Development of self-adaptive P&amp;O MPPT algorithm for wind generation systems with concentrated search area

Youssef, Abdel‐Raheem; Mousa, Hossam H. H.; Mohamed, Essam E. M.

doi:10.1016/j.renene.2020.03.050

Cited by 50 publications

(26 citation statements)

References 39 publications

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“…In the IPC technique, the power is pre-calculated using the wind speed plots, which maximizes the mechanical power P wind , while in the DPC scheme, the electrical power (P ele ) is directly examined to operate the WT at the MPP. The DPC control scheme includes the MPPT algorithms such as perturb and observe (P&O) [46], incremental conductance (INC) [47], and optimal relation-based (ORB) [36,48]. An overview of several MPPT algorithms is presented in this section.…”

Section: Mppt Algorithms For Wecsmentioning

confidence: 99%

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A Review of Maximum Power Point Tracking Algorithms for Wind Energy Conversion Systems

Pande

Nasikkar

Kotecha

et al. 2021

JMSE

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Renewable energy resources are gaining a lot of popularity. Several researchers have worked on the tracking and extraction of energy from these sources. In the past few decades, among the available green energy resources, wind energy has been the most attractive option among the resources available. It is imperative to use the maximum power available in the wind to achieve the wind turbine (WT) operation at maximum power. The maximum power point tracking (MPPT) algorithms are a pioneer in this context. Many research papers are contributed in this domain which necessitates a thorough review while choosing an appropriate technique. This paper comprehensively focuses on reviewing different algorithms in the past and present for tracking maximum power point, and capturing maximized output power from the wind energy conversion system (WECS). In this paper, the algorithms are classified based on the direct and indirect power measurement, hybrid and smart algorithms for tracking maximum power point, and they are compared, considering the parameters like complexity, convergence speed, use of sensors, memory requirement, need for knowledge of system parameters, etc. The immense popularity of the different versions of perturb and observe (P&O) based algorithms due to their various features is evident from the literature. The review reveals that the hybrid maximum power point tracking algorithms can use the advantages of the conventional methods and eliminate their drawbacks.

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Section: Mppt Algorithms For Wecsmentioning

confidence: 99%

“…Adaptive step size [48,69,76,114] Depending on the definite objective function, accurate clarification of the relation among the control variables, and wind speed, the step sizes are varied. The objective function can be dependent on multiple constants.…”

Section: Generated Step Size Type Reference Remarksmentioning

confidence: 99%

A Review of Maximum Power Point Tracking Algorithms for Wind Energy Conversion Systems

Pande

Nasikkar

Kotecha

et al. 2021

JMSE

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“…The summary of review results related to the recent studies of maximization of output power of the wind turbine is [17] Maximum power harvesting RBFNN control strategy of MPPT of WECS 0.5200 [23] Search maximum power point (SMPP) of variable-speed WECS SELF-ADAPTIVE perturb and observe algorithm for MPPT 0.4800 [24] Optimize and control the HAWT PSO with a neuro-fuzzy controller 0. 4669 [25] MPPT for wind turbine Optimal torque control 0.4800 [26] Optimize power capture by wind turbine RBFNN technique 0.4800 [27] Wind turbine output power improvement fuzzy inference based Generator torque control 0.4800 [28] Maximize output power of SCIG based WECS Fuzzy Logic Controller 0.4700 [29] Extract maximum power from the wind control via ANN-PSO for MPPT of small wind turbine 0.4750 [30] Extract maximum energy from the wind Fuzzy logic control based on HCSM 0.4550 [31] MPPT for WECS control via GA Optimization 0.4800 [32] WECS output power maximization Direct torque control of WT driven DFIG 0.4900…”

Section: Related Literature Reviewmentioning

confidence: 99%

Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System

Haile¹,

Worku²,

Beyene³

et al. 2021

ACIS

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Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (C p ) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient C p of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.

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“…is a nonlinear function of tip speed ratio and pitch angle, and its mathematical model can be obtained according to the method of numerical approximation calculation [15]- [16] According to (1), when the wind speed is stable, the active power obtained by the wind turbine is proportional to the wind energy utilization coefficient . The higher the wind energy utilization coefficient is, the higher the wind energy conversion efficiency will be, and the greater the power of the wind turbine will be.…”

Section: A Wind Power Generation System Model and Maximum Power Tracmentioning

confidence: 99%

Robust Adaptive Control of Maximum Power Point Tracking for Wind Power System

Chen

Han

2020

IEEE Access

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Development of self-adaptive P&O MPPT algorithm for wind generation systems with concentrated search area

Cited by 50 publications

References 39 publications

A Review of Maximum Power Point Tracking Algorithms for Wind Energy Conversion Systems

A Review of Maximum Power Point Tracking Algorithms for Wind Energy Conversion Systems

Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System

Robust Adaptive Control of Maximum Power Point Tracking for Wind Power System

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