DEVELOPMENT OF INTELLIGENT CONTROL STRATEGY FOR POWER QUALITY IMPROVEMENT OF HYBRID RES

Abstract

The major problem in renewable energy system is that the variation in power generation from time to time because of the intermittent nature of the renewable sources. In this paper presents the Artificial Neural Network (ANN) based intelligent control strategy for hybrid standalone microgrid system for varying wind, varying solar irradiation, varying load and symmetrical and asymmetrical fault conditions is presented. A dynamic model of hybrid standalone microgrid consisting wind and solar PV renewable sources with maximum power point tracking control algorithm is developed in MATLAB/Simulink software. A solar PV panel, wind energy conversion system and dynamic model of hybrid energy storage consisting Lithium-ion batteries (Li-Ion) and supercapacitor is connected with the DC Bus. The DC link is connected through the power electronic interfacing circuit and converters connected to a source for a diverse electricity generation. The main objective of this thesis is to improve the power quality of the hybrid microgrid system by solving the voltage sag and swell problem and reduce total harmonics distortion occurring due to various symmetrical and asymmetrical condition. The simulation studies have been carried out to determine system performance with different scenarios of the sources such as typical solar radiation, temperature, wind, battery and supercapacitor charge or discharge conditions The proposed strategy gives better performance characteristics and reduces the harmonics of the system compared to the conventional solution.