Design and Simulation Study of Neural Adaptive Power System Stabilizer of DFIG

doi:10.16182/j.issn1004731x.joss.201701022

Journal of System Simulation ›› 2017, Vol. 29 ›› Issue (1): 162-169.doi: 10.16182/j.issn1004731x.joss.201701022

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Design and Simulation Study of Neural Adaptive Power System Stabilizer of DFIG

Niu Yuguang¹, Yang Wei^2,3, Li Xiaoming², Wang Shilin², Lin Zhongwei¹

1. State Key Laboratory for Alternate Electric Power System with Renewable Energy Source, North China Electric Power University,Beijing 102206, China;
2. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
3. North China Power Engineering CO. LTD., China Power Consulting Group, Beijing 100120, China

Received:2015-04-27 Revised:2015-09-17 Online:2017-01-08 Published:2020-06-01

Abstract

Abstract: A Flux Magnitude Angle Control (FMAC) strategy based Neural Adaptive Power System Stabilizer (NPSS) was designed to improve the transient stability of grid-connected Double Fed Induction Generators (DFIGs). An online training algorithm based Elman artificial neural network was adopted to achieve adaptive control. For releasing computing burden and improving computing speed, a simplified method was used, where the calculation of jacobian matrix was replaced by the sign of itself. A simplified and generic renewable power system demonstrates the control performance contributions. The results of both dominant eigenvalue analysis and time response simulation illustrate contributions to system damping that the NPSS can make. Performance capabilities superior to those provided by Synchronous Generation (SG) with Automatic Voltage Regulator (AVR) and PSS control demonstrate that NPSS installed DFIG has better performances of system damping, voltage regulation and transient stability.

Key words: double fed induction generator, power system stabilizer, artificial neural network, system damping

CLC Number:

TP13

Niu Yuguang, Yang Wei, Li Xiaoming, Wang Shilin, Lin Zhongwei. Design and Simulation Study of Neural Adaptive Power System Stabilizer of DFIG[J]. Journal of System Simulation, 2017, 29(1): 162-169.

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Design and Simulation Study of Neural Adaptive Power System Stabilizer of DFIG

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