Selection of optimal controller for active and reactive power control of doubly fed induction generator (DFIG)

Bhutto, Darya Khan; Ansari, Jamshed Ahmed; Chachar, Faheem Akhtar; Katyara, Sunny; Soomro, Jahangeer Badar

doi:10.1109/icomet.2018.8346416

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Recently, one of the most eco-friendly and accessible renewable energy sources (RESs) being utilized all across the world is wind energy [21]. Considering the beneficial characteristic of clean energy resources such as flexible control and regulation, RESs' expansion programs and sustainable growth to reduce the greenhouse gas emissions are the main purposes of European Commission in Energy Road map 2050 [22]. Regarding the necessity of employing renewable energies, and also the remarkable global growth in the use of such energy sources, wind energy has important advantages such as zero-emission energy production and low operating costs.…”

Section: Active and Reactive Power Control In The Ocean Wind Energy Smentioning

confidence: 99%

“…Forasmuch as fuzzy sets are defined based on linguistic variables, thus the T2FS is appropriate to model uncertainty process using linguistic variable. The primary membership grade in T1FSs is a crisp number in [0, 1], whereas the primary membership grade in T2FSs is a T1FSs in range of 13 Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges DOI: http://dx.doi.org /10.5772/intechopen.88715 [0, 1] and the secondary membership is a crisp number in [0, 1] as well [17,22]. Figure 6 represents T2FSs with its upper, lower, and footprint ranges of the uncertainty.…”

Section: Extended Fuzzy Setsmentioning

confidence: 99%

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Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Gandoman¹,

Ahmadi²,

Aleem³

et al. 2020

Advances in Modelling and Control of Wind and Hydrogenerators

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Wind energy is one of the most important sources of energy in the world. In recent decades, wind as one of the massive marine energy resources in the ocean to produce electricity has been used. This chapter introduces a comprehensive overview of the efficient ocean wind energy technologies, and the global wind energies in both offshore and onshore sides are discussed. Also, the classification of global ocean wind energy resources is presented. Moreover, different components of a wind farm offshore as well as the technologies used in them are investigated. Possible layouts regarding the foundation of an offshore wind turbine, floating offshore, as well as the operation of wind farms in the shallow and deep location of the ocean are studied. Finally, the offshore wind power plant challenges are described.

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Section: Active and Reactive Power Control In The Ocean Wind Energy Smentioning

confidence: 99%

Section: Extended Fuzzy Setsmentioning

confidence: 99%

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Gandoman¹,

Ahmadi²,

Aleem³

et al. 2020

Advances in Modelling and Control of Wind and Hydrogenerators

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“…These systems are equipped with a DFIG generator. The variable speed turbine can tolerate some of the power variation due to turbulence by increasing the rotor speed, so that the power output at each wind speed can be controlled [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Power Optimization Control Scheme for Doubly Fed Induction Generator Used in Wind Turbine Generators

et al. 2020

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Scientists and researchers are exploring different methods of generating and delivering electrical energy in an economical and reliable way, enabling them to generate electricity focusing on renewable energy resources. All of these possess the natural property of self-changing behavior, so the connection of these separate independent controllable units to the grid leads to uncertainties. This creates an imbalance in active power and reactive power. In order to control the active and reactive power in wind turbine generators with adjustable speed, various control strategies are used to allay voltage and current variations. This research work is focused on the design and implementation of effective control strategies for doubly fed induction generator (DFIG) to control its active and reactive power. A DFIG system with its control strategies is simulated on MATLAB software. To augment the transient stability of DFIG, the simulation results for the active and reactive power of conventional controllers are compared with three types of feed forward neural network controllers, i.e., probabilistic feedforward neural network (PFFNN), multi-layer perceptron feedforward neural network (MLPFFN) and radial basic function feedforward neural network (RBFFN) for optimum performance. Conclusive outcomes clearly manifest the superior robustness of the RBFNN controller over other controllers in terms of rise time, settling time and overshoot value.

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“…Where Vds = stator voltage (direct axis) Vqs = stator voltage (quadrature axis) 𝜔𝑠 − 𝜔𝑠) ѱ𝑑𝑟(9) Where Vdr = stator voltage (direct axis) Vqr = stator voltage (quadrature axis)Stator flux components:[23] { ѱ𝑑𝑠 = 𝐿𝑠 𝐼𝑑𝑠 + 𝐿𝑚 𝐼𝑑𝑟 ѱ𝑞𝑠 = 𝐿𝑠 𝐼𝑞𝑠 + 𝐿𝑚 𝐼𝑞𝑟(10) Where Ls = self-inductance on stator side Lm = mutual inductance Rotor flux components:[23] { ѱ𝑑𝑟 = 𝐿𝑟 𝐼𝑑𝑟 + 𝐿𝑚 𝐼𝑑𝑠 ѱ𝑞𝑟 = 𝐿𝑟 𝐼𝑞𝑟 + 𝐿𝑚 𝐼𝑞𝑠(11)Where Lr = self-inductance on rotor side DFIG electromagnetic torque:[23] …”

mentioning

confidence: 99%

Tilt Integral Derivative Controller Optimized by Battle Royale Optimization for Wind Generator Connected to Grid

Samir

Singh

Ahamad

2022

IJEEI

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Globally the countries are focusing on reducing the carbon footprint leading to a greater effort for electrical energy generation by renewable energy sources, particularly wind. The wind turbines are invariably using doubly fed asynchronous generator. In this paper a controller has been designed for a doubly fed induction motor. The proposed Tilt Integral Derivate controller for was compared with commonly used PI, PID controllers. Several optimization algorithms were used for tuning of controllers and the best one was selected for each type of controller. The controller has been optimized using battlefield optimization. It had been compared with proportional integral controller, fractional order proportional integral derivative controller. Other controllers were optimized using meta heuristic algorithms. The controller enhanced the system response in terms of settling time, rise time and other parameters. The Tilt controller gave the overall superior performance in terms of parameters like rise time, settling time, settling minimum, peak, and peak time. The results were obtained using MATLAB. This paper discusses operation of doubly fed induction motor operation and optimization methods.

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Selection of optimal controller for active and reactive power control of doubly fed induction generator (DFIG)

Cited by 7 publications

References 12 publications

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Power Optimization Control Scheme for Doubly Fed Induction Generator Used in Wind Turbine Generators

Tilt Integral Derivative Controller Optimized by Battle Royale Optimization for Wind Generator Connected to Grid

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