Stability Modeling and Analysis of Grid Connected Doubly Fed Wind Energy Generation Based on Small Signal Model

Authors

  • Zheng Yangbing 1) College of Mechanical and Electronic Engineering, Nanyang Normal University, Nanyang 473061, Henan, China 2) Qinghai Wandong Ecological Environment Development Co.LTD, Geermu 816000, Qinghai, China
  • Xue Xiao School of Information Engineering, Nanyang Institute of Technology, Nanyang 473004, Henan, China
  • Wang Xing College of Mechanical and Electronic Engineering, Nanyang Normal University, Nanyang 473061, Henan, China
  • Cui Mingyue College of Mechanical and Electronic Engineering, Nanyang Normal University, Nanyang 473061, Henan, China
  • Chao Lu Nanyang Zehui Technology Co.LTD, Nanyang 473000, Henan, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.3823

Keywords:

Small signal model, control strategy, stability modeling, doubly fed wind energy generation, operating conditions, compensation capacity

Abstract

Stable wind power generation can ensure the quality of power transmitted by the grid. The application of large-scale grid-connected wind power systems will induce problems such as grid oscillation and frequency instability. In order to solve the problem of abnormal power system interaction caused by large-scale wind power access and improve the stability of grid-connected doubly-fed wind power generation, this paper proposes a stability modeling analysis of grid-connected doubly-fed wind power generation based on small signal model. First, the operating conditions of the grid-connected DFIG are analyzed, and the vector diagrams of the three operating conditions are given. When the grid-connected DFIG is in the super-synchronous working state, the sub-synchronous working state and the synchronous working state. According to the operating conditions of the grid-connected doubly-fed generators, the grid-connected doubly-fed wind power generation system is linearized. According to the relationship between the actual speed and the synchronous speed of the doubly-fed generator, the operating conditions of the doubly-fed generator are analyzed. By introducing the small-signal model, we analyze the small-signal of the grid-connected doubly-fed wind power generation system. The indirect current control circuit is used to perform reactive power compensation for grid-connected doubly-fed wind turbines. By calculating the reactive power loss of the grid-connected DFIG and the reactive power loss of the transmission line, the compensation capacity of the grid-connected DFIG is calculated. The transient voltage of the wind turbine is controlled by the rotor-side frequency converter, combined with the pitch angle control model. So far, this paper has realized the modeling analysis of grid-connected doubly-fed wind power generation stability. The simulation results show that the modeling analysis in this paper is reasonable for the small-signal analysis results of the stability of grid-connected doubly-fed wind power generation. In the rotor voltage simulation test, after the oscillation occurs for 1 s, the model starts to simulate and eliminate the fault. During the simulation period of 0.7 s∼1.0 s, the output voltage of the converter decreased to 168 V, and the voltage waveform did not fluctuate greatly after 1.0 s. The experimental results show that this method can improve the stability of grid-connected doubly-fed wind power generation.

Downloads

Download data is not yet available.

Author Biographies

Zheng Yangbing, 1) College of Mechanical and Electronic Engineering, Nanyang Normal University, Nanyang 473061, Henan, China 2) Qinghai Wandong Ecological Environment Development Co.LTD, Geermu 816000, Qinghai, China

Zheng Yangbing, Associate Professor of control science and engineering, with Nanyang Normal University, Nanyang, China. She received her Bachelor of Engineering Science in Electronic Information Engineering from Nanyang Institute of Technology, Henan, China, in 2006; and the Doctor Degree of Engineering in detection technology and automatic equipment from China University of Mining and Technology, Beijing, China, in 2013, respectively. Her current research interests include active robot control, and nonlinear control.

Xue Xiao, School of Information Engineering, Nanyang Institute of Technology, Nanyang 473004, Henan, China

Xue Xiao, Associate Professor of School of Electronic and Electrical Engineering in Nanyang Institute of Technology, Nanyang, China. He received his Bachelor of Engineering Science in Electronic Information Engineering from Nanyang Institute of Technology, Henan, China, in 2003; the Doctor Degree of Engineering in detection technology and automatic equipment from China University of Geosciences, Wuhan, China, in 2015. His current research interests include Detection technology, and intelligent control.

Wang Xing, College of Mechanical and Electronic Engineering, Nanyang Normal University, Nanyang 473061, Henan, China

Wang Xing, Professor of control science and engineering, with Nanyang Normal University, Nanyang, China. He mainly undertakes the teaching of professional courses such as introduction to Internet of things and computer control system, and his research direction is computer control. He received his Bachelor’s Degree in electrical technology from the school of Computer Science of Henan University and his Master’s Degree in computer application technology from Wuhan University of Technology.

Cui Mingyue, College of Mechanical and Electronic Engineering, Nanyang Normal University, Nanyang 473061, Henan, China

Cui Mingyue, Associate Professor of control science and engineering, with Nanyang Normal University, Nanyang, China. He graduated from the automation School of Chongqing University with the Doctorate in Engineering. His current research interests include mobile robot navigation and control.

Chao Lu, Nanyang Zehui Technology Co.LTD, Nanyang 473000, Henan, China

Chao Lu, Engineer of Nanyang Zehui Technology Co., LTD. He received his Bachelor of Engineering Science in electronic information engineering form Nanyang Institute of Technology, Henan, China, in 2015.

