Visualizing Power Frequency Dynamics Using D-Partitioning

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


In this paper we introduce D-Partitioning as a convenient direct method for visualizing the performance of power systems subjected to load changes.  In Automatic Generation Control (AGC) schemes, such visualizations rely on manual calculations of the system stability limits. D-Partitioning obviates this reliance permitting a direct reading-off of the limits. The method assumes a test power system of known parameters embedding distributed solar power generation. The generated solar power injects discretely variable real power system disturbances. MATLAB/SIMULINK is used to mask the parameters of the system transfer function thus allowing for data entry or changes via a user interface. The system frequency behaviour is analyzed applying traditional methods within MATLAB and MATHEMATICA.  Subsequent comparative results are established by applying D-Partitioning. Nyquist stability criterion and step frequency response results show that the system's stability limits cannot be discerned with certainty visually but only manually. In contrast D-Partitioning provides visually clearer stability limits. The paper provides an additional tool for the analysis of power systems described by transfer functions possibly with several variable parameters. The MATLAB User Interface used in the paper limits any flexible variation of the parameters.  An improvement will be considered in ongoing studies by applying on the named parameters, MATHEMATICA's dynamic interactivity feature.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


AGC; Matlab/Simulink; Mathematica; D-Partitioning; Stability

Full Text:



Yanev, K.M., Masupe, S., Robust design and efficiency in case of parameters uncertainties, disturbances and noise, (2012) International Review of Automatic Control (IREACO), 5 (6), pp. 860-867.

Masupe S., Yanev K. M., Design and D-Partitioning of Optimal Control System Compensation, (2011) International Review of Automatic Control (IREACO), 4 (6), pp. 838-845.

Yanev K.M, Anderson G.O., Masupe S., Multivariable System's Parameters Interaction and Robust Control Design, (2011) International Review of Automatic Control (IREACO), 4 (2), pp. 180-190.

Bergen A. R., Vittal V., “Power Systems Analysis, 2nd Edition”, Prentice Hall, 2000.

Camacho E.F., Samad T., Garcia-Sanz M., and Hiskens I., “Control for renewable Energy and SmartGrids”, Available at, Accessed October 2013

Abdul Jaleel, J., Rekhasree, R.L., A comparative study on AGC of power systems using reinforcement learning and genetic algorithm, (2013) International Review of Automatic Control (IREACO), 6 (4), pp. 404-409.

Hadi Saadat, Power System Analysis, 2nd Edition McGraw-Hill, 2004.

Cheng Y., Sahni M.S., Generation & Transmission Planning PwrSolutions Inc.“ALTI-ESS Automatic Generation Control (AGC) Study”, February 2012, Available at, Accessed October 2013.

Wang L., Chen D., Siemens Energy Inc. – Power Technology, Issue 107, “Automatic Generation Control (AGC) Dynamic Simulation in PSS®E, Available at, Accessed October 2013

California Independent System Operator, “California Integration of Renewable Analysis”, Available at,, November 2007, Accessed October 2013.

Seyed Abbas Taher and Reza Hematti, “Robust Decentralized Load Frequency Control Using Multi Variable QFT Method in Deregulated Power Systems”, American Journal of Applied Sciences, Vo. %, No.7, pp 818-828, ISSN 1546-9239, 2008

Mathematica, Wolfram Technology Guide – Dynamic Instant Interactivity,,Accessed July 2013


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize