Design of bald eagle search optimised cascaded PI-FOPID controller for frequency regulation of islanded microgrid system

D. Mishra, P. C. Nayak, R. Prusty, B. K. Sahu
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Abstract

This article introduces the “Bald Eagle Search Optimisation” (BESO) technique for the improvement of the load frequency control (LFC) of an isolated Microgrid (i-MG) system using a cascaded PI - fractional order FOPID controller. The BESO algorithm is based on a bald eagle's intelligent hunting strategy for food. The i-MG consists of distributed energy sources (DEs) such as wind/ photovoltaic systems with diesel and various energy storage systems (ESSs). Real wind dynamics and photovoltaic uncertainties are considered for this study. The proposed BESO optimised PI-FOPID controller responses are compared with BESO set PID and FOPID controller and also with other typical optimizing tools like differential evolution (DE) and Particle Swarm Optimization (PSO). The performance of the proposed approach is studied under stochastic power variations of load, wind and photovoltaic systems under the influence of several ESSs to manage the deficiency of power in critical situations.
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秃鹰搜索优化级联PI-FOPID孤岛微电网调频控制器设计
本文介绍了利用级联PI -分数阶FOPID控制器改进孤立微电网(i-MG)系统负载频率控制(LFC)的“白头鹰搜索优化”(BESO)技术。BESO算法是基于秃鹰寻找食物的智能策略。i-MG由分布式能源(de)组成,如风能/光伏柴油系统和各种储能系统(ess)。本研究考虑了实际风动力学和光伏的不确定性。提出的BESO优化PI-FOPID控制器响应与BESO设定PID和FOPID控制器以及其他典型优化工具如差分进化(DE)和粒子群优化(PSO)进行了比较。研究了在多个ess影响下负载、风电和光伏系统的随机功率变化情况下,该方法的性能,以管理临界情况下的电力不足。
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