Power System Security Assessment and Enhancement: A Bibliographical Survey


Power system security assessment and enhancement are two major crucial issues in a large interconnected power system. System security can be classified on the basis of major functions that are carried out in control centers, namely system monitoring, contingency analysis and security enhancement. The key element involved in security assessment is contingency analysis. In real-time, all contingencies may not cause same severity level. To eliminate non-violation cases and select only critical cases, called contingency analysis, the idea of severity/performance indices seems to fulfill this need. Security enhancement incorporates security constrained optimal power flow (SCOPF) which ensures that system is operating at normal state by taking preventive and corrective control actions so that no contingencies result in violations. SCOPF recommends controller actions to optimize specific objective function such as fuel cost, power losses, emission that subject to a set of power system operating constraints. This paper presents a literature review on two topics which are reviewed in chronological order of appearance; security assessment and enhancement. We explore both traditional and soft computing techniques for assessing system security and enhancement of the power system and also identify key areas for future research.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2


\(P_{k}\) :

Active power flow in kth line

\(P_{k}^{\hbox{max} }\) :

Maximum active power flow in kth line

\({\text{nl}},\,{\text{nb}}\) :

Total number of lines and buses in the network

\(n\) :

Exponent component of index

\(w_{i}\) :

Weighing factor

\(V_{i}\) :

Voltage magnitude of ith bus

\(V_{{i,{\text{sp}}}}\) :

Specified voltage of ith bus

\(\Delta V_{i}^{\lim }\) :

Maximum voltage deviation in ith bus

\(P_{\text{LV}} ,\,P_{{{\text{L}}\delta }}\) :

Severity indices related to voltage and phase angles

\(G_{ik}\) :

Conductance of the transmission line connected between bus i and k

\(\delta_{i}\) :

Phase angle of ith bus

\(d_{{{\text{v}},i}}^{\text{u}} ,\,d_{{{\text{v}},i}}^{\text{l}}\) :

Upper and lower voltage limits of ith bus

\(F_{i}^{\text{u}} ,\,F_{i}^{\text{l}}\) :

Upper and lower voltage alarm limits of ith bus

\(g_{{{\text{v}},i}}^{\text{u}} ,\,g_{{{\text{v}},i}}^{\text{l}}\) :

Normalized upper and lower factors

\(V_{i}^{\text{d}}\) :

Desired voltage at each bus

\(V_{i}^{\text{u}} ,\,V_{i}^{\text{l}}\) :

Upper and lower voltage security limits

\(d_{\text{p,j}} ,g_{{{\text{p}},j}}\) :

Line limits violation vector and normalized vector

\(P_{{{\text{p}},j}} ,\,P_{{{\text{F}},j}}\) :

Security and alarm power flow limits

\(\left| {P_{j} } \right|\) :

Absolute value of the power flows in the jth line

\(\bar{\theta }\) :

Generator rotor angle

\(t_{\text{c1}}\) :

Fault clearance time

\({\text{NG}}\) :

Number of generators

\(T\) :

Length of period after fault clearing

\(V_{\text{ke}} ,\,V_{\text{pe}}\) :

Transient kinetic and potential energy

\(M_{i}\) :

Inertia of ith generator

\(M_{\text{T}}\) :

Total inertia of all generators

\(P_{\text{a}}\) :

Accelerating power

\(P_{\text{mi}}\) :

Mechanical input power

\(P_{\text{ei}}\) :

Electrical output power

\(\theta_{i} ,\,\theta_{i}^{\text{cl}}\) :

Rotor angles with respective to COI and fault clearing time of ith generator

\(\delta_{{{\text{c}}i,\hbox{max} }}\) :

Maximum load angle deviation

\(\delta_{{{\text{c}},\hbox{max} ,{\text{adm}}}}\) :

Maximum admissible load angle deviation is given by the relay

\(\Delta f_{i,\hbox{max} }\) :

Maximum frequency deviation

\(\Delta f_{{\hbox{max} ,{\text{adm}}}}\) :

Maximum admissible frequency deviation

\(v_{i,\hbox{min} }\) :

Minimum instantaneous voltage during transients

\(v_{{i,\hbox{min} ,{\text{adm}}}}\) :

Minimum admissible voltage

\(\Delta v_{{i,{\text{aft}}}}\) :

Voltage deviation at the end of transient period

\(\Delta v_{i,\lim }\) :

Maximum voltage deviation

\(P_{{i,{\text{aft}}}}\) :

Power flow through the line at the end of transient period

\(P_{i,\lim }\) :

Maximum admissible power flow

\(\delta_{\text{coa}}\) :

Centre of load angle


  1. 1.

    B. Stott, O. Alsac, A.J. Monticelli, Security analysis and optimization. Proc. IEEE 75(12), 1623–1644 (1987)

    Google Scholar 

  2. 2.

    N. Balu, T. Bertram, A. Bose, On-line power system security analysis. Proc. IEEE 80(2), 262–280 (1992)

    Google Scholar 

  3. 3.

    N. A. E. R. Council, Reliability Concepts in Bulk Power Electric Systems (1985)

  4. 4.

    K. Morison, L. Wang, P. Kundur, Power system security assessment. IEEE Power Energy Mag. 2, 30–39 (2004)

    Google Scholar 

  5. 5.

    A.J. Wood, B.F. Wollenberg, Power Generation Operation and Control, 2nd edn. (Wiley India Private Limited, New Delhi, 2006)

    Google Scholar 

  6. 6.

    R.P. Schulz, W.W. Price, Classification and identification of power system emergencies. IEEE Trans. Power Appar. Syst. 103(12), 3470–3479 (1984)

    Google Scholar 

  7. 7.

    EPRI Rep. EPRI EL-5290, Composite-System Reliability Evaluation: Phase IScoping Study

  8. 8.

    F.F. Wu, Real-time network security monitoring, assessment and optimization. Int. J. Electr. Power Energy Syst. 10(2), 83–100 (1988)

    Google Scholar 

  9. 9.

    H. Glavitsch, Switching as means of control in the power system. Int. J. Electr. Power Energy Syst. 7(2), 92–100 (1985)

    Google Scholar 

  10. 10.

    M. Shahidehpour, W.F. Tinney, Y. Fu, Impact of security on power systems operation. Proc. IEEE 93(11), 2013–2025 (2005)

    Google Scholar 

  11. 11.

    G.C. Ejebe, B.F. Wollenberg, Automatic contingency selection. IEEE Trans. Power Appar. Syst. 98(1), 97–109 (1979)

    Google Scholar 

  12. 12.

    R. Caglar, A. Ozdemir, F. Mekic, Contingency selection based on real power transmission losses, PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376), vol. C, no. 11, p. 1982 (1999)

  13. 13.

    K. Nara, K. Tanaka, H. Kodama, R.R. Shoults, M.S. Chen, P. Van Olinda, D. Bertagnolli, On-line contingency selection algorithm for voltage security analysis. IEEE Trans. Power Appar. Syst. 104(4), 846–856 (1985)

    Google Scholar 

  14. 14.

    R. Sunitha, K.R. Sreerama, A.T. Mathew, Development of a composite security index for static security evaluation, in IEEE Region Annual International Conference, Proceedings/TENCON, pp. 1–6, 2009

  15. 15.

    B. Stott, O. Alsac, Fast decoupled load flow. IEEE Trans. Power Appar. Syst. 93(3), 859–869 (1974)

    Google Scholar 

  16. 16.

    A.J. Wood, B.F. Wollenberg, Power Generation, Operation and Control, 2nd edn. (Wiley, New York, 1996)

    Google Scholar 

  17. 17.

    P.W. Sauer, On the formulation of power distribution factors for linear load flow methods. IEEE Trans. Power Appar. Syst. 100(2), 764–770 (1981)

    Google Scholar 

  18. 18.

    A. Mohamed, G.B. Jasmon, Voltage contingency selection technique for security assessment. IEEE Proc. Gener. Transm. Distrib. 136(1), 24 (1989)

    Google Scholar 

  19. 19.

    C.I. Faustino Agreira, C.M. MacHado Ferreira, J.A. Dias Pinto, F.P. MacIel Barbosa, Contingency screening and ranking algorithm using two different sets of security performance indices, in 2003 IEEE Bologna PowerTech - Conference Proceedings, vol. