pith. sign in

arxiv: 1907.06846 · v1 · pith:RSMUTHSJnew · submitted 2019-07-16 · 📡 eess.SY · cs.SY

Coherency and Online Signal Selection Based Wide Area Control of Wind Integrated Power Grid

Abilash Thakallapelli , S J Hossain , Sukumar Kamalasadan This is my paper

Pith reviewed 2026-05-24 21:10 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords wide area controlcoherency groupingresidue methodinterarea oscillationswind integrated power griddiscrete LQRKalman filteringpower system stabilizers
0
0 comments X

The pith

A wide-area control architecture uses real-time coherency grouping and residue-based signal selection to damp interarea oscillations more effectively in wind-integrated grids.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a method that combines discrete linear quadratic regulator control with Kalman filtering for state estimation in wide-area damping. It incorporates online coherency grouping to track real-time grid changes and residue-based selection to choose effective control signals. This setup adapts to wind integration and varying conditions, yielding better oscillation damping than conventional local power system stabilizers or offline wide-area designs. The approach is validated on a two-area wind-integrated system and the IEEE 39-bus network.

Core claim

The architecture provides online coherency grouping that properly characterizes real-time changes in the power grid and online wide-area signal selection based on residue method for proper selection of the WAC signals, allowing more effective damping than conventional local signal based power system stabilizers or offline based WAC designs.

What carries the argument

Discrete linear quadratic regulator with Kalman filtering state-estimation, extended by real-time coherency grouping and residue-based wide-area signal selection.

If this is right

  • The controller monitors and adapts to real-time grid changes caused by wind integration.
  • Appropriate wide-area signals are selected dynamically for improved interarea oscillation damping.
  • Performance exceeds both local stabilizers and offline wide-area designs on the tested two-area and 39-bus systems.
  • The method reduces reliance on fixed tuning that becomes outdated as operating conditions shift.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The online grouping and selection steps could extend to other variable renewable sources such as solar.
  • Embedding the method in existing phasor measurement unit networks would strengthen its real-time capability.
  • Similar residue-based selection might improve other wide-area applications like voltage control.

Load-bearing premise

Power grid dynamics can be represented by linear models suitable for discrete LQR and Kalman filtering, with real-time coherency grouping and residue selection remaining effective under varying wind conditions without instability or delays.

What would settle it

A simulation on the IEEE 39-bus system under fluctuating wind output where the proposed controller produces less damping or induces instability compared with local power system stabilizers.

Figures

Figures reproduced from arXiv: 1907.06846 by Abilash Thakallapelli, S J Hossain, Sukumar Kamalasadan.

