Artificial Neural Network Application for Contingency Ranking Based on Condition Number Incorporating IPFC

Author Name: Suresh Babu Daram, P.S.Venkataramu, M.S.Nagaraj
Author Email: *sureshbabudaram@gmail.com

Abstract

This paper investigates the effect of incorporating an IPFC on Contingency Ranking(CR). The contingencies are ranked based Condition Number (CN) of the Jacobian matrix of Newton- Raphson load flow technique. The contingency ranking was done for single transmission line outage conditions with and without incorporating IPFC. Further, the ranking was done under system load enhancement condition. Suitable Artificial Neural Network (ANN) architectures are designed to predict the contingency ranking of a system. The results obtained based on case studies carried out on IEEE-6 Bus System and Indian Utility  UPSEB-75 Bus System are presented. MATLAB environment was used for simulation purpose.

Introduction

One of the major tasks of EMS is to carry out the contingency analysis. Steady state contingency analysis is carried out on real power systems for evaluating the performance of the system and to determine the necessary control actions. Generally it is not feasible and required to consider all the possible contingencies in the system for such study, hence credible contingencies are required to be ranked based on their relative severity. The study of contingency is an essential activity during power system planning, operation and control. So, power system security is one of the important concerns for power engineers to maintain the system stability for all kinds of outages. In some cases of contingency, the effects may lead to transmission line overloads or bus voltage limit violations. As the process of cascading outages in the system may lead to complete black out or collapse of the system. Static system results are very helpful to the system operators to secure the system during any transmission line outage in the system.  To overcome such issues, there is a need to identify such contingencies. Contingency screening and ranking process accurately determines the possibility of specific contingencies which may cause power system instability based on their severity. Contingency selection is one such study which has the ability to identify the critical contingency in the system. Suitable preventive control actions can be implemented considering contingencies that are likely to affect the power system performance.

As the recent power systems are experiencing the threat of voltage instability, the contingencies are required to be ranked incorporating voltage stability phenomenon. The curve fitting approach has been proposed for contingency ranking in [1]. The change in the estimation of the loading margin to voltage collapse when a line outage occurs is compared by continuation power flow to obtain a nominal loading margin, and then linear quadratic sensitivities of the loading margin to each contingency are computed to find the resulting change in the loading margin [2]. The second order information derived from the Singular Value Decomposition (SVD) analysis of the load flow Jacobian matrix to obtain the contingency ranking [3]. A methodology for on-line voltage stability assessment for the voltage collapse point is determined through an extrapolation technique based on target vector behavior in [4]. A powerful procedure for identifying severe single branch outage contingency with respect to saddle node bifurcation induced voltage collapse, given an operating point, a load demand forecast and a generation dispatch [5]. The target vector where the idea consists of monitoring target vector norm associated with each contingency is given in [6]. The static voltage collapse indicator (LF-index) for the ranking of line outage contingency utilizing normal power flow calculations is developed. The maximum power transfer theory is used to calculate this indicator. The purpose of this indicator is to quantify the proximity of a particular operating point to voltage collapse [7].  The severity based on super components which refer to complex electrical facilities as: substations, generation plants and multiple circuit transmission lines. An outage of the super component implies the multiple and simultaneous outages of many elements. These kinds of events are not, generally, considered in the mentioned analyses; however, it would be very important to keep them in mind in the security evaluation of power systems [8]. The single line outage contingency ranking through condition number of the Jacobian matrix obtained from the Newton – Raphson load flow method is in [9].

Conclusion

The effect of installing a multi-transmission FACTS controller, i.e., IPFC in the system under single line outage conditions has been presented in this chapter. The proposed model of IPFC has been incorporated to understand the system under line outage condition. The system stability based on condition number (CN) without IPFC is compared with IPFC. Further, ANN architectures are developed for accurate for on-line prediction of contingency ranking indices without and with incorporation of IPFC in the system. The results show a very good potential for incorporation of these architectures into the online security assessment modules in EMS

