A Comparison of Fuzzy Logic Modal for Gain Scheduling in Load Frequency Control

Abstract

Fuzzy logic solves the problem of non linear systems and handles them with great efficiency and provides robustness to the system. However our aim lies in achieving the adaptive Fuzzy logic load frequency control model for gain scheduling. In this thesis the recent data based artificially intelligent techniques like fuzzy and neural network have been customized and used .The application/case study has been taken from a research paper which appeared in a reputed conference. Fuzzy provides a robust inference mechanism with no learning and adaptability and artificial neural network provides learning and adaptability. Artificial neural networks and fuzzy systems have been successfully applied to the LFC problem with rather promising results. The salient feature of these techniques is that they provide a model-free description of control systems and do not require model identification. In this thesis, an adaptive fuzzy gain scheduling scheme for conventional PI and optimal controllers has been simulated and tested for offnominal operating conditions. From the simulation and the result obtained in this thesis it has been shown that the proposed adaptive fuzzy logic controller offers better performance than fixed gain controllers, to guarantee that the fuel cell is protected by maintaining its cell utilization within its admissible range and 2) to track load changes and regulate the frequency. The two respective loops are called primary and secondary loops. The primary loop maintains constant the ratio of stack current to input fuel flow, and the secondary loop tracks the load and regulates the frequency. A distribution area error (DSE) is introduced to formulate the frequency-control problem. The secondary loop feeds back this DSE signal. Tuning of the parameters is performed using genetic