Conventional, Intelligent, and Fractional-Order Control Method for Maximum Power Point Tracking of a Photovoltaic System: A Review

Kamal, Nashwa Ahmad; Ibrahim, Ahmed M.

doi:10.1016/b978-0-12-816152-4.00020-0

Cited by 18 publications

(11 citation statements)

References 200 publications

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“…Many computational requests for fractional order derivatives according to the definition have been suggested by Riemann-Liouville and Grunwald-Letnikov, [9]. e general form of fractional order differentiator can be expressed by Kamal and Ibrahim [21]; supposed that f m (t) � t m and m � 1, 2, 3, . .…”

Section: Fractional Order Inc Methods (Fo-inc)mentioning

confidence: 99%

Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

et al. 2019

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This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. The proposed FO-INC controls the output voltage of the PV arrays to obtain maximum power point tracking (MPPT). Due to its simplicity and efficiency, the incremental conductance MPPT (INC-MPPT) is one of the most popular algorithms used in the PV scheme. However, owing to the nonlinearity and fractional order (FO) nature of both PV and DC-DC converters, the conventional INC algorithm provides a trade-off between monitoring velocity and tracking precision. Fractional calculus is used to provide an enhanced dynamical model of the PV system to describe nonlinear characteristics. Moreover, three metaheuristic optimization techniques are applied; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and AntLion Optimizer (ALO) are used for tuning the FO parameters of the proposed INC-MPPT. A MATLAB-Simulink-based model of the PV and optimization have been developed and simulated for different INC-MPPT techniques. Different techniques aim to control the boost DC-DC converter towards the MPP. The proposed optimization algorithms are, also, developed and implemented in MATLAB to tune the target parameters. Four performance indices are also introduced in this research to show the reliability of the comparative analysis of the proposed FO-INC with metaheuristic optimization and the conventional INC-MPPT algorithms when applied to a dynamical PV system under rapidly changing weather conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FO-INC as a MPPT control for different climatic conditions with disturbance rejection and robustness analysis.

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Section: Fractional Order Inc Methods (Fo-inc)mentioning

confidence: 99%

Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

et al. 2019

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“…A switched-mode DC-DC converter is mostly connected at the output of the PV panel and converts the varying voltage to a constant voltage required to charge the battery load. The duty cycle of the converter is varied using PWM by the MPPT algorithm to get the maximum possible output from the PV panel [97]. A PV inverter converts the DC to a fixed AC output for AC loads.…”

Section: A Photo-voltaic Systemsmentioning

confidence: 99%

“…A diode with two resistors is used to represent the non-linearities inside the PV cell. The single diode model of a PV cell is shown in Figure 9 [97]. It is seen that the current generated by the solar cell depends on temperature and irradiance [98].…”

Section: A Photo-voltaic Systemsmentioning

confidence: 99%

Fractional Order Control of Power Electronic Converters in Industrial Drives and Renewable Energy Systems: A Review

Warrier

Shah

2021

IEEE Access

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The power electronics industry is undergoing a revolution driven by an industry 4.0 perspective, with smart and green/hybrid energy management systems being the requirement of the future. There is a need to highlight the potential of fractional order control in power electronics for the highly efficient systems of tomorrow. This paper reviews the developments in fractional order control in power electronics ranging from stand-alone power converters, industrial drives and electric vehicles to renewable energy systems and management in smart grids and microgrids. Various controllers used in power electronics such as the fractional order PI/PID (FOPI/FOPID) and fractional-order sliding mode controllers have been discussed in detail. This review indicates that the plug-and-play type of intelligent fractional order systems needs to be developed for our sustainable future. The review also points out that there is tremendous scope for the design of modular fractional-order intelligent controllers. Such controllers can be embedded into power converters, resulting in smart power electronic systems that contribute to the faster and greener implementation of industry 4.0 standards. INDEX TERMS Fractional calculus, power electronic converters, fractional order control, industrial drives, electric vehicles, renewable energy applications, smart grids and microgrids, industry 4.0.

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“…The classical logic based on these precise expressions that are the standard set of truth values (degrees) consists of (0,1), where 0 represents "totally false" and 1 represents "totally true". As opposed to the classical logic, the values 0 and 1 are seen as boundary values, not absolute values in fuzzy (Kamal & Ibrahim, 2018). The truth values may take any values in this range.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Detection of COVID-19 Cases with Fuzzy Classifiers Using Chest Computed Tomography

Kokten

Kılıç

2021

European Journal of Science and Technology

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The novel coronavirus 2019 (COVID-19) is still spreading rapidly since it first appeared in Wuhan city of China in December 2019, resulting in a worldwide pandemic. Early detection of positive cases plays a key role in preventing the further spread of the epidemic which leads to the development of diagnostic methods that give rapid and accurate responses for the detection of COVID-19. Previous studies confirmed that chest computed tomography (CT) is an indispensable tool for early screening and diagnosing of COVID-19 cases. As a result of examinations on CT scans, a radiological finding that is called ground-glass opacity, causing color, and texture change, was found in the lung of a person with COVID-19. Due to the carelessness of radiologists who work long hours and the misdiagnosis resulting in confusion of the findings with different diseases, an automatic system that helps radiologists is needed. In this paper, we present a new approach based on fuzzy classification for the detection of COVID-19 using 3D CT volumes. In the proposed approach, the skewness, kurtosis, and average statistical features of 3D CT images of patients consisting of two classes, COVID and Normal, are calculated and the value ranges are determined for both classes. Three statistical features and value ranges are used as membership functions in the development of fuzzy logic classifier. The proposed approach provides rapid and accurate diagnostics in terms of COVID vs. Normal (binary classification) under a user-friendly interface. Experimental evaluations demonstrate that our approach has great potential for radiologists to validate their initial screening and improve early diagnosis, isolation, and treatment, which contributes to infection prevention and control of the epidemic.

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Conventional, Intelligent, and Fractional-Order Control Method for Maximum Power Point Tracking of a Photovoltaic System: A Review

Cited by 18 publications

References 200 publications

Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

Fractional Order Control of Power Electronic Converters in Industrial Drives and Renewable Energy Systems: A Review

Detection of COVID-19 Cases with Fuzzy Classifiers Using Chest Computed Tomography

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