Figure 2
Figure 2. Figure 2: Two-area study system model. Group-1 Group-3 Group-2 WAC WAC WAC [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 1
Figure 1. Figure 1: Flow chart of the proposed coherency grouping algorithm. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 6
Figure 6. Figure 6: Coherency group-3 5 6 7 8 9 time (s) 375 376 377 378 379 380 Speed (rad/s) Slow Coherency (Group-4) W10 5 6 7 8 9 time (s) 375 376 377 378 379 380 Speed (rad/s) Spectral Clustering (Group-4) W10 [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: Coherency group-2 at various locations of IEEE-39 bus system. For this, 3-ph faults are created for a duration of 0.1 sec at bus-14, bus-19, and bus-6 at 5, 31 and 61 sec respectively. Table II shows the coherency grouping comparison of 39-bus system for various operating conditions [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Coherency grouping for different operating conditions. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Input/Output signal for transfer function estimation. [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Proposed wide area controller. so k is assumed to be a large number which is limited by number of samples l and computational burden. Then, modes with negligible residues are discarded [38] and new order p is identified (i.e the order of (9) is reduced from k to p). The discrete state space matrices are formulated using (9) which can be written as in (15). x(p + 1) = Ax(p) + Bu(p) y(p) = Cx(p) + Du(p) (15… view at source ↗
Figure 11
Figure 11. Figure 11: Experimental test bed. A. Implementation test results using two-area system Based on coherency grouping as shown in Table I, it can be seen that generators 1 and 2 are in one group and generators 3 and 4 are in the other group, so two WACs are required which are to be placed one in each group. The signal selection for WAC control loop is discussed in section II. The simulation results with the proposed co… view at source ↗
Figure 15
Figure 15. Figure 15: Relative speed of generator 1 w.r.t generator 4. [PITH_FULL_IMAGE:figures/full_fig_p009_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Relative speed of generator 2 w.r.t generator 4. [PITH_FULL_IMAGE:figures/full_fig_p009_16.png] view at source ↗
Figure 14
Figure 14. Figure 14: Relative speed of generator 2 w.r.t generator 3. [PITH_FULL_IMAGE:figures/full_fig_p009_14.png] view at source ↗
Figure 20
Figure 20. Figure 20: Relative speed of generator 5 w.r.t generator 2. [PITH_FULL_IMAGE:figures/full_fig_p010_20.png] view at source ↗
Figure 18
Figure 18. Figure 18: Wind Speed. 0 10 20 30 40 50 60 70 80 90 100 time (s) 0 50 100 150 200 250 300 Active Power (MW) [PITH_FULL_IMAGE:figures/full_fig_p010_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: WTG Active Power. the active power. It is seen that (see [PITH_FULL_IMAGE:figures/full_fig_p010_19.png] view at source ↗
Figure 23
Figure 23. Figure 23: WAC input signal to generator 2, 7, and 8 [PITH_FULL_IMAGE:figures/full_fig_p010_23.png] view at source ↗
read the original abstract

This paper introduces a novel method of designing wide area control (WAC) based on a discrete linear quadratic regulator and Kalman filtering based state-estimation that can be applied for real-time damping of interarea oscillations of wind integrated power grid. The main advantages of the proposed method are that the architecture provides online coherency grouping that properly characterizes real-time changes in the power grid and online wide-area signal selection based on residue method for proper selection of the WAC signals. The proposed architecture can, thus, accurately monitors changes in the power grid and select the appropriate control signal for more effectively damping the interarea oscillation when compared to the conventional local signal based power system stabilizers or offline based WAC designs. The architecture is tested on a wind integrated two-area system and the IEEE 39 bus system in order to show the capability of the proposed method.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes a wide-area control (WAC) architecture for real-time damping of inter-area oscillations in wind-integrated power grids. It combines discrete LQR control with Kalman filtering for state estimation, adding online coherency grouping to track real-time grid changes and residue-based online selection of WAC signals. The central claim is that this yields more effective damping than conventional local-signal PSS or offline WAC designs; the architecture is tested on a wind-integrated two-area system and the IEEE 39-bus system.

Significance. If the online coherency and signal-selection features can be shown to deliver measurable improvements in damping under realistic wind variation, the work would provide a practical, incrementally novel extension of standard LQR/Kalman tools to adaptive wide-area control. The explicit motivation for the online components is independent of the core LQR/Kalman machinery and addresses a recognized limitation of offline designs.

major comments (2)
  1. [Abstract] Abstract: the abstract states that the architecture was tested on the wind-integrated two-area system and IEEE 39-bus system yet supplies no quantitative results, error metrics, comparison data, or robustness checks. Without these data the central performance claims cannot be evaluated.
  2. [Abstract] Abstract (method description): the design rests on the assumption that the wind-integrated system can be represented by linear time-invariant models suitable for discrete LQR and Kalman filtering. No analysis or simulation is supplied to show that online coherency grouping and residue selection remain stable and effective when wind-speed fluctuations move the operating point or introduce nonlinearities.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the paper accordingly to strengthen the presentation of results and clarify methodological assumptions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the abstract states that the architecture was tested on the wind-integrated two-area system and IEEE 39-bus system yet supplies no quantitative results, error metrics, comparison data, or robustness checks. Without these data the central performance claims cannot be evaluated.

    Authors: We agree that the abstract would benefit from inclusion of key quantitative metrics. The full manuscript contains simulation results with damping ratios, settling times, and comparisons against local PSS and offline WAC designs on both test systems, including cases with wind variation. In the revised version we will condense the most salient numerical results (e.g., percentage improvement in damping ratio and inter-area mode settling time) into the abstract. revision: yes

  2. Referee: [Abstract] Abstract (method description): the design rests on the assumption that the wind-integrated system can be represented by linear time-invariant models suitable for discrete LQR and Kalman filtering. No analysis or simulation is supplied to show that online coherency grouping and residue selection remain stable and effective when wind-speed fluctuations move the operating point or introduce nonlinearities.

    Authors: The method is formulated within the standard small-signal linear framework used for inter-area oscillation damping; the online coherency and residue-based selection modules are explicitly intended to retune the controller when the operating point shifts due to wind changes. The reported simulations already include wind-speed variations on both test systems and demonstrate continued effective damping. To directly address the concern we will add a dedicated paragraph discussing the range of validity of the LTI assumption, the adaptation mechanism, and any observed limitations under larger disturbances. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper describes an architecture using standard discrete LQR and Kalman filtering for wide-area control, augmented with online coherency grouping and residue-based signal selection. No load-bearing steps in the abstract or described method reduce by construction to fitted inputs, self-definitions, or self-citation chains; the online features are presented as extensions of established LQR/Kalman tools with independent motivation from real-time grid monitoring needs. The two-area and IEEE 39-bus tests are cited as validation rather than as the source of the claimed performance. This matches the default expectation that most papers are not circular, yielding a self-contained derivation against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard linear control assumptions and domain-specific power system modeling choices whose details are not visible in the abstract; no free parameters, invented entities, or ad-hoc axioms are explicitly introduced in the provided text.

axioms (2)
  • domain assumption Power system dynamics can be sufficiently approximated by linear time-invariant models for discrete LQR and Kalman filter application
    Invoked by the choice of D-LQR and Kalman estimation for real-time control (abstract).
  • domain assumption Residue method and coherency grouping remain reliable indicators for signal selection under real-time wind variability
    Central to the online selection advantage claimed (abstract).

pith-pipeline@v0.9.0 · 5684 in / 1342 out tokens · 26221 ms · 2026-05-24T21:10:18.245051+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages

  1. [1]

    A fundamental study of inter-area oscillations in power systems,

    M. Klein, G. J. Rogers, and P. Kundur, “A fundamental study of inter-area oscillations in power systems,” IEEE Transactions on Power Systems, vol. 6, no. 3, pp. 914–921, Aug 1991

    work page 1991

  2. [2]

    Chow, Power System Coherency and Model Reduction , ser

    J. Chow, Power System Coherency and Model Reduction , ser. Power Electronics and Power Systems. Springer New York, 2014. [Online]. Available: https://books.google.com/books?id=HGnABAAAQBAJ

    work page 2014

  3. [3]

    Pal and B

    B. Pal and B. Chaudhuri, Robust Control in Power Systems , ser. Power Electronics and Power Systems. Springer US, 2006. [Online]. Available: https://books.google.com/books?id=UXh1o196-McC

    work page 2006

  4. [4]

    Rogers, Power System Oscillations , ser

    G. Rogers, Power System Oscillations , ser. Power Electronics and Power Systems. Springer US, 2012. [Online]. Available: https: //books.google.com/books?id=xOTTBwAAQBAJ

    work page 2012

  5. [5]

    Damping inter-area oscillations based on a model predictive control (mpc) hvdc supplementary con- troller,

    S. P. Azad, R. Iravani, and J. E. Tate, “Damping inter-area oscillations based on a model predictive control (mpc) hvdc supplementary con- troller,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3174– 3183, Aug 2013

    work page 2013

  6. [6]

    Wide-area damping controller of facts devices for inter-area oscillations considering communication time delays,

    W. Yao, L. Jiang, J. Wen, Q. H. Wu, and S. Cheng, “Wide-area damping controller of facts devices for inter-area oscillations considering communication time delays,” IEEE Transactions on Power Systems , vol. 29, no. 1, pp. 318–329, Jan 2014

    work page 2014

  7. [7]

    Robust design method for power oscillation damping controller of statcom based on residue and tls-esprit,

    S. F. P. Jiang and X. Wu, “Robust design method for power oscillation damping controller of statcom based on residue and tls-esprit,” Interna- tional Transactions on Electrical Energy Systems , vol. 24, no. 10, pp. 1385–1400, Jul 2013

    work page 2013

  8. [8]

    Robust power system sta- bilizer design using h infin; loop shaping approach,

    C. Zhu, M. Khammash, V . Vittal, and W. Qiu, “Robust power system sta- bilizer design using h infin; loop shaping approach,” IEEE Transactions on Power Systems, vol. 18, no. 2, pp. 810–818, May 2003

    work page 2003

  9. [9]

    Adaptive delay compensator for the robust wide-area damping controller design,

    M. Beiraghi and A. M. Ranjbar, “Adaptive delay compensator for the robust wide-area damping controller design,” IEEE Transactions on Power Systems, vol. 31, no. 6, pp. 4966–4976, Nov 2016

    work page 2016

  10. [10]

    Wide area measurements based robust power system controller design,

    M. Watanabe, M. Yamashita, and Y . Mitani, “Wide area measurements based robust power system controller design,” in 2014 Power Systems Computation Conference, Aug 2014, pp. 1–7

    work page 2014

  11. [11]

    Wide-area measurement system development at the distribution level: An fnet/grideye example,

    Y . Liu, “Wide-area measurement system development at the distribution level: An fnet/grideye example,” in2016 IEEE Power and Energy Society General Meeting (PESGM) , July 2016, pp. 1–1

    work page 2016

  12. [12]

    Distributed data analytics platform for wide-area synchrophasor measurement systems,

    D. Zhou, J. Guo, Y . Zhang, J. Chai, H. Liu, Y . Liu, C. Huang, X. Gui, and Y . Liu, “Distributed data analytics platform for wide-area synchrophasor measurement systems,” IEEE Transactions on Smart Grid , vol. 7, no. 5, pp. 2397–2405, Sept 2016

    work page 2016

  13. [13]

    Probing signal design for power system identifica- tion,

    J. W. Pierre, N. Zhou, F. K. Tuffner, J. F. Hauer, D. J. Trudnowski, and W. A. Mittelstadt, “Probing signal design for power system identifica- tion,” IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 835–843, May 2010

    work page 2010

  14. [14]

    Armax-based transfer function model identification using wide-area measurement for adaptive and coordinated damping control,

    H. Liu, L. Zhu, Z. Pan, F. Bai, Y . Liu, Y . Liu, M. Patel, E. Farantatos, and N. Bhatt, “Armax-based transfer function model identification using wide-area measurement for adaptive and coordinated damping control,” IEEE Transactions on Smart Grid , vol. 8, no. 3, pp. 1105–1115, May 2017

    work page 2017

  15. [15]

    A dynamic coherency identification method based on frequency deviation signals,

    A. M. Khalil and R. Iravani, “A dynamic coherency identification method based on frequency deviation signals,” IEEE Transactions on Power Systems, vol. 31, no. 3, pp. 1779–1787, May 2016

    work page 2016

  16. [16]

    A new method suitable for real- time generator coherency determination,

    M. B. M. Jonsson and J. Daalder, “A new method suitable for real- time generator coherency determination,” IEEE Transactions on Power Systems, vol. 19, no. 3, pp. 1473–1482, Aug 2004

    work page 2004

  17. [17]

    Generator coherency using the hilberthuang transform,

    N. Senroy, “Generator coherency using the hilberthuang transform,” IEEE Transactions on Power Systems , vol. 23, no. 4, pp. 1701–1708, Nov 2008

    work page 2008

  18. [18]

    Identification of coherent generators using energy function,

    M. H. Haque and A. H. M. A. Rahim, “Identification of coherent generators using energy function,” in IEE Proceedings C - Generation, Transmission and Distribution, July 1990, pp. 255–260

    work page 1990

  19. [19]

    Multi-machine power system dynamic equivalents using artificial intelligence,

    I. D. H. M. Abd-EI-Rehim and M. A. H. Omar, “Multi-machine power system dynamic equivalents using artificial intelligence,” in Power Sys- tems Conference, 2006. MEPCON 2006. Eleventh International Middle East, Dec 2006, pp. 255–260

    work page 2006

  20. [20]

    A coherency-based approach for signal selection for wide area stabilizing control in power systems,

    S. C. S. B. B. Padhy and N. K. Verma, “A coherency-based approach for signal selection for wide area stabilizing control in power systems,” IEEE Systems Journal , vol. 7, no. 4, pp. 807–816, April 2013

    work page 2013

  21. [21]

    Identification of coherent groups and pmu placement for inter-area monitoring based on graph theory,

    M. A. Rios and O. Gomez, “Identification of coherent groups and pmu placement for inter-area monitoring based on graph theory,” in 2011 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America), Nov 2011, pp. 255–260

    work page 2011

  22. [22]

    Coherency identification in power systems through principal component analysis,

    N. F. T. K. K. Anaparthi, B. Chaudhuri and B. C. Pal, “Coherency identification in power systems through principal component analysis,” IEEE Transactions on Power Systems , vol. 20, no. 3, pp. 1658–1660, Aug 2005

    work page 2005

  23. [23]

    Coherency approach by hybrid pso, k-means clustering method in power system,

    E. R. M. Davodi, H. R. Modares and A. Sarikhani, “Coherency approach by hybrid pso, k-means clustering method in power system,” in IEEE 2nd International Power and Energy Conference, 2008 , Jan 2008, pp. 255–260

    work page 2008

  24. [24]

    Wide-area measurement based stabilizing control of large power systems-a decentralized/hierarchical approach,

    I. Kamwa, R. Grondin, and Y . Hebert, “Wide-area measurement based stabilizing control of large power systems-a decentralized/hierarchical approach,” IEEE Transactions on Power Systems , vol. 16, no. 1, pp. 136–153, Feb 2001

    work page 2001

  25. [25]

    Selection of input/output signals for wide area control loops,

    H. Nguyen-Duc, L. A. Dessaint, A. F. Okou, and I. Kamwa, “Selection of input/output signals for wide area control loops,” in IEEE PES General Meeting, July 2010, pp. 1–7

    work page 2010

  26. [26]

    Electromechan- ical mode on-line estimation using regularized robust rls methods,

    N. Zhou, D. Trudnowski, J. Pierre, and W. Mittelstadt, “Electromechan- ical mode on-line estimation using regularized robust rls methods,” in IEEE PES General Meeting , July 2010, pp. 1–1

    work page 2010

  27. [27]

    Electromechanical mode shape estimation based on transfer function 11 identification using pmu measurements,

    N. Zhou, Z. Huang, L. Dosiek, D. Trudnowski, and J. W. Pierre, “Electromechanical mode shape estimation based on transfer function 11 identification using pmu measurements,” in 2009 IEEE Power Energy Society General Meeting , July 2009, pp. 1–7

    work page 2009

  28. [28]

    Electromechanical mode estimation using recursive adaptive stochastic subspace identification,

    S. A. N. Sarmadi and V . Venkatasubramanian, “Electromechanical mode estimation using recursive adaptive stochastic subspace identification,” in 2014 IEEE PES T D Conference and Exposition , April 2014, pp. 1–1

    work page 2014

  29. [29]

    Initial results in power system identification from injected probing signals using a subspace method,

    N. Zhou, J. W. Pierre, and J. F. Hauer, “Initial results in power system identification from injected probing signals using a subspace method,” IEEE Transactions on Power Systems , vol. 21, no. 3, pp. 1296–1302, Aug 2006

    work page 2006

  30. [30]

    Coherency based online wide area control of wind integrated power grid,

    A. Thakallapelli, S. J. Hossain, and S. Kamalasadan, “Coherency based online wide area control of wind integrated power grid,” in 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Dec 2016, pp. 1–6

    work page 2016

  31. [31]

    A. Ng. (2017, Dec.) The k-means clustering algorithm. [Online]. Available: http://cs229.stanford.edu/notes/cs229-notes7a.pdf

    work page 2017

  32. [32]

    Parallel spectral clustering in distributed systems,

    W. Y . Chen, Y . Song, H. Bai, C. J. Lin, and E. Y . Chang, “Parallel spectral clustering in distributed systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 33, no. 3, pp. 568–586, March 2011

    work page 2011

  33. [33]

    Real-time frequency based reduced order modeling of large power grid,

    A. Thakallapelli, S. Ghosh, and S. Kamalasadan, “Real-time frequency based reduced order modeling of large power grid,” in 2016 IEEE Power and Energy Society General Meeting (PESGM) , July 2016, pp. 1–5

    work page 2016

  34. [34]

    A pid controller for real-time dc motor speed control using the c505c microcontroller,

    S. Kamalasadan and A. Hande, “A pid controller for real-time dc motor speed control using the c505c microcontroller,” in CAINE, 2004, pp. 1–8

    work page 2004

  35. [35]

    Real-time reduced order model based adaptive pitch controller for grid connected wind turbines,

    A. Thakallapelli, S. Ghosh, and S. Kamalasadan, “Real-time reduced order model based adaptive pitch controller for grid connected wind turbines,” in 2016 IEEE Industry Applications Society Annual Meeting , Oct 2016, pp. 1–8

    work page 2016

  36. [36]

    ˚Astr¨om and B

    K. ˚Astr¨om and B. Wittenmark, Adaptive Control: Second Edition , ser. Dover Books on Electrical Engineering. Dover Publications, 2013. [On- line]. Available: https://books.google.com/books?id=4CLCAgAAQBAJ

    work page 2013

  37. [37]

    Alternating direction method of multipliers (admm) based distributed approach for wide-area control,

    A. Thakallapelli and S. Kamalasadan, “Alternating direction method of multipliers (admm) based distributed approach for wide-area control,” in 2017 IEEE Industry Applications Society Annual Meeting , Oct 2017, pp. 1–7

    work page 2017

  38. [38]

    Distributed optimization algorithms for wide-area oscillation monitoring in power systems using interregional pmu-pdc architectures,

    S. Nabavi, J. Zhang, and A. Chakrabortty, “Distributed optimization algorithms for wide-area oscillation monitoring in power systems using interregional pmu-pdc architectures,” IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2529–2538, Sep 2015

    work page 2015

  39. [39]

    On the value functions of the discrete- time switched lqr problem,

    W. Zhang, J. Hu, and A. Abate, “On the value functions of the discrete- time switched lqr problem,” IEEE Transactions on Automatic Control , vol. 54, no. 11, pp. 2669–2674, Nov 2009

    work page 2009

  40. [40]

    A two-stage kalman filter approach for robust and real-time power system state estimation,

    J. Zhang, G. Welch, G. Bishop, and Z. Huang, “A two-stage kalman filter approach for robust and real-time power system state estimation,” IEEE Transactions on Sustainable Energy , vol. 5, no. 2, pp. 629–636, April 2014

    work page 2014

  41. [41]

    Doubly fed induction generator using back-to-back pwm converters and its application to variable-speed wind-energy generation,

    R. Pena, J. C. Clare, and G. M. Asher, “Doubly fed induction generator using back-to-back pwm converters and its application to variable-speed wind-energy generation,” IEE Proceedings - Electric Power Applica- tions, vol. 143, no. 3, pp. 231–241, May 1996. A. Thakallapelli (S’14) received his B.Tech degree in Electrical Engineering from Acharya Nagarjuna ...

    work page 1996

  42. [42]

    He is currently a PhD candidate at University of North Carolina at Charlotte

    He worked as a software engineer in Samsung Research and Development institute Bangladesh from 2013 to 2015. He is currently a PhD candidate at University of North Carolina at Charlotte. His research interests are distributed energy systems in- tegration, modeling and control, and wide area mon- itoring, optimization and control of power system. S. Kamala...

    work page 2013