A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control

Abstract

This paper presents a novel fuzzy logic control technique tuned by particle swarm optimization (PSO-FLC) for maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed PV system composes of a current-mode boost converter (CMBC) with bifurcation control. An optimal slope compensation technique is used in the CMBC to keep the system adequately remote from the first bifurcation point in spite of nonlinear characteristics and instabilities of this converter. The proposed PSO technique allows easy and more accurate tuning of FLC compared with the trialand-error based tuning. Consequently, the proposed PSO-FLC method provides faster tracking of maximum power point (MPP) under varying light intensities and temperature conditions. The proposed MPPT technique is simple and particularly suitable for PV system equipped with CMBC. Experimental results are shown to confirm superiority of the proposed technique comparing with the conventional PVVC technique and the trial-and-error based tuning FLC.

Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System

“…where o N = 0 for i = 4, j = 4. The second part, E 2 = (1/2)B(ΔD k ref ) 2 is related to the output of FLC and depends only on neurons N i (i = 8,9,10,11,12,13,14,15,16). The ΔD k ref can be defined by defuzzification by using the centroid method and is written as:…”

“…Developing fuzzy method also involves expert knowledge and experimentation in selecting parameters and membership functions. For this reason, adaptive fuzzy logic control [10] and parameter optimization techniques such as genetic algorithm [11] and particle swam optimization [12,13] have been introduced to overcome the problem in MPPT algorithms.…”

Synergetic power optimization control of photovoltaic systems

“…The changes are related to both solar insolation and cell temperature. The most commonly control techniques are Hill-Climbing, Perturb and Observe (P&O), Incremental Conductance (Inc-Cond), fractional open-circuit voltage (V oc ), fractional short-circuit current control (l sc ) [1][2][3][4][5], intelligent methods as artificial neural networks (NN), genetic algorithms and fuzzy logic (FL) [6][7][8][9]. For more details on these methods and related applications on solar energy, PV panel, the readers can refer to [8][9][10][11][12][13][14] and the references therein.…”

Sliding-Mode Controller for Maximum Power Point Tracking in Grid-Connected Photovoltaic Systems

“…The main advantages of those solutions are the low cost and implementation simplicity since they only require a single (voltage or current) sensor [9,10]; But their efficiency is low compared with the P & O and IC algorithms. In contrasts, techniques based on computational intelligence, such as neural networks and fuzzy logic, offer speed and efficiency in tracking the MPP [11][12][13]; however its complexity and implementation costs are high compared with the P & O and IC algorithms, which make them costly solutions.…”