References

Sohn A P, Maurício B. de C. Salles, Luís F. C. Alberto. Transient Stability of Power Systems Under High Penetrations of Wind Power Generation[J]. Journal of Control, Automation and Electrical Systems, 2019, 30(6):1116–1125.

Santra S B, Ogungbe B T. A cogeneration scheme with biogas and improvement of frequency stability using inertia based control in AC microgrid[J]. International Journal of Emerging Electric Power Systems, 2021, 22(6):675–691.

Rakhshani E, Gusain D, Sewdien V, et al. A Key Performance Indicator to Assess the Frequency Stability of Wind Generation Dominated Power System[J]. IEEE Access, 2019, PP(99):1–1.

He L. DC Bus Voltage Adaptive Control for DC Microgrid with Wind Power Generator Systems[J]. International Core Journal of Engineering, 2019, 5(10):215–225.

Du W, Ren B, Wang H, et al. Small-disturbance stability of a weakly grid-connected wind farm with multiple PMSGs dominated by the DC-link voltage dynamics[J]. IET Renewable Power Generation, 2020, 14(16):3103–3114.

Zuo H, Liu G, Jiagiang E, et al. Catastrophic analysis on the stability of a large dish solar thermal power generation system with wind-induced vibration[J]. Solar Energy, 2019, 183(MAY):40–49.

Lu Jia, Research on Intelligent Control of Transient Stability for Wind Power Generation System[J]. Journal of China Academy of Electronics and Information Technology, 2019, 14(3):321–326.

Tavoosi J, Mohammadzadeh A, Pahlevanzadeh B, et al. A machine learning approach for active/reactive power control of grid-connected doubly-fed induction generators[J]. Ain Shams Engineering Journal, 2022, 13(2):101564.

Devadason J, Moses P S, Masoum M. Stability Domain Analysis and Enhancement of Squirrel Cage Induction Generator Wind Turbines in Weak Grids[J]. Energies, 2021, 14(16):4786.

Shah S, Koralewicz P, Gevorgian V, et al. Impedance Methods for Analyzing the Stability Impacts of Inverter-Based Resources: Stability Analysis Tools for Modern Power Systems[J]. IEEE Electrification Magazine, 2021, 9(1):53–65.

Perzhabinsky S M, Karamov D N, Achitaev A A. A Model of Reliability Optimization of a Stand-Alone Electric Power System with Constraints on Dynamic Stability of the Wind Turbine[J]. Journal of Siberian Federal University Engineering & Technologies, 2021:55–71.

Yuan Z, Wang W, Fan X. Back propagation neural network clustering architecture for stability enhancement and harmonic suppression in wind turbines for smart cities[J]. Computers & Electrical Engineering, 2019, 74:105–116.

Ma R, Li X, Gao W, et al. Random-Fuzzy Chance-Constrained Programming Optimal Power Flow of Wind Integrated Power Considering Voltage Stability[J]. IEEE Access, 2020, 8:217957–217966.

Naz M N, Imtiaz S, Bhatti M, et al. Dynamic Stability Improvement of Decentralized Wind Farms by Effective Distribution Static Compensator[J]. Journal of Modern Power Systems and Clean Energy, 2021, 9(3):516–525.

Chen Y, Mazhari M, Chung C Y, et al. Rotor Angle Stability Prediction of Power Systems with High wind energy Penetration Using a Stability Index Vector[J]. IEEE Transactions on Power Systems, 2020, PP(99):1–1.

Yi J, Lin W, Hu J, et al. An Integrated Model-driven and Data-driven Method for On-line Prediction of Transient Stability of Power System with wind energy Generation[J]. IEEE Access, 2020, PP(99):1–1.

Wu A, Zhao B, Mao J, et al. Adaptive active fault-tolerant MPPT control for wind energy generation system under partial loss of actuator effectiveness[J]. International Journal of Electrical Power & Energy Systems, 2019, 105(FEB.):660–670.

Dai B. Design of complex wind energy generation parameter control system based on embedded control combined with Internet of Things[J]. Web Intelligence and Agent Systems, 2019, 17(2):131–139.

Rakhshani E, Gusain D, Sewdien V, et al. A Key Performance Indicator to Assess the Frequency Stability of Wind Generation Dominated Power System[J]. IEEE Access, 2019, PP(99):1–1.

Said S, Aly M, Hartmann B. A robust-SMES control for enhancing stability of distribution systems fed from intermittent wind energy generation[J]. Turkish Journal of Electrical Engineering and Computer Sciences, 2019, 99(99):1–16.

Zheng D, Ouyang J, Xiong X, et al. Rotor angle stability control for DFIG-integrated power system considering phase-amplitude characteristics of transient-grid voltage[J]. Generation, Transmission & Distribution, IET, 2019, 13(16):3549–3555.

Jobayer A M, Chowdhury M R, Zhao L, et al. Impact of Societal Events on Frequency Stability Considering LED TVs in Low Inertia Trending Power Systems[J]. IEEE Transactions on Industry Applications, 2021, PP(99):1–1.

Downloads

Published

2023-01-03

How to Cite

Yangbing, Z. ., Xiao, X. ., Xing, W. ., Mingyue, C. ., & Lu, C. . (2023). Stability Modeling and Analysis of Grid Connected Doubly Fed Wind Energy Generation Based on Small Signal Model. Distributed Generation &Amp; Alternative Energy Journal, 38(02), 413–434. https://doi.org/10.13052/dgaej2156-3306.3823

Issue

2023: Vol 38 Iss 2

Section

Articles