Control and Comparison of Power for a Variable-Speed Wind Turbine Using Fuzzy PID Controller

Sahoo, Satyabrata; Panda, Gayadhar

doi:10.1007/978-981-16-4369-9_38

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Lin Pan and Xudong Wang also proposed a repetitive control combined with a Takagi-Sugeno fuzzy PID pitch controller in order to stabilize the output power [34]. However, in [35] a comparison between three control schemes for the WT pitch control system was proposed, which are PID, fuzzy, and fuzzy-PID regarding generator power, torsional torque, and generator speed in the above-rated wind speed region. The PID controller had a higher deviation in the three comparison aspects, while fuzzy and fuzzy-PID showed remarkable power fluctuation reduction and also a reduction in torsional torque.…”

Section: Pitch Control Techniquementioning

confidence: 99%

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Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review

Elkodama,

Ismaiel,

Abdellatif

et al. 2023

Energies

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In recent years, the increasing environmental problems, especially the issue of global warming, have motivated demand for a cleaner, more sustainable, and economically viable energy source. In this context, wind energy plays a significant role due to the small negative impact it has on the environment, which makes it among the most widespread potential sustainable renewable fuel nowadays. However, wind turbine control systems are important factors in determining the efficiency and cost-effectiveness of a wind turbine (WT) system for wind applications. As wind turbines become more flexible and larger, it is difficult to develop a control algorithm that guarantees both efficiency and reliability as these are conflicting objectives. This paper reviews various control strategies for the three main control systems of WT, which are pitch, torque, and yaw control, in different operational regions considering multi-objective control techniques. The different control algorithms are generally categorized as classical, modern (soft computing) and artificial intelligence (AI) for each WT control system. Modern and soft computing techniques have been showing remarkable improvement in system performance with minimal cost and faster response. For pitch and yaw systems, soft computing control algorithms like fuzzy logic control (FLC), sliding mode control (SMC), and maximum power point tracking (MPPT) showed superior performance and enhanced the WT power performance by up to 5% for small-scale WTs and up to 2% for multi-megawatt WTs. For torque control systems, direct torque control (DTC) and MPPT AI-based techniques were suitable for reducing generator torque fluctuations and estimating the torque coefficient for different wind speed regions. Classical control techniques such as PI/PID resulted in poor dynamic response for large-scale WTs. However, to improve classical control techniques, AI algorithms could be used to tune the controller’s parameters to enhance its response, as a WT is a highly non-linear system. A graphical abstract is presented at the end of the paper showing the pros/cons of each control system category regarding each WT control system.

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Section: Pitch Control Techniquementioning

confidence: 99%

“…Pitch Classical [13][14][15]18,19,39] Modern [20][21][22][23][25][26][27][28][31][32][33][36][37][38][40][41][42][43][44][45]47,48,53] AI [16,17,24,29,34,35,46,[49][50][51][52][54][55][56] Torque Classical [60] Modern [58,59,62,63,[66][67][68][69]…”

Section: Control System Control Technique Referencesmentioning

confidence: 99%

Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review

Elkodama,

Ismaiel,

Abdellatif

et al. 2023

Energies

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Speed Control of Wind Turbines System using Evolutionary Algorithm based Cascaded Controller

Ahmad,

Gulzar,

Shakoor

et al. 2024

2024 International Conference on Control, Automation and Diagnosis (ICCAD)

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Enhancing pitch angle control in variable speed doubly fed induction generator based wind energy conversion system using advanced adaptive neuro-fuzzy approach with particle swarm optimization

Sahoo

2024

Wind Engineering

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Pitch angle control is a common method used to smooth out output power fluctuations in the wind turbines. This paper focuses on Particle Swarm Optimization (PSO) based Adaptive Neuro-Fuzzy Inference System (ANFIS) pitch angle control scheme for a variable speed Doubly Fed Induction Generator (DFIG) based wind energy conversion system designed for operation in high wind speed regions. The proposed enhanced adaptive controller comprises both Sugeno type fuzzy inference system (FIS) and neural network architecture. In the Sugeno type FIS, the membership functions are optimized using a revisited PSO method. The primary objective is to control the pitch angle to ensure that generator power and speed operate within desired references, reducing fluctuations and minimizing mechanical blade stress. A MATLAB/SIMULINK model of wind energy conversion system setup is prepared and simulations are conducted using different control methods. The effectiveness of the proposed approach is confirmed based on the simulation results.

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Control and Comparison of Power for a Variable-Speed Wind Turbine Using Fuzzy PID Controller

Cited by 3 publications

References 12 publications

Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review

Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review

Speed Control of Wind Turbines System using Evolutionary Algorithm based Cascaded Controller

Enhancing pitch angle control in variable speed doubly fed induction generator based wind energy conversion system using advanced adaptive neuro-fuzzy approach with particle swarm optimization

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