kalmanfilter based deduction of sssc.pptx

The low inertia characteristic of the power system is increasingly prominent with the increase of wind power penetration. Therefore, the threat of random wind speeds and sudden frequency events to the frequency stability of a low inertia power system is further enhanced. It is necessary for wind power generators to provide frequency auxiliary service for the power system through appropriate control schemes. To address these issues, a frequency control scheme is proposed for a doubly-fed induction generator (DFIG) to support the frequency stability of the system in this paper

1. COLLEGE OF ENGINEERING, GUINDY Kalman Filter Based Detection and Mitigation of Subsynchronous Resonance with SSSC Guided by: Presented by: Dr.S.V.Anbuselvi A. Swetha Assistant Professor 2021227027 Department of EEE Power System Engg Anna University Anna University

2. Project Outline Objective Introduction Literature Survey Abstract Circuit model Simulation diagram Outcomes Simulation output

3. OBJECTIVE The objective is to mitigate SSR using SSSC with an estimated subsynchronous voltage component in which LPFs are used in estimation algorithm.

4. INTRODUCTION In a long transmission line the use of static series synchronous compensation (SSSC) with fixed series capacitor enables fast control of power flow. There is a potential risk of sub synchronous resonance (SSR) due to the series capacitor. Propose to use the Kalman filter (KF) for state estimation of sub synchronous components present in series compensated line and the mitigation of SSR. The design of KF-damping controller is based on the magnitude of damping torque in the range of torsional mode frequencies.

5. MOTIVE Since Flexible AC Transmission (FACTS) device VSC provides both real(P) and reactive power(Q) support, by using such devices and mitigate SSR.

6. LITERATURE SURVEY This paper explains the complete Process of Kalman Filter estimation of Current signals involved in SSR between the masses. S.No Authors Title Journals Year 1. Thirumalaivasan Rajaram, Janaki Muneappa Reddy,, and Yunjian Xu Kalman Filter Based Detection and Mitigation of Subsynchronous Resonance with SSSC IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 32, NO. 2, MARCH 2017 2017

7. LITERATURE SURVEY S.NO S.No Authors Title Journals Year 2. Chengbing He, Dakang Sun 2 , Lei Song 3 and Li Ma Analysis of Subsynchronous Resonance Characteristics and Influence Factors in a Series Compensated Transmission System Energies 2019, 12, 3282; doi:10.3390/en1217328 2019 The frequency due to the torsional interaction are obtained from the above paper.

8. Modified First Bench Mark model with SSSC

9. Known Frequencies of IEEE First Bench Mark Model HP-IP = 15.78 Hz IP-LPA = 20.22 Hz LPA-LPB = 25.56 Hz LPB-GEN = 32.31 Hz GEN-EXC = 47.46 Hz

10. Kalman Filter The Kalman Filter is an efficient optimal estimator (a set of mathematical equations) that provides a recursive computational methodology for estimating the state of a discrete-data controlled process from measurements that are typically noisy, while providing an estimate of the uncertainty of the estimates.

11. Sub synchronous components involved in estimation The sub synchronous current in D-Q frame is given by The KF and the D-Q components of a sub synchronous frequency current The state space model of Kalman estimator for a sub synchronous frequency current

12. Kalman Filter Kalman Filter matrix Prior estimation of state and error covariance: Compute Kalman gain: Update estimate: Update error covariance:

13. BLOCK DIAGRAM

14. Simulation Diagram

15. Kalman Filter Circuit

16. SSR due to torsional interaction

17. Current At Terminal 2 in DQO frame

18. Kalman Filter Outputs For 35Hz and 25Hz compared together

19. Kalman Filter Output For 35Hz

20. Kalman Filter Output For 25Hz

21. Waveform of Iabc_HV

22. Output of Kalman Filter

23. Output after converting into abc stator frame

24. FFT of Iabc_HV

25. Thank You