Pitch Angle Control for Variable Speed Wind Turbine Using Fuzzy Logic

Sahoo, Satyabrata; Subudhi, Bidyadhar; Panda, Gayadhar

doi:10.1109/icit.2016.019

Cited by 23 publications

(14 citation statements)

References 9 publications

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“…The outputs of the fuzzy logic controller are desired pitch angle (θ d ) and reference generator torque (T gr ). The details of fuzzy sets for error, change in error and output are as per reference [7].…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

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

Sahoo

Panda

2021

Advances in Intelligent Systems and Computing

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Output power fluctuation of a wind turbine depends on third power of wind speed, which leads to power quality problem. Pitch angle control method is one of the common methods that is employed for smoothing of the output power fluctuations. This paper focuses on design of proportional-integral-derivative control (PID control), fuzzy logic control (FLC) and fuzzy PID control for control of wind energy conversion system (WECS) through pitch angle. The control objective here is that by controlling the pitch angle and generator torque, the generator power regulation is optimized. The MATLAB-Simulink models have been developed for simulation of the control schemes. From the obtained results, it is found that controller exhibits excellent response.

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Section: Fuzzy Logic Controllermentioning

confidence: 99%

“…In fact, in wind energy conversion technology problems [6][7][8][9], fuzzy control is an appropriate choice through pitch angle control implementation.…”

Section: Introductionmentioning

confidence: 99%

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

Sahoo

Panda

2021

Advances in Intelligent Systems and Computing

Self Cite

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“…The emphasis is that the non-linearity of the power curve of the wind turbine will result in greater error amplification, and a smaller deviation from the wind speed will affect a larger power deviation. As a result, since a large amount of wind energy can soften the output power curve, a proper wind farm aggregation approach is desired to perform further prediction tests [104]. Authors [105], describe several models of statistical forecasting, named the autoregressive mechanically variant moving average (ARMA) approach, to calculate speed wind and energy addition in the first-hour markets.…”

Section: Evaluation Of Wind Speed and Power Forecastsmentioning

confidence: 99%

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

et al. 2020

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To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

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“…Hassan et al [16], Elfergani et al [43], and Naik and Gupta [52] repeat the same control strategy used in PMSG for a SCIG. However, [40] and [53] also combine torque control at the same time as a pitch control for a DFIG. Renuka and Reji [54] proposed changing the input variables to wind speed (υ) and the error in the speed of rotation of the generator (eω) also for a DFIG motor.…”

Section: ) Fuzzy Rulesmentioning

confidence: 99%

Expert Control Systems Implemented in a Pitch Control of Wind Turbine: A Review

et al. 2019

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Wind energy is the strongest renewable energy source developed in recent decades. Being systems that are directly connected to the grid of the electrical system, it is essential to use the maximum available power of the wind and obtain the maximum electrical power converted from the turbine. In this paper, the fundamental problem of the wind turbine is how to obtain at all times the maximum output power of the turbine in a wide range of wind speed. The randomness of the wind adds an intrinsic difficulty to be able to plan the available wind energy in advance. To solve this problem, it is not necessary to know the dynamic operation of the system; we must anticipate the control response to each one of the different probable scenarios. An expert control system can be used based on human knowledge and experience, which, through proper management of its variables and adequate control of criteria to manipulate stored data, provides a way to determine solutions. In other words, it is a model of the experience of professionals in this field. The more variables in the system are considered, the more complete the model will be, and the more information will be available for decision-making, with a more efficient system and higher results in power generation as a response. For this reason, the objective of this paper is to present expert systems developed in recent years and, thus, offer a control solution that approximates the conditions of different wind turbines.INDEX TERMS Artificial neural network, fuzzy logic, genetic algorithms, wind power generation, control systems.

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Pitch Angle Control for Variable Speed Wind Turbine Using Fuzzy Logic

Cited by 23 publications

References 9 publications

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

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

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

Expert Control Systems Implemented in a Pitch Control of Wind Turbine: A Review

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