regulation (LQR).
机译:使用KAUTZ功能提出了连续时间模型预测控制器,以提高负载频率控制(LFC)的性能。互连电力系统的动态模型用于模型预测控制器(MPC)设计。 MPC通过计算最佳闭环反馈增益矩阵来预测动态模型的未来轨迹。在本文中,使用Kautz函数计算了最佳闭环反馈增益矩阵。作为正常的基函数(OBF),Kautz函数具有求解复杂的极值非线性系统的优点。应用遗传算法(GA)以最佳地调整基于Kautz函数的MPC。基于相平面分析的约束以成本函数实现,以提高基于KAUTZ函数的MPC的稳健性。通过三个区域互连的电力系统模拟所提出的方法,并通过与常规比例和积分(PI)控制器和线性二次调节(LQR)进行比较并表现出所提出的方法的效率。

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