Load Frequency Control Analysis of PV System Using PID and ANFC Controller

R. Rituraj, A. Várkonyi-Kóczy
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引用次数: 1

Abstract

This paper deals with the Adaptive neuro-fuzzy inference system (ANFIS)–based load frequency controller (LFC). These controllers are projected for load frequency control of thermal-Photovoltaic (PV) power generation entity as a hybrid power system. In this study, random solar isolation is applied to the proposed hybrid power system. The proposed hybrid power system consists of a PV power unit with a maximum power point tracking control, a PV inverter, and an AC load. Simulations are performed with structural change in the load setting. The solar isolation results are compared with conventional proportional-integral-derivative (PID) and fuzzy logic controller (FLC). The results are then projected with an ANFIS based LFC. The simulation results observed that ANFIS attains a relatively better response for the frequency deviation profile. It typically controls the frequency deviation of a given hybrid power system and thereby advances the dynamic performances. The results also show that the performance of the hybrid power system with the use of ANFIS based neuro-fuzzy controllers attains relatively better than those which attains by the PID and FLC.
基于PID和ANFC控制器的光伏系统负荷频率控制分析
本文研究了基于自适应神经模糊推理系统的负载频率控制器(LFC)。将这些控制器应用于作为混合动力系统的热光伏发电实体的负荷频率控制。在本研究中,随机太阳能隔离应用于所提出的混合电力系统。所提出的混合电力系统由具有最大功率点跟踪控制的光伏发电单元、光伏逆变器和交流负载组成。在结构改变荷载设置的情况下进行了模拟。并与传统的比例-积分-导数(PID)和模糊逻辑控制器(FLC)进行了比较。然后用基于ANFIS的LFC预测结果。仿真结果表明,ANFIS对频率偏差曲线有较好的响应。它典型地控制了给定混合动力系统的频率偏差,从而提高了系统的动态性能。结果表明,采用基于ANFIS的神经模糊控制器的混合动力系统性能优于PID和FLC控制器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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