Slot ripples in the damper windings of a salient-pole synchronous generator

1994 ◽  
Vol 9 (1) ◽  
pp. 126-134 ◽  
Author(s):  
J. Matsuki ◽  
T. Katagi ◽  
T. Okada
2017 ◽  
Vol 27 (11) ◽  
pp. e2401
Author(s):  
Nur Iylia Shah Riman ◽  
Mohd Faris Abdullah ◽  
Mohd Fakhizan Romlie ◽  
Zuhairi Baharudin

2018 ◽  
Vol 73 ◽  
pp. 81-90
Author(s):  
Ante Elez ◽  
Marijan Petrinic ◽  
Miroslav Petrinic ◽  
Babak Vaseghi ◽  
Alireza Abasian

2020 ◽  
Vol 2020 (0) ◽  
pp. 17B12
Author(s):  
Takuma MUTOH ◽  
Akira OZAWA ◽  
Teppei IKUTA ◽  
Takumi ITO ◽  
Nobumichi FUJISAWA ◽  
...  

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1609 ◽  
Author(s):  
Nikola Lopac ◽  
Neven Bulic ◽  
Niksa Vrkic

Synchronous generator load angle is a fundamental quantity for power system stability assessment, with possible real-time applications in protection and excitation control systems. Commonly used methods of load angle determination require additional measuring equipment, while existing research on load angle estimation for wound rotor synchronous generator has been limited to the estimator based on the generator’s phasor diagram and estimators based on artificial neural networks. In this paper, a load angle estimator for salient-pole wound rotor synchronous generator, based on a simple sliding mode observer (SMO) which utilizes field current, stator voltages, and stator currents measurements, is proposed. The conventional SMO structure is improved with use of hyperbolic tangent sigmoid functions, implementation of the second order low-pass filters accompanied with corresponding phase delay compensation, and introduction of an adaptive observer gain proportional to the measured field current value. Several case studies conducted on a generator connected to a power system suggest that the proposed estimator provides an adequate accuracy during active and reactive power disturbances during stable generator operation, outperforming the classical phasor diagram-based estimator by reducing mean squared error by up to 14.10%, mean absolute error by up to 41.55%, and maximum absolute error by up to 8.81%.


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