Fractional Control Design of Renewable Energy Systems

Singh, Shashi Kant; Tayal, Vijay Kumar; Singh, H. P.; Yadav, Vinod Kumar

doi:10.1109/icrito48877.2020.9197902

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…Applications of FOC controllers are various with some realizations in research laboratories and real processes supervision like in robotic manipulators [6], [7], renewable energy systems [8], chaotic systems [9], vehicle control [10]. The concept of non-integer differentiation was developed far in the past and was recognized in the mathematics research community.…”

Section: Introductionmentioning

confidence: 99%

Automatic voltage regulator performance enhancement using a fractional order model predictive controller

Deghboudj

Ladaci

2021

Bulletin EEI

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In this paper, a new design method for fractional order model predictive control (FO-MPC) is introduced. The proposed FO-MPC is synthesized for the class of linear time invariant system and applied for the control of an automatic voltage regulator (AVR). The main contribution is to use a fractional order system as prediction model, whereas the plant model is considered as an integer order one. The fractional order model is implemented using the singularity function approach. A comparative study is given with the classical MPC scheme. Numerical simulation results on the controlled AVR performances show the efficiency and the superiority of the fractional order MPC.

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Section: Introductionmentioning

confidence: 99%

Automatic voltage regulator performance enhancement using a fractional order model predictive controller

Deghboudj

Ladaci

2021

Bulletin EEI

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“…where G c (s) is controller transfer function, e(s)error, c(s)output. The term λ and μ are the fractional of integral [20] parts and derivative parts [21].…”

Section: Fopid Controllermentioning

confidence: 99%

“…Shun-Cheung wang et al (2020) suggest a simple and efficient maximum power tracking method that combines simplified model-based state estimation (SMSE) with the adaptive alpha perturb-and-observe (P&O) method. Swati Singh et al (2020) represented that review of optimization and modeling methodologies, as well as the interface mechanism of various renewable energy sources with various control designs such as the Fuzzy Controller, Fractional Order FPID (Fuzzy PID) Controller, and FOPID (Fractional Order PID) Controller. Majid Dehghani et al (2021) presented that to achieve the full PowerPoint; a fuzzy logic controller (FLC) was optimized by a combination of particle swarm optimization (PSO) and genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy FoPID Controller for Energy Reshaping in Grid Connected PV Inverters for Electric Vehicles

Manjusha¹,

Sivarani²,

Jerusalin³

2022

Intelligent Automation &Amp; Soft Computing

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By utilizing Fuzzy based FOPID-controller, this work is designed via the energy reshaping concept for Grid connected Photovoltaic (PV) systems for electric vehicles and this PV module has its own inverter which is synchorised with the utility grid. In grid connected PV system, the mitigation plays an important role where the capacity of PV arrays increases it maintains power quality and it is necessary to comply with some requirements such as harmonic mitigation. Unless a maximum power point tracking (MPPT) algorithm is used, PV systems do not continuously produce their theoretical optimal power. Under various atmospheric conditions, MPPT is obtained by using Perturb and Observe (P&O) techniques. A storage function in terms of DC-link voltage and current, and q-axis current is created, by means of which physical characteristics are studied. Further the energy reshaping is done with the aid of the proposed Fuzzy Fractional Order PID (Fuzzy-FoPID) framework. The FOPID control parameters λ and μ are optimally tuned by Grey Wolf Optimization technique (GWO). The results obtained verify the advantages of using Fuzzy-FoPID control as compared to conventional PID (Proportional Integral Derivative) control and FoPID control. The injected reactive power into the grid and the efficiency of the proposed systems is studied by extensive simulations.

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“…Micro-grid units are delivered by several micro sources; the high step-up converters can be utilized for increasing the output voltage of a micro source to 380-400 V for dc interfaces to a primary electrical source via the Direct Current (DC) to alternating current (AC) inverter [4]. The fuel cell stack, as well as the single solar cell module, were low-voltage resources, and therefore, a high stepup voltage gain DC-DC converter was needed for regulating the voltage of DC-DC interfaces [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

Hema¹,

Sukhi²

2023

Computer Systems Science and Engineering

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Smart grids and their technologies transform the traditional electric grids to assure safe, secure, cost-effective, and reliable power transmission. Non-linear phenomena in power systems, such as voltage collapse and oscillatory phenomena, can be investigated by chaos theory. Recently, renewable energy resources, such as wind turbines, and solar photovoltaic (PV) arrays, have been widely used for electric power generation. The design of the controller for the direct Current (DC) converter in a PV system is performed based on the linearized model at an appropriate operating point. However, these operating points are everchanging in a PV system, and the design of the controller is usually accomplished based on a low irradiance level. This study designs a fractional-order proportional-integrated-derivative (FOPID) controller using deep learning (DL) with quasi-oppositional Archimedes Optimization algorithm (FOPID-QOAOA) for cascaded DC-DC converters in micro-grid applications. The presented FOPID-QOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter. In addition, the proposed model develops a FOPID controller using a stacked sparse autoencoder (SSAE) model to regulate the converter output voltage. To tune the hyper-parameters related to the SSAE model, the QOAOA is derived by the including of the quasi-oppositional based learning (QOBL) with traditional AOA. Moreover, an objective function with the including of the integral of time multiplied by squared error (ITSE) is considered in this study. For validating the efficiency of the FOPID-QOAOA method, a sequence of simulations was performed under distinct aspects. A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.

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Fractional Control Design of Renewable Energy Systems

Cited by 11 publications

References 31 publications

Automatic voltage regulator performance enhancement using a fractional order model predictive controller

Automatic voltage regulator performance enhancement using a fractional order model predictive controller

Application of Fuzzy FoPID Controller for Energy Reshaping in Grid Connected PV Inverters for Electric Vehicles

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

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