Output Power Leveling of Wind Farm Using Pitch-angle Control with Fuzzy Neural Network

Senjyu, Tomonobu; Sakamoto, Ryosei; Kaneko, Toshiaki; Yona, Atsushi; Funabashi, Toshihisa

doi:10.1080/15325000802046637

Cited by 30 publications

(30 citation statements)

References 13 publications

(17 reference statements)

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“…The objective of the FLENN controller is to train the parameters w ij , w rj , w jy and w Ey to make the best match with regard to the control signal O (5) to generate ΔK P , ΔK I and ΔK D . Oci (2) Ocn ( …”

Section: A Pid Linear Damping Controllermentioning

confidence: 99%

“…In recent years some small-scale offshore wind farms (OWFs) have been evaluated, while a number of large-scale OWFs have been constructed and are now in commercial operation. Researchers have examined induction generators with wound-rotor or cage-type rotors [2], [3], but in recent years there has been increasing interest in Permanent Magnet Synchronous Generators (PMSG). Such generators are attractive due to their compact structure, high air-gap flux density, high power density, high torque-to-inertia ratio, and high torque capability [4].…”

Section: Introductionmentioning

confidence: 99%

“…1 shows the configuration of the system studied in this paper. The OWF represented by a large equivalent aggregated PMSG is driven by an equivalent aggregated wind turbine through the gearbox in [2]. The SG with a capacity of 555MVA is connected to a single-machine infinite-bus (SMIB) through a transformer T 1 and two parallel transmission lines: Line 1 and Line 2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of a Novel Intelligent Damping Controller for UPFC in Power System Connected Offshore Wind Power Applications

Lin¹,

Lu²

2015

IJIRSET

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This paper proposes a unified power flow controller (UPFC)-based oscillation damping controller for use with a permanent magnet synchronous generator (PMSG)-based offshore wave farm (OWF). A novel intelligent damping controller (NIDC) was used to increase the stability of power control and improve the performance, where the proposed NIDC consists of the adaptive critic network, the Functional Link based Elman Neural Network (FLENN), and PID linear damping controller. The UPFC is used with the NIDC to OWF, which is connected to a single-machine infinite-bus to improve system stability. The PID damping controller is analysis of complex eigenvalues based on a theory of modal control. The node connecting weights of the FLENN and critic network are trained online. The proposed NIDC can be used to achieve better damping characteristics. The internal power fluctuations in the power system can also be effectively alleviated under variable wind power generation.

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Section: A Pid Linear Damping Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Novel Intelligent Damping Controller for UPFC in Power System Connected Offshore Wind Power Applications

Lin¹,

Lu²

2015

IJIRSET

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“…In [25][26][27], a pitch angle controller based on fuzzy logic was presented. In [27], active power and in [25,27], both reactive power and rotational rotor speed were used as input signals. As in the mentioned items wind speed was ignored, the controller did not show as fast response and could cause mechanical damages to the synchronous generator.…”

Section: Introductionmentioning

confidence: 99%

“…It does not need a fast processor, and is more powerful than other nonlinear controllers too [24]. In [25][26][27], a pitch angle controller based on fuzzy logic was presented. In [27], active power and in [25,27], both reactive power and rotational rotor speed were used as input signals.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

Izadbakhsh¹,

Rezvani²,

Gandomkar³

2014

Archives of Electrical Engineering

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Abstract:The microgrid (MG) technology integrates distributed generations, energy storage elements and loads. In this paper, dynamic performance enhancement of an MG consisting of wind turbine was investigated using permanent magnet synchronous generation (PMSG), photovoltaic (PV), microturbine generation (MTG) systems and flywheel under different circumstances. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique was used by an adaptive neuro-fuzzy inference system (ANFIS); also, control of turbine output power in high speed winds was achieved using pitch angle control technic by fuzzy logic. For tracking the maximum point, the proposed ANFIS was trained by the optimum values. The simulation results showed that the ANFIS controller of grid-connected mode could easily meet the load demand with less fluctuation around the maximum power point. Moreover, pitch angle controller, which was based on fuzzy logic with wind speed and active power as the inputs, could have faster responses, thereby leading to flatter power curves, enhancement of the dynamic performance of wind turbine and prevention of both frazzle and mechanical damages to PMSG. The thorough wind power generation system, PV system, MTG, flywheel and power electronic converter interface were proposed by using Mat-lab/Simulink.

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Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

Rezvani

Izadbakhsh

Gandomkar

2015

Int J Numerical Modelling

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The microgrid (MG) is described as an electrical network of small modular distributed generation, energy storage devices and controllable loads. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique is used by artificial neural network (ANN), and also, control of turbine output power in high wind speeds is proposed using pitch angle control technic by fuzzy logic. To track the maximum power point (MPP) in the photovoltaic (PV), the proposed ANN is trained by the genetic algorithm (GA). In other word, the data are optimized by GA, and then these optimum values are used in ANN. The simulation results show that the ANN‐GA in comparison with the conventional algorithms with high accuracy can track the peak power point under different insolation conditions and meet the load demand with less fluctuation around the MPP; also it can increase convergence speed to achieve MPP. Moreover, pitch angle controller based on fuzzy logic with wind speed and active power as inputs that have faster responses which leads to have flatter power curves enhances the dynamic responses of wind turbine. The models are developed and applied in Matlab/Simulink. Copyright © 2015 John Wiley & Sons, Ltd.

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Output Power Leveling of Wind Farm Using Pitch-angle Control with Fuzzy Neural Network

Cited by 30 publications

References 13 publications

Design of a Novel Intelligent Damping Controller for UPFC in Power System Connected Offshore Wind Power Applications

Design of a Novel Intelligent Damping Controller for UPFC in Power System Connected Offshore Wind Power Applications

Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

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