Maximum power point tracking using perturb and observe, fuzzy logic and ANFIS

Abstract

The main objective of this project is to investigate the performance conventional method of perturb and observe (P&O) and soft computing techniques (fuzzy logic and adaptive neuro-fuzzy inference system) of maximum power point tracking (MPPT) for photovoltaic (PV) module. In this paper, the MSX-64 PV module and boost DC-DC converter are used for simulation and modeling the MPPT system. This work demonstrates the performance of those three types of MPPT techniques, which subjected to a partial shading pattern as well as a non-shaded and shaded of real weather profile. These MPPT techniques are compare in terms of power extracted, MPPT efficiency, rise time, its ability to track global maximum power point (MPP), and the response to varied weather. Simulation results of soft computing MPPT techniques have shown the ability to track the MPP during partial shading conditions and the response to weather changes when nonshaded real weather profile applied to the system. The performance of the conventional P&O based MPPT has depicted the failure of this controller to track the global MPP during partial shading and lack response to real weather changes. The proposed system has simulated using MATLAB SIMULINK.

Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

“…The P&O algorithm [1] controls the duty cycle (D) of the converter depending on the PV panel's present voltage and power values. The predicted maximum power P mp by the SVMR model, proposed SVMR strategy (P svmr ), and P&O method responses are compared in Fig.…”

“…Fuzzy logic control (FLC) [1,27] handles the system's nonlinearities in a better way, no need a precise mathematical model and also works with defective inputs. The variation in D (ΔD) is FLC output.…”

“…Each technique has its own set of strengths and weaknesses and its own method of tracking the MPP. The conventional methods are the Perturb and Observe (P&O) [1] and incremental conductance (IC) [2] methods, mathematical methods such as curve fitting [3] and beta MPPT [4], measurement-based methods such as look-up table [5] and current sweep [6], constant parameter methods such as fractional open circuit voltage [7] and fractional short circuit current [8] methods, trial and error methods such as gradient descent method [9] and variable inductance method [10], optimization techniques like genetic algorithm [11], ant colony optimization [12], practical swarm optimization [13], gray wolf optimiza-tion [14], and cuckoo search optimization [15], intellectual methods like an artificial neural network [16], fuzzy logic control [1], and ANFIS [1,9] are listed in the literature.…”

Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control

“…Advanced control technology is an alternative solution to improve the overall performance of an energy system. Among them, the Maximum power point tracking (MPPT) algorithm is recognized as an effective method to solve the problem of power output efficiency of power generation system, which has been widely used in photovoltaic power generation system (PV) 10 – 13 , wind power generation system 14 , 15 , and wind solar hybrid power system 16 . In recent years, the application of MPPT in fuel cell system has been reported 17 – 19 .…”

A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV

“…Perturb and observation (P&O) algorithm [35][36][37]; • Incremental conductance algorithm [37][38][39];…”