MPPT controller for a photovoltaic power system based on fuzzy logic

Garraoui, Radhia; Hamed, Mouna Ben; Sbita, Lassaâd

doi:10.1109/ssd.2013.6564055

Cited by 19 publications

(6 citation statements)

References 5 publications

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“…These involve using fuzzy model-based approaches [38][39][40][41][42][43], or using fuzzy logic to decide variable step sizes to use with other conventional techniques [16,27,[44][45][46]. The Takagi-Sugeno fuzzy system requires few rules and coefficients and therefore can be implemented with lower complexity and computational time than the Mamdani method [39][40][41]47].…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Maximum Power Point Tracking Methods for PV Systems

Lyden

Haque

Mahmud

2016

Advances in Solar Photovoltaic Power Plants

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Maximum power point tracking (MPPT) is an important consideration in photovoltaic (PV) systems. These systems exhibit variable nonlinear current-voltage (I-V) and power-voltage (P-V) characteristics which vary with environmental conditions. The optimum operation of a PV system occurs when the system operates at the unique maximum power point (MPP) for the given environmental conditions. Key environmental conditions include the irradiance on the cell, temperature of the cell and any shading phenomenon. Shading can occur due to objects, dust or dirt and module mismatch arising from damage or manufacturing tolerances. These shading effects introduce further nonlinearity into the I-V and P-V characteristics of the system. An extensive variety of MPPT techniques has been proposed which vary from simple estimation techniques to advanced tracking techniques. In this chapter, the criteria for assessing the performance of MPPT methods are defined followed by a complete description and discussion of both techniques designed for uniform environmental conditions and those designed for non-uniform environmental conditions.

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Maximum Power Point Tracking Methods for PV Systems

Lyden

Haque

Mahmud

2016

Advances in Solar Photovoltaic Power Plants

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“…As mentioned in previous section, implementing the fuzzy logic controller needs to have enough experience more than knowing the technical model of the system because in process of designing the fuzzy logic controller, rules and inference and also the range of membership functions are the important and crucial sections. Each fuzzy system has a subset rule that will be defined according to the specific range of membership function and vice versa [29][30][31][32]. In systems with the boost converter, different fuzzy logic subsets with different rules and range of membership functions have been used in order to obtain MPP.…”

Section: Fuzzy Logic Subsetsmentioning

confidence: 99%

Evaluation of Fuzzy Logic Subsets Effects on Maximum Power Point Tracking for Photovoltaic System

Hajighorbani

Radzi

Kadir

et al. 2014

International Journal of Photoenergy

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Photovoltaic system (PV) has nonlinear characteristics which are affected by changing the climate conditions and, in these characteristics, there is an operating point in which the maximum available power of PV is obtained. Fuzzy logic controller (FLC) is the artificial intelligent based maximum power point tracking (MPPT) method for obtaining the maximum power point (MPP). In this method, defining the logical rule and specific range of membership function has the significant effect on achieving the best and desirable results. This paper presents a detailed comparative survey of five general and main fuzzy logic subsets used for FLC technique in DC-DC boost converter. These rules and specific range of membership functions are implemented in the same system and the best fuzzy subset is obtained from the simulation results carried out in MATLAB. The proposed subset is able to track the maximum power point in minimum time with small oscillations and the highest system efficiency (95.7%). This investigation provides valuable results for all users who want to implement the reliable fuzzy logic subset for their works.

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“…Several authors have improved the use of FLC technique; each research is characterised by a different number of input and output parameters. The traditional FLC MPPT method with two parameters has been proposed in several works [37–39]. However, this method needs a long time for compilation, since the setup and adjustment of the FLC rules with an important number lead to a big computing time and need a big memory.…”

Section: Introductionmentioning

confidence: 99%

Efficiency optimisation of a microcontroller‐based PV system employing a single sensor fuzzy‐logic controller

Abdourraziq

Maâroufi

2018

IET Power Electronics

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This study presents a particular fuzzy-logic control (FLC) to produce the maximum power point of the photovoltaic system. The particularity of the single sensor FLC (SSFLC) method uses single current sensor and improves the drawbacks of response time and accuracy of the Perturb and Observe (P&O) method. Compared with the P&O method, the SSFLC improves the response time, precision at steady state and reduces the steady-state oscillation. To confirm its high performance, the proposed SSFLC and P&O algorithms have been evaluated by simulation utilising MATLAB/Simulink under linear and nonlinear profiles of weather conditions. The experimental results demonstrate that the effectiveness of the SSFLC method is an average 95.8% compared to 83.3%of the P&O method.

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MPPT controller for a photovoltaic power system based on fuzzy logic

Cited by 19 publications

References 5 publications

Maximum Power Point Tracking Methods for PV Systems

Maximum Power Point Tracking Methods for PV Systems

Evaluation of Fuzzy Logic Subsets Effects on Maximum Power Point Tracking for Photovoltaic System

Efficiency optimisation of a microcontroller‐based PV system employing a single sensor fuzzy‐logic controller

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