Abstract
Recent surveys conducted in the field of Power Control and Engineering show that photovoltaic (PV) systems are currently being discussed worldwide and research on the same is being carried globally. It is necessary to optimize the expanding use of photovoltaic systems through error detection in Maximum Power Point Tracking (MPPT) systems. Through this paper, an attempt is made to develop an efficient photovoltaic MPPT system using hybrid fuzzy technique to extract maximum power under a multivariable environment (changing temperature and irradiance). The MPPT system using Hybrid Controller (combining PID & FLC) has an increased efficiency and optimized output in comparison to the MPPT system using PID and Fuzzy individually. The system has explored a concept of computing academic performance indices with three MPPT models for future research based on global MPP calculation. Citation: Sharma, C., and Jain, A. (2018). Comparison of MPPT Systems in Error Optimization using PID, Fuzzy and Hybrid Fuzzy in Multivariable Environment. Trends in Renewable Energy, 4, 8-21. DOI: 10.17737/tre.2018.4.3.0046
Highlights
The power output from photovoltaic (PV) systems is the largest when it is operated at the Maximum Power Point (MPP)
The three Maximum Power Point Tracking (MPPT) systems developed are tested for Academic Performance Indices and its optimization
The converter outputs corresponding to three different controllers are obtained and optimized with the most appropriate results in the hybrid MPPT
Summary
The power output from photovoltaic (PV) systems is the largest when it is operated at the Maximum Power Point (MPP). A MPP Tracker is used to maintain this set point under a multivariable environment i.e. varying temperature and varying irradiance. Whereas a number of MPPT strategies are available based on single or multivariable approach designed using conventional or intelligent controllers [1,2,3,4,5]. Various types of MPPT systems are designed to meet voltage regulation, frequency regulation, power and harmonics control with quick response time, reduced error and increased gain. Due to difference in real time system and results of digital simulated system it is sometimes not adaptable to obtain MPP in multivariable environment. The SPC (Statistical Process Control) management tool compiles an overall mathematical measure for multiple sets of simulation and determines performance index.
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