Wind energy maximum power extraction algorithms: A review

Bhandare, Aniket; Bandekar, P. J.; Mane, Shital

doi:10.1109/iceets.2013.6533435

Cited by 11 publications

(5 citation statements)

References 52 publications

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“…the turbine power. This HCS is an old method for tracking the MPPT due to its simplicity, adaptability and very flexible [88] in varying its output due to a change in wind velocity [87]. There are few problems with this HCS algorithm and they are overcome by implementing the adaptive step and the modified HCS algorithms.…”

Section: Hill-climbing Searchmentioning

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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Section: Hill-climbing Searchmentioning

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

View full text Add to dashboard Cite

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“…4. There are many different MPPT methods in practice [16][17][18][19], but all the dynamic couplings can be divided into two categories: proportional-integral (PI)-controlled coupling (if the dynamic coupling existed between the PI controller and the VSG active power control loop) and non-PI-controlled coupling. The energy participating in the inertial response would come from the DC bus capacitors if the MPPT control is implemented in the MSC (MSC MPPT control); otherwise, with the GSC MPPT control, the energy would be from the WT rotor.…”

Section: Dynamic Coupling In the Wecs-vsgmentioning

confidence: 99%

“…Xi et al [15] reveal that the characteristic differences between the WECS-VSG and the SG result from the dynamic coupling due to the different control loops in the WECS-VSG and it discussed the coupling effects between the power speed feedback (PSF)-based maximum power point tracking (MPPT) control and the VSG active power control loops. Other popular MPPT schemes, such as described in [16][17][18][19][20][21][22], are not addressed. Typical MPPT methods can be divided into the ones without wind speed detection, such as PSF control [19], perturbation and observation (P&O) [20] and optical torque control (OT) [21] or with wind speed detection, e.g.…”

Section: Introductionmentioning

confidence: 99%

Inertial response characteristics analysis and optimisation of PMSG‐based VSG‐controlled WECS

Geng

et al. 2018

IET Renewable Power Generation

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In this study, the inertial response characteristics of the permanent magnetic synchronous generator (PMSG)-based wind energy conversion system (WECS) with different virtual synchronous generator (VSG) schemes are analysed and compared in detail. It is found that the dynamic coupling between the VSG active power control loop and the other control function, e.g. maximum power point tracking control and direct current bus voltage control, in VSG-controlled WECS (WECS-VSG) is inevitable. The coupling would result in the variations of the equivalent inertia constant and damping coefficient. The frequency support capability of the WECS-VSG is thus weakened and the WECS-VSG is even harmful to grid frequency support under some conditions. Besides, the requirement of the smooth energy recovery stage is usually put forward by the power system operator for the characteristics of the energy sources for inertial response (ESIRs) in WECS. To regain the expected synchronous generator alike inertial response characteristics and follow the ESIR property, a generalised optimal WECS-VSG control strategy which can be embedded into different WECS-VSG schemes is proposed and two examples for different ESIRs are designed. Finally, the effectiveness of the analysis and the proposed strategy are verified by simulation results.

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“…Maximum power point tracking (MPPT) algorithms are the part of the control that seeks the most efficient operation point continuously [3]. In the WECS case, the operation point is the pair wind speed/rotor speed, which gives us the value.…”

Section: (2)mentioning

confidence: 99%

A neural adaptative step size method for maximum power point tracking in low power wind turbine systems

Wattes

Praça

Braga

et al. 2015

2015 IEEE 13th Brazilian Power Electronics Conference and 1st Southern Power Electronics Conference (COBEP/SPEC)

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This paper aims to present an algorithm based on artificial neural networks, with Multilayer Perceptron architecture, capable of tracking the maximum power point of a wind turbine system: (i) without the use of any mechanical wind speed sensors and (ii) with less oscillatory steady state. The proposed method it is based on the traditional Perturb & Observe algorithm. However, it uses the neural network to learn a strategy to define the best action (the update step size) for each operation point. The learning is done through backpropagation. To validate the proposed algorithm, it is shown simulations made on Simulink/Matlab software. The simulations consists of a generic wind turbine of 1kW. The average values of generated electric power are the performance index.

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Wind energy maximum power extraction algorithms: A review

Cited by 11 publications

References 52 publications

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

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

Inertial response characteristics analysis and optimisation of PMSG‐based VSG‐controlled WECS

A neural adaptative step size method for maximum power point tracking in low power wind turbine systems

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