Maximum Power Point Tracking of Photovoltaic Generation Based on the Type 2 Fuzzy Logic Control Method

Gheibi, Amir; Mohammadi, Sirus; Maghfoori, Malihe

doi:10.1016/j.egypro.2011.10.073

Cited by 18 publications

(4 citation statements)

References 12 publications

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“…This process of converting a numerical variable to a linguistic variable (fuzzy number) [39]. The steps of Fuzzification are -Using a normalized continuous universe of discourse [-1, 1].…”

Section: A -Fuzzificationmentioning

confidence: 99%

Power Quality Enhancement based on Type 2 Fuzzy logic direct power control of a Microgrid connected photovoltaic system with battery storage feeding nonlinear load

Bouziane¹,

Zarour²,

Mehazzem

2022

Preprint

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In this paper, we deal with control performance and power qualityimprovement of Microgrid connected Photovoltaic System (PVS) withstorage. A comparative study was conducted between two versions of Fuzzy Logic Controllers: Type 1 (T1FLC) and Type 2 (T2FLC). DCside of the proposed microgrid system is composed of a photovoltaic systemand battery storage. Both are controlled by fuzzy logic algorithmsto extract maximum power from solar panels, and guarantee optimalmanagement of battery storage. AC side is composed of a two-level MultifunctionalVoltage Source Inverter (MVSI) associated with a ShuntActive Power Filter (SAPF) connected to the grid and supplies a nonlinearload. This side is controlled by a Direct Power Control strategyassociated with Space Vector-Modulation technique (DPC-SVM),and an Fuzzy Logic Controller (FLC) algorithm to inject the extractedactive power into the network and compensate the reactive power.The growing integration of Renewable Energy Sources (RES) poses several challenges to Microgrid systems. The T2FLC structureis proposed to better handle those challenges and enhance performance.Simulation of the proposed solution is validated under MATLAB/Simulink. The obtained simulation results show that T2FLC technique provides the best solution in terms of good tracking andoptimization performance, reactive power compensation, Total HarmonicDistortion (THD) elimination caused by non-linear loads,and robustness against irradiance and nonlinear load variations.

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“…This process of converting a numerical variable to a linguistic variable (fuzzy number) [39]. The steps of Fuzzification are -Using a normalized continuous universe of discourse [-1, 1].…”

Section: A -Fuzzificationmentioning

confidence: 99%

Power Quality Enhancement based on Type 2 Fuzzy logic direct power control of a Microgrid connected photovoltaic system with battery storage feeding nonlinear load

Bouziane¹,

Zarour²,

Mehazzem

2022

Preprint

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“…Algorithms based on fuzzy logic [3,[14][15][16]: MPPT control techniques based on fuzzy logic have recently been introduced because they offer the advantage of robust control and do not require exact knowledge of the mathematical model of the system. In addition, they improve performances (convergence speed, accuracy, ease of implementation, and low cost).…”

Section: Motivationmentioning

confidence: 99%

Study of the Intelligent Behavior of a Maximum Photovoltaic Energy Tracking Fuzzy Controller

et al. 2018

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The Maximum Power Point Tracking (MPPT) strategy is commonly used to maximize the produced power from photovoltaic generators. In this paper, we proposed a control method with a fuzzy logic approach that offers significantly high performance to get a maximum power output tracking, which entails a maximum speed of power achievement, a good stability, and a high robustness. We use a fuzzy controller, which is based on a special choice of a combination of inputs and outputs. The choice of inputs and outputs, as well as fuzzy rules, was based on the principles of mathematical analysis of the derived functions (slope) for the purpose of finding the optimum. Also, we have proved that we can achieve the best results and answers from the system photovoltaic (PV) with the simplest fuzzy model possible by using only 3 sets of linguistic variables to decompose the membership functions of the inputs and outputs of the fuzzy controller. We compare this powerful controller with conventional perturb and observe (P&O) controllers. Then, we make use of a Matlab-Simulink® model to simulate the behavior of the PV generator and power converter, voltage, and current, using both the P&O and our fuzzy logic-based controller. Relative performances are analyzed and compared under different scenarios for fixed or varied climatic conditions.

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“…The fuzzy logic technique is frequently used with PV system as in [16][17][18][19][20] but none of previous research implements the fuzzy logic for adaptation for FOPID controller. Fuzzy type 2 was implemented from 2013 in the research and some authors use this technique for MPPT of PV system as in [21] and [22] but none of them use this technique with FOPID controller. The system used in this paper is related to a real boost pumping station in ras ghareb city located in Egypt.…”

Section: Introductionmentioning

confidence: 99%

FOPID Controller Based AC Pump Supplied from PV Standalone Source Tuned using Fuzzy Logic Type 2

Ismail¹,

Bendary²

2016

IJEECS

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This paper presents 500 KW three phase standalone photovoltaic systems supplying pumping station consist of four pumps 80 KW rating. The system utilizes a two stage energy conversion power conditioning unit topology composed of a DC-DC boost converter and three level-three phase voltage source inverter (VSI). The Boost converter in this paper is designed to operate in continuous mode and controlled for maximum power point tracking (MPPT). In this paper, the performance of the pumps is improved by adapting the controller of MPPT using different techniques. The system is modeled and studied using MATLAB/Simulink.

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Maximum Power Point Tracking of Photovoltaic Generation Based on the Type 2 Fuzzy Logic Control Method

Cited by 18 publications

References 12 publications

Power Quality Enhancement based on Type 2 Fuzzy logic direct power control of a Microgrid connected photovoltaic system with battery storage feeding nonlinear load

Power Quality Enhancement based on Type 2 Fuzzy logic direct power control of a Microgrid connected photovoltaic system with battery storage feeding nonlinear load

Study of the Intelligent Behavior of a Maximum Photovoltaic Energy Tracking Fuzzy Controller

FOPID Controller Based AC Pump Supplied from PV Standalone Source Tuned using Fuzzy Logic Type 2

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