[en] Computation of the closed-loop control laws, capable
to realize multiple switching operations of a resistive brake (RB)
aimed to enhance power system stability, is the primary topic
of this brief. The problem is formulated as a multistage decision
problem and use of a model-based reinforcement learning
(RL) method, known as prioritized sweeping, to compute the
control law is considered. To illustrate the performances of the
proposed approach results obtained using the model of a synthetic
four-machine power system are given. Handling measurement
transmission delays is discussed and illustrated.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Glavic, Mevludin ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète
Language :
English
Title :
Design of a Resistive Brake Controller for Power System Stability Enhancement Using Reinforcement Learning
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
P. Kundur, Power System Stability and Control. New York: McGraw Hill, 1994.
W. H. Croft and R. H. Hertley, “Improving transient stability by use of dynamic braking,” IEEE Trans. Power App. Syst., vol. PAS-59, no. 1, pp. 17–26, Jan.-Feb. 1962.
M. L. Shelton, R. F. Winkelman, W. A. Mittelstadt, and W. L. Bellerby, “Bonneville power administration 1400 MW braking resistor,” IEEE Trans. Power App. Syst., vol. PAS-94, no. 2, pp. 602–611, Mar.-Apr. 1975.
S. S. Joshi and D. G. Tamaskar, “Augmentation of transient stability limit of a power system by automatic multiple application of dynamic braking,” IEEE Trans. Power App. Syst., vol. PAS-104, no. 11, pp. 3004–3012, Nov. 1985.
“State of the art in non classical means to improve power system stability,” Electra, no. 118, pp. 87–113, 1988.
H. Jiang, D. T. Habelter, and K. V. Eckroth, “A cost effective generator brake for improved generator transient response,” IEEE Trans. Power Syst., vol. 9, no. 4, pp. 1840–1846, Nov. 1994.
D. L. Lubkeman and G. T. Heydt, “The application of dynamic programming in a discrete supplementary control for transient stability enhancement of multimachine power system,” IEEE Trans. Power App. Syst., vol. PAS-104, no. 9, pp. 2342–2348, Sep. 1985.
Y. Wang, R. R. Mohler, R. Spee, and W. Mittelstadt, “Variable structure FACTS controllers for power system transient stability,” IEEE Trans. Power Syst., vol. 7, no. 1, pp. 307–313, Feb. 1992.
Y. Wang, W. Mittelstadt, and D. J. Maratukulam, “Variable structure braking resistor control in a multimachine power system,” IEEE Trans. Power Syst., vol. 9, no. 3, pp. 1557–1562, Aug. 1994.
A. H. M. A. Rahim and D. A. H. Alamgir, “A closed-loop quasi-optimal dynamic braking resistor and shunt reactor control strategy for transient stability,” IEEE Trans. Power Syst., vol. 3, no. 3, pp. 879–886, Aug. 1988.
A.H.M.A. Rahim, A. M. Al-Shehri, and A. I. J. Al-Sammak, “Optimum control strategies for transient as well as oscillatory instability of power systems,” IEEE Trans. Power Syst., vol. 8, no. 2, pp. 491–496, May 1993.
B. Das, A. Ghosh, and P. Sachchidanand, “A novel control strategy for a braking resistor,” Int. J. Elect. Power Energy Syst., vol. 20, no. 6, pp. 391–403, 1998.
T. Hiyama, M. Mishiro, and H. Kihara, “Fuzzy logic switching of thyristor controlled braking resistor considering coordination with SVC,” IEEE Trans. Power Del., vol. 10, no. 4, pp. 2020–2026, Oct. 1995.
A. H. M. A. Rahim and S. A. Al-Baiyat, “Dynamic brake switching strategies for stabilization of power systems using artificial neural networks,” Expert Syst. Appl., vol. 18, pp. 101–109, 2000.
J. Machowski, A. Smolarczyk, and J. W. Bialek, “Damping of power swings by control of braking resistors,” Int. J. Elect. Power Energy Syst., vol. 23, pp. 539–549, 2001.
Russian Far East Interconnected Power System Emergency Stability Control, A. A. Grobovoy.http://www.transmission.bpa.gov/orgs/opi/Power_Stability/ [Online].
R. Bellman, Dynamic Programming. Princeton, NJ: Princeton Univ. Press, 1957.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 1998.
D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming. Belmont, MA: Athena Scientific, 1996.
A. W. Moore and C. G. Atkeson, “Prioritized sweeping: reinforcement learning with less data and less real time,” Mach. Learn., vol. 13, pp. 103–130, 1993.
M. Glavic, D. Ernst, and L. Wehenkel, “A reinforcement learning based discrete supplementary control for power system transient stability enhancement,” in Proc. Intelligent Systems Application in Power Systems (ISAP'03), Lemnos, Greece, 2003, Paper ISAP03/012.
C. C. Liu, W. Jung, G. T. Heydt, V. Vittal, and A. G. Phadge, “The Strategic Power Infrastructure Defense (SPID) system,” IEEE Control Syst. Mag., vol. 20, pp. 40–52, 2000.
J. Jung, C. C. Liu, S. L. Tanimoto, and V. Vittal, “Adaptation in load shedding under vulnerable operating conditions,” IEEE Trans. Power Syst., vol. 17, no. 4, pp. 1199–1205, Nov. 2002.
D. Ernst, M. Glavic, and L. Wehenkel, “Power system stability control: reinforcement learning framework,” IEEE Trans. Power Syst., vol. 19, no. 1, pp. 427–436, Feb. 2004.
M. Pavella, D. Ernst, and D. Ruiz-Vega, Transient Stability of Power System—A Unified Approach to Assessment and Control. Norwell, MA: Kluwer, 2000.
K. V. Katsikopoulos and S. E. Engelbrecht, “Markov decision process with delays and asynchronous cost collection,” IEEE Trans. Autom. Control, vol. 48, no. 4, pp. 568–574, Apr. 2003.
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