References

  1. C. Ejebe, G. D. Irisarri, S. Mokhtari, O. Obadina, P. Ristanovic and J. Tong, “Methods for contingency screening and ranking for voltage stability analysis of power systems”, IEEE Transactions on power systems, Vol. 11, No. 1, Feb. 1996, pp. 350-356.
  2. Scott Greene, Ian Dobson, Fernando L. Alvarado, ‘Contingency ranking for voltage collapse via sensitivities from a single nose curve’, IEEE Trans. On Power Systems, Vol.14, No.1, Feb 1999, pp 232-239.
  3. Alberto Berizzi, Yong-Gang Zeny, Paolo Marannno, Alsesandro Vaccarini, Pierangela A.Scarpellini, ‘A Second Order method for Contingency severity assessment with respect to voltage collapse’, IEEE Trans. On Power Systems, Vol.15, No.1, Feb 2000, pp 81-87.
  4. Antonio C.Zambroni de Souza, Julio C. Stachhini de Souza, Armando M. Leite de Silva,’Online Voltage stability monitoring’, IEEE Trans. On Power Systems, Vol.15, No.4, Nov 2000, pp 1300-1305.
  5. J.Fluek, Renuka Gonella, Jayabharath R. Dondeti, ‘A new power sensitivity method of ranking branch outage contingencies for voltage collapse’, IEEE Trans. On Power Systems, Vol.17, No.2, May 2002, pp 265-270.
  6. Acozambroni de Souza.A.C, Alves da silva.A.P, Jorge L.A,Jardim, SilvaNeto.C.A Torres.G.L, Claudio Ferrerira, Araiyo Ferreira.L.C,’ A new Contingency analysis approach for voltage collapse assessment’, International Journal on Electric Power and Energy Systems, Vol. 25, Jan 2003, pp 781-785.
  7. S. Venkataramu, “Power System Studies incorporating Voltage Collapse Phenomenon”, a PhD thesis, Visvesvaraya Technological University, India, 2006.
  8. A. Caro, M. A. Ríos, “Super Components Contingency Modeling for Security Assessment in Power Systems”, IEEE Latin America Transactions, Vol. 7, No. 5, September 2009, pp 552-559.
  9. Suresh Babu Daram, P. S. Venkataramu, M. S. Nagaraj, “Condition Number Based Contingency Ranking under Line Outage Condition Incorporating IPFC”, International Conference on Renewable Energy Utilization, Dept. of EEE, Coimbatore Institute of Technology, Coimbatore, India, January 2016.
  10. H.B, Ekwue.A.O, “Artificial neural network based contingency ranking method for voltage collapse”, International Journal on Electric Power and Energy Systems, Vol.22, 2000, pp 349-354.
  11. D, Yegnanarayana.B, Ramar.K, “Radial basis function networks for fast contingency ranking”, International Journal on Electric Power and Energy Systems, Vol.24, 2002, pp 387-395
  12. Manjaree Pandit, Laxmi Srivastava, Jaydev Sharma, “Fast voltage contingency selection using fuzzy parallel self-organizing hierarchical neural network”, IEEE Trans. On Power Systems, Vol.18, No.2, May 2003, pp 657-664.
  13. T, Srivastava..L, Singh.S.N, “Fast contingency screening using radial basis function neural network”, IEEE Trans. On Power Systems, Vol.18, No.4, Nov.2003, pp 1359-1365.
  14. P.Zhang, “Robust Modeling of the Interline Power Flow Controller and the Generalized Unified Power Flow Controller with Small Impedances in Power Flow Analysis”, Electrical Engineering, Vol.89 no.1. October 2006, p.gno:1-9.
  15. Karthik, I. Alagarasan, S. Chandrasekhar, “Optimal Location of Interline Power Flow Controller For Controlling Multi-Transmission Line: A New integrated Technique”, Research Article Electrical Electronics Engg, Higher Education Press and Springer-2012, PP 447-458.
  16. Yang Ye, Mehrdad Kazerani, “Power flow control schemes for series-connected FACTS controllers”, Electric Power Systems Research, 76 (2006) 824–831.
  17. Akanksha Mishra, Venkata Nagesh Kumar Gundavarapu, “Contingency management of power system with Interline Power Flow Controller using Real Power Performance Index and Line Stability Index”, Ain Shams Engineering Journal, Volume 7, Issue 1, March 2016, Pages 209-222.
  18. An Improved Steady-State Model of an Interline Power Flow Controller for the Multi-Transmission System, International Journal of Grid and Distributed Computing, Vol.9, No.5, May 2016, pp.13-24.
  19. Hadi Sadat, “Power System Analysis” Tata McGraw-Hill Edition 2001

447 total views, 1 views today

Download File

About the author: admin