A Developed Integrated Scheme Based Approach for Wind Turbine Intelligent Control

Mahmoud, Tawfek; Zhang, Dong; Ma, Jin

doi:10.1109/tste.2016.2632104

Cited by 31 publications

(21 citation statements)

References 37 publications

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“…On the other hand, when the speed of the rotor is above its upper-speed limit, then the power adjustment of the WTG is achieved by controlling the pitch angle. According to the WT theory [23], the aerodynamic power output by WT can be expressed as…”

Section: Wind Turbines Modelling and Controlmentioning

confidence: 99%

“…The complexity of the integrated intelligent model impacts a significant challenge to the balance among the model accuracy and the system complexity when Matlab is integrated with PowerFactory [21,41]. The complete WF system is modelled, simulated, and the parameters are chosen and adjusted by using real power system containing a large WF and IEEE standard templates [21,23,39,40]. The following models interact with each other during simulation: PowerFactory DSL, Matlab m file and Matlab Simulink.…”

Section: Full Wf System Descriptionsmentioning

confidence: 99%

“…Soft sensorless estimates the real-time WS values for several locations based on historical measurements associated with real-time measurements. The CEEMDAN method is used for decomposing the WS time series into sets of IMFs [23,46], which called realisation process. The ApEn as a complex measured-based technique [23] is used for reconstructing WS IMFs realisations into three components; low wind/noise, trend, and cyclic components.…”

Section: Real-time Soft Sensorless Ws Estimation System Using Ceemdanmentioning

confidence: 99%

“…The CEEMDAN method is used for decomposing the WS time series into sets of IMFs [23,46], which called realisation process. The ApEn as a complex measured-based technique [23] is used for reconstructing WS IMFs realisations into three components; low wind/noise, trend, and cyclic components. Then the ELM [23] as a fast gradient free method is used for modelling real-time soft sensorless WS estimation for each component [23].…”

Section: Real-time Soft Sensorless Ws Estimation System Using Ceemdanmentioning

confidence: 99%

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Integrated optimal active and reactive power control scheme for grid connected permanent magnet synchronous generator wind turbines

Mahmoud

Zhang²,

2018

IET Electric Power Applications

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This research presents a novel approach for optimal active and reactive power control using permanent magnet synchronous generator with fully rated converter (PMSG-FRC). The power output variations and wind energy intermittent performance create significant challenges for power system integration, operation, and control. A fast and reliable sensorless optimal wind speed (WS) control system-based extreme learning machines and complete ensemble empirical mode decomposition with adaptive noise is developed. Also, an approximate entropy-based complexity measure is used for online WS estimation. The fuzzy controller system is used for calculating the optimal wind turbine (WT) speed, power, and torque, and then adjusts WT by the optimal speed value. Moreover, the wind farm (WF) active and reactive power controller is utilised for obtaining the active and reactive power reference values for every region/feeder. The system is applied and integrated to power system grid by using IEEE standard models, and the speed of WT is adjusted by the required active power level and the estimated WS value to increase the maximum power capture from WF. The simulations results using different cases studies and power system voltage levels have confirmed the validity and accuracy of the proposed control algorithms for practical applications and demonstrated excellent performance for power system integration.

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Section: Wind Turbines Modelling and Controlmentioning

confidence: 99%

Section: Full Wf System Descriptionsmentioning

confidence: 99%

Section: Real-time Soft Sensorless Ws Estimation System Using Ceemdanmentioning

confidence: 99%

Section: Real-time Soft Sensorless Ws Estimation System Using Ceemdanmentioning

confidence: 99%

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Integrated optimal active and reactive power control scheme for grid connected permanent magnet synchronous generator wind turbines

Mahmoud

Zhang²,

2018

IET Electric Power Applications

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“…In ELM, the hidden layer parameters are randomly selected and the output layer parameters are determined by minimum norm least squares estimation methods, resulting in negligible computation time and better generalization. In Mahmoud et al, an ELM‐based intelligent control technique is proposed to increase the extracting energy as well as to improve the power system performance using signal decomposition and grouping. It develops an optimal wind turbine control with ensemble prediction technique by combining the Complimentary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) wind speed decomposition and ELM.…”

Section: Introductionmentioning

confidence: 99%

Control for grid-connected DFIG-based wind energy system using adaptive neuro-fuzzy technique

Shihabudheen

Raju²,

Pillai

2018

Int Trans Electr Energ Syst

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Summary Smooth operation and control of power electronic converters are essential to ensure wind energy systems in compliance with modern grid codes. This paper proposes an intelligent adaptive control strategy for doubly fed induction generator–based wind energy system using recently proposed extreme learning adaptive neuro‐fuzzy inference system (ELANFIS). ELANFIS is a type of neuro‐fuzzy systems, which combines erudition capabilities of extreme learning machine and unambiguous knowledge of fuzzy systems. In ELANFIS, premise parameters are generated randomly with restraints to house fuzziness and consequent parameters are identified using Moore‐Penrose generalized inverse method. The vector control with proposed ELANFIS control strategy is tested under various contingencies and is able to handle the uncertainties in the wind speed and grid disturbance. The performance of the proposed technique is verified through real‐time digital simulator with hardware in loop configuration.

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A novel fractional‐order nonlinear PID ‐based pitch angle control strategy for a PMSG ‐based wind energy system

Pathak

Gaur

2020

Int Trans Electr Energ Syst

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The mechanical power of the wind turbine has a cubic relationship with wind speed. Therefore, during high wind speed, the output power of wind turbine driven permanent magnet synchronous generator (PMSG) needs to be maintained at rated value effectively. This can be achieved by the pitch angle control of the wind turbine. In this paper, a new application of the fractionalorder nonlinear proportional-integral-derivative (FONPID) controller is pro

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A Developed Integrated Scheme Based Approach for Wind Turbine Intelligent Control

Cited by 31 publications

References 37 publications

Integrated optimal active and reactive power control scheme for grid connected permanent magnet synchronous generator wind turbines

Integrated optimal active and reactive power control scheme for grid connected permanent magnet synchronous generator wind turbines

Control for grid-connected DFIG-based wind energy system using adaptive neuro-fuzzy technique

A novel fractional‐order nonlinear PID ‐based pitch angle control strategy for a PMSG ‐based wind energy system

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