MPPT control of wind turbines by direct adaptive fuzzy-PI controller and using ANN-PSO wind speed estimator

Sabzevari, Sanaz; Karimpour, Ali; Monfared, Mohammad; Sistani, Mohammad Bagher Naghibi

doi:10.1063/1.4973447

Cited by 40 publications

(11 citation statements)

References 30 publications

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“…The new wind turbines are usually utilize the AC/DC and DC/AC converters as the interfaces between the generators and the distribution networks [27] and [28]. In the case of higher power requirements, an appropriate option is to use the multi-layer converter topologies [29] and [30].…”

Section: Component Modeling a Converter Modelmentioning

confidence: 99%

Shapley Value-Based Techno-Economic Framework for Harmonic and Loss Mitigation

Nazari

Azad‐Farsani

2019

IEEE Access

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This study presents a multi-objective policy for harmonic distortion and loss mitigation in micro-grid and active distribution grid. The implementation of this policy in networks including Distributed Generation (DG), is put forward based on the determination of the locational marginal price of each DG bus, considering their impacts on the loss and harmonic mitigation. In this process, each DG receives an incentive in the form of price increment, based on the Aumann-Shapley value game-theoretic method as an iterative algorithm, considering the effectiveness of reducing loss or harmonic distortion. Furthermore, as a decision-making tool, the operator can change the contribution of the incentives used to mitigate the loss or harmonic distortion and make an appropriate estimation for the next step conditions. The simulations are organized based on real data on the modified Taiwan Power Company grid (TPC). The modified TPC consists of renewable energy sources (RES) and fossil-fuel based DG units. Also, to achieve a realistic framework, generation of RES, loads, and market prices are considered as uncertain parameters. The results demonstrate the competence of the proposed method for the TPC network in terms of triple pricing methods comparison, total loss reduction, harmonic mitigation, and merchandising surplus controlling.

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Section: Component Modeling a Converter Modelmentioning

confidence: 99%

Shapley Value-Based Techno-Economic Framework for Harmonic and Loss Mitigation

Nazari

Azad‐Farsani

2019

IEEE Access

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“…The control algorithm allows reaching and tracking the maximum power point MPP at all wind velocities. Over the years, several control methods have been proposed for extracting maximum power from wind to overcome various constraints such as Optimal Tip Speed Ratio Control [4,5] has developed nonlinear proportional complex integral (PCI), fuzzy controller [6,7], artificial neural networks (ANN) [8], robust control [9], DTC based [10], direct adaptive fuzzy-PI controller and using ANN-PSO wind speed estimator [11], robust adaptive neural controller [12,13] has proposed Experimental enhancement of fuzzy fractional order PI+I controller of grid connected variable speed wind energy conversion system. This study presents a FFOPI controlled MPPT system suitable for the permanent magnet synchronous generator operating at variable speeds.…”

Section: Introductionmentioning

confidence: 99%

MPPT control design for variable speed wind turbine

Rachedi

Saidi

Arbaoui

2020

IJECE

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Variable speed wind turbine systems (VSWT’s) have been in receipt of extensive attention among the various renewable energy systems. The present paper focuses on fuzzy fractional order proportional-integral (FFOPI) control segment for variable speed wind turbine (VSWT) directly driving permanent magnet synchronous generator (PMSG). The main objective of this study is to reach maximum power point tracking (MPPT) through combination of advanced control based on FFOPI control applied to generator side converter (turbine and PMSG). The basic idea of the FFOPI controller is to implement a fuzzy logic controller (FLC) in cascade with Fractional Order Proportional Integral controller (FOPI). A comparative study with FOPI and classical PI control schemes is made. The traditional PI controller cannot deliver a sufficiently great performance for the VSWT. However, the results found that the proposed approach (FFOPI) is more effective and feasible for controlling the permanent magnet synchronous generator to mantain maximum power extraction. The validation of results has been performed through simulation using Matlab/Simulink®.

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“…There are few more methods that depend on this, but not completely, i.e. they require the historical data pertaining to the wind speed of performing that method and they are fuzzy logic control (FLC), neural network (NN) base [24] and direct adaptive fuzzy proportional-integral (PI) controller methods [25].…”

Section: Introductionmentioning

confidence: 99%

Review on control techniques and methodologies for maximum power extraction from wind energy systems

Kumar

Saravanan

Padmanaban

et al. 2018

IET Renewable Power Generation

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This review study focuses on various methods and technologies used in past and present for obtaining maximum output power from a wind energy conversion system. There are plenty of solution for maximum power point (MPP), but the problem lies in the effective choice made among them and it needs the expert knowledge on every technique for picking up the best MPP method as every method on its own has some advantages and disadvantages. A comparison has been made among various MPP methods in terms of convergence time, efficiency, training, complexity and wind measurement. Here, different MPP tracking (MPPT) algorithms are classified based on wind speed measurement (WSR) and without WSR models. In this study, from the literature, a novel maximum electrical power tracking (MEPT) and maximum mechanical power tracking (MMPT) methods are compared with state-of-the-art MPPT algorithms. On basis of the results obtained from the literature available, the MEPT algorithm has fast convergence rate of 15 ms; on the other hand, optimal relation-based method is having large convergence rate of 364 ms and less efficient. A case study has been considered for performance validation, and MEPT and MMPT are having a good response for dynamic variation in wind speed.

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MPPT control of wind turbines by direct adaptive fuzzy-PI controller and using ANN-PSO wind speed estimator

Cited by 40 publications

References 30 publications

Shapley Value-Based Techno-Economic Framework for Harmonic and Loss Mitigation

Shapley Value-Based Techno-Economic Framework for Harmonic and Loss Mitigation

MPPT control design for variable speed wind turbine

Review on control techniques and methodologies for maximum power extraction from wind energy systems

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