An Improvement of a Fuzzy Logic-Controlled Maximum Power Point Tracking Algorithm for Photovoltic Applications

Na, Woonki; Chen, Pengyuan; Kim, Jong-Hoon

doi:10.3390/app7040326

Cited by 30 publications

(23 citation statements)

References 30 publications

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“…The proposed method and conventional method compare the speed at which the maximum power point is tracked and the error at steady state. Since the environment is constructed using the same artificial light source, comparisons of output power, voltage and current can be a valid method for verifying peak power point tracking performance [51][52][53][54]. Figure 13 shows the circuit diagram and control system for the MPPT control performance test of solar power generation.…”

Section: Experiments Resultsmentioning

confidence: 99%

Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid

Kim

2019

Sustainability

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This paper proposes the method for maximum power point tracking (MPPT) of the photovoltaic (PV) system. The conventional PI controller controls the system with fixed gains. Conventional PI controllers with fixed gains cannot satisfy both transient and steady-state. Therefore, to overcome the shortcomings of conventional PI controllers, this paper presents the variable gain proportional integral (VGPI) controllers that control the gain value of PI controllers using fuzzy control. Inputs of fuzzy control used in the VGPI controller are the slope from the voltage-power characteristics of the PV module. This paper designs fuzzy control's membership functions and rule bases using the characteristics that the slope decreases in size, as it approaches the maximum power point and increases as it gets farther. In addition, the gain of the PI controller is adjusted to increase in transient-state and decrease in steady-state in order to improve the error in steady-state and the tracking speed of maximum power point of the PV system. The performance of the VGPI controller has experimented in cases where the solar radiation is constant and the solar radiation varies, to compare with the performance of the P&O method, which is traditionally used most often in MPPT, and the performance of the PI controller, which is used most commonly in the industry field. Finally, the results from the experiment are presented and the results are analyzed.

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Section: Experiments Resultsmentioning

confidence: 99%

Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid

Kim

2019

Sustainability

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“…See Equation (19). Figure 6 shows the buck converter that was modeled using the fundamental blocks of Simulink.…”

Section: Modelling Of Buck Convertermentioning

confidence: 99%

“…Also, algorithms based on artificial intelligence techniques such as fuzzy logic [13][14][15][16][17][18][19] and neural networks [20][21][22] have been used, as well as the implementation of optimization algorithms such as glowworm swarm [23], ant colony [24,25] and bee colony [26][27][28]. These algorithms are part of soft computing techniques and have the advantage of being easily implemented using embedded systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Based MPPT Controller for a PV System

Robles-Algarín¹,

Giraldo²,

Álvarez³

2017

Energies

124

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Abstract:The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT) to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O) algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA) was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.

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“…In order to improve efficiency and tracking performance, numerous MPPT algorithms have been published based on many aspects such as complexity, sensors required, cost, and efficiency [2][3][4][5][6][7]. Several approaches have been discussed in order to eliminate and/or reduce the number of sensing elements [8].…”

Section: Introductionmentioning

confidence: 99%

A Single-Phase Multilevel PV Generation System with an Improved Ripple Correlation Control MPPT Algorithm

Hammami

Grandi

2017

Energies

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Abstract:The implementation of maximum power point tracking (MPPT) schemes by the ripple correlation control (RCC) algorithm is presented in this paper. A reference is made to single-phase single-stage multilevel photovoltaic (PV) generation systems, when the inverter input variables (PV voltage and PV current) have multiple low-frequency (ripple) harmonics. The harmonic analysis is carried out with reference to a multilevel configuration consisting of an H-bridge inverter and level doubling network (LDN) cell, leading to the multilevel inverter having double the output voltage levels as compared to the basic H-bridge inverter topology (i.e., five levels vs. three levels). The LDN cell is basically a half-bridge fed by a floating capacitor, with self-balancing voltage capability. The multilevel configuration introduces additional PV voltage and current low-frequency harmonics, perturbing the basic implementation of the RCC scheme (based on the second harmonic component), leading to malfunctioning. The proposed RCC algorithm employs the PV current and voltage harmonics at a specific frequency for the estimation of the voltage derivative of power dP/dV (or dI/dV), driving the PV operating point toward the maximum power point (MPP) in a faster and more precise manner. The steady-state and transient performances of the proposed RCC-MPPT schemes have been preliminarily tested and compared using MATLAB/Simulink. Results have been verified by experimental tests considering the whole multilevel PV generation system, including real PV modules, multilevel insulated-gate bipolar transistor (IGBT) inverters, and utility grids.

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An Improvement of a Fuzzy Logic-Controlled Maximum Power Point Tracking Algorithm for Photovoltic Applications

Cited by 30 publications

References 30 publications

Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid

Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid

Fuzzy Logic Based MPPT Controller for a PV System

A Single-Phase Multilevel PV Generation System with an Improved Ripple Correlation Control MPPT Algorithm

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