Asymmetrical interval type-2 fuzzy logic control based MPPT tuning for PV system under partial shading condition
Objective
The main objective of this project is to track global peak in partial shading condition (PSC) with different PV array arrangements with high efficiency.
Abstract
This study has proposed asymmetrical Interval Type-2 Fuzzy Logic Control (IT-2 AFLC) based MPP algorithm for tracking global peak in Partial Shading Condition (PSC) with different PV array arrangements. Conventional Maximum Power Point Tracking (MPPT) algorithm shows best performance under uniform insolation but when photovoltaic array is partially irradiated, the Power vs Voltage (P– V) plot consists of multiple Local Maxima Power Point (LMPP) and one Global Maxima Power Point (GMPP). The conventional MPPT algorithm may track local peak and fluctuate around it, resulting in lower power tracking.
To eradicate this drawback of conventional algorithm, the solar PV system requires the synthesis of modified controller which is able to discriminate between local and global peak point. Along with implementing modified MPPT controller, to minimize the adverse effect of partial shading on PV system, different PV array arrangements like Series-Parallel (SP), Honey Comb (HC), Total Cross Tied (TCT) etc. may be used.
The presented algorithm has been compared with other approaches viz. Perturb & Observe (P&O) and type-1(T-1) FLC for GMPP tracking, fill factor, shading losses, mismatch loss and efficiency to establish its superiority. For evaluating the efficiency of different algorithms, MPPT efficiency test has been performed under dynamic condition. The proposed algorithm has been developed under MATLAB/Simulink.
Keywords: Maximum Power Point Tracking (MPPT), Adaptive Neuro Fuzzy Interference System (ANFIS), Photovoltaic, intelligent asymmetrical interval type-2 FLC.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software Configuration:
Operating System : Windows 7/8/10
Application Software : Matlab/Simulink
Hardware Configuration:
RAM : 8 GB / 4 GB (Min)
Processor : I3 / I5(Mostly prefer)
Learning Outcomes
- Introduction to Matlab/Simulink
- What is EISPACK & LINPACK
- How to start with MATLAB
- About Matlab language
- About tools & libraries
- Application of Matlab/Simulink
- About Matlab desktop
- Features of Matlab/Simulink
- Basics on Matlab/Simulink
- Introduction to power electronics converters
- Introduction to phase locked loop (PLL)
- Introduction to PWM techniques
- Introduction to solar power generation
- Introduction to ANFIS Controller
- Design of PLL
- Design of fuzzy logic controller
- Design of solar PV system
- Design of MPPT controller
- Design of DC – DC boost converter
- We can learn about the generation of gate pulses to the Boost converter
- We can learn about the Local Maxima Power Point(LMPP)
- We can learn about the Global Maxima Power Point(GMPP)
- We can learn about fuzzy Logic Controller
- We can learn about type-2 fuzzy logic based MPPT
- We can learn about series PV arrangement
- We can learn about series-parallel PV array management
- We can learn about ANFIS Controller
- We can learn about incremental conductance method
- Introduction to controllers
- Design of PI controller
- Project Development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
