Parameters Identification of Equivalent Model of Permanent Magnet Synchronous Generator (PMSG) Wind Farm Based on Analysis of Trajectory Sensitivity

As wind farms have great influences on power system stability, it is essential to develop an adaptive as well as robust equivalent model of it. In this paper, a detailed equivalent model of PMSG wind farm and initialization method is developed. The trajectory sensitivity of parameters is analyzed. T...

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Bibliographic Details
Published in:Energies (Basel) 2020-09, Vol.13 (18), p.4607
Main Authors: Zhang, Jian, Cui, Mingjian, He, Yigang
Format: Article
Language:English
Subjects:
Adaptive systems
Algorithms
Genetic algorithms
Global optimization
improved GLPSO hybrid algorithm
Measuring instruments
Methods
Parameter estimation
Parameter identification
Parameter sensitivity
Permanent magnets
Phasors
PMSG
Sensitivity analysis
Simulation
Systems stability
Trajectory analysis
trajectory sensitivity
Turbines
Wind effects
Wind farms
Wind power
Wind speed
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Summary:As wind farms have great influences on power system stability, it is essential to develop an adaptive as well as robust equivalent model of it. In this paper, a detailed equivalent model of PMSG wind farm and initialization method is developed. The trajectory sensitivity of parameters is analyzed. Then, the key parameters are estimated using improved Genetic Learning Particle Swarm Optimization (GLPSO) hybrid algorithm with phasor measurement unit (PMU). The description and generalization capability, stability for parameter identification of the equivalent model under wake effects, and when some wind turbines are off-line or wind speed is unknown after an event are analyzed. The maximum differences between the values of estimated parameters and their real ones are less than 10% for the proportional magnification constant of DC voltage controller Kp2 and grid side current controller Kp3. The convergence rate and global optimization performance of the improved GLPSO hybrid algorithm is 0.5 times higher than the classical particle swarm optimization algorithm (PSO) and genetic algorithm (GA).
ISSN:1996-1073
1996-1073
DOI:10.3390/en13184607