Tip speed ratio control of wind turbine generating system connected in series

Yokoyama, Haruki; Tatsuta, Fujio; Nishikata, Shoji

doi:10.1109/icems.2011.6073595

Cited by 14 publications

(6 citation statements)

References 2 publications

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“…Moreover, the wind turbine and gear box is emulated by a permanent‐magnet synchronous motor (PMSM) servo drive as a prime mover. In general, the characteristics of a wind turbine can be represented in a simplified form of power performance coefficient and tip speed ratio ( C p – λ ) [17]. The relationship of C p – λ is shown in Fig.…”

Section: Wind Turbine Emulatormentioning

confidence: 99%

Improved differential evolution‐based Elman neural network controller for squirrel‐cage induction generator system

Lin

Tan

Tsai

2016

IET Renewable Power Generation

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Section: Wind Turbine Emulatormentioning

confidence: 99%

Improved differential evolution‐based Elman neural network controller for squirrel‐cage induction generator system

Lin

Tan

Tsai

2016

IET Renewable Power Generation

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“…This paper uses maximum power point tracking (MPPT) to track the OPEN ACCESS maximum power point so as to increase the output efficiency of the wind power [1]. There are multiple algorithms for increasing the MPPT efficiency of wind turbines, such as perturbation and observation (P&O) [2][3][4], the hill-climbing search (HCS) algorithm [5,6], tip speed ratio control (TSR) [7,8], and so on. Zou et al [9] mentioned wind power system stability analysis, using the Optimal Reference Power Curve for MPPT, and developed small signal analysis MPPT for nonlinear turbine systems.…”

Section: Introductionmentioning

confidence: 99%

Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems

2014

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This paper proposes a sliding mode extremum seeking control (SMESC) of chaos embedded particle swarm optimization (CEPSO) Algorithm, applied to the design of maximum power point tracking in wind power systems. Its features are that the control parameters in SMESC are optimized by CEPSO, making it unnecessary to change the output power of different wind turbines, the designed in-repetition rate is reduced, and the system control efficiency is increased. The wind power system control is designed by simulation, in comparison with the traditional wind power control method, and the simulated dynamic response obtained by the SMESC algorithm proposed in this paper is better than the traditional hill-climbing search (HCS) and extremum seeking control (ESC) algorithms in the transient or steady states, validating the advantages and practicability of the method proposed in this paper.

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“…It is clear from the literature [1][2][3][4][5][6][7] that there are already quite a few algorithms that can be used to increase the maximum power point tracking efficiency of a wind turbine. These include Perturbation and Observation (P&O) [1][2][3], the Hill-Climbing Search Algorithm (HCS) [4,5], Tip Speed Ratio Control (TSR) [6,7] and others.…”

Section: Introductionmentioning

confidence: 99%

“…These include Perturbation and Observation (P&O) [1][2][3], the Hill-Climbing Search Algorithm (HCS) [4,5], Tip Speed Ratio Control (TSR) [6,7] and others. In addition, some investigators have made use of the Optimal Reference Power Curve of stability analysis to do maximum power point tracking (MPPT).…”

Section: Introductionmentioning

confidence: 99%

Blade Fault Diagnosis in Small Wind Power Systems Using MPPT with Optimized Control Parameters

Chen

Hung

2015

Energies

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Abstract:A systematic experiment verification of Chaos Embedded Sliding Mode Extremum Seeking Control for maximum power point tracking and a method for detecting possible faults in small wind turbine systems in advance are proposed in this paper. The chaotic logistic map is used to replace the random function in the particle swarm optimization algorithm for faster searching the optimal control parameter U . From the experimental results, it is verified that the Chaos Embedded Sliding Mode Extremum Seeking Control scheme has a better dynamic response than traditional Extremum Seeking Control scheme and Hill-Climbing Search scheme for maximum power point tracking. In the proposed scheme for fault detection, a chaotic synchronization method is used to transform the maximum power point tracking signal into a chaos synchronization error distribution diagram. It is then taken as the characteristic for fault diagnosis purposes. Finally, an extension theory pattern recognition technique is applied to diagnose the fault. Notably, the use of the chaotic dynamic errors as the fault diagnosis characteristic reduces the number of extracted features required, and therefore greatly reduces both the computation time and the hardware implementation cost. From the experimental results, it is shown that the fault diagnosis rate of the proposed method exceeds 98% not only in non-real-time but also in real-time of faults detection of the blades.

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Tip speed ratio control of wind turbine generating system connected in series

Cited by 14 publications

References 2 publications

Improved differential evolution‐based Elman neural network controller for squirrel‐cage induction generator system

Improved differential evolution‐based Elman neural network controller for squirrel‐cage induction generator system

Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems

Blade Fault Diagnosis in Small Wind Power Systems Using MPPT with Optimized Control Parameters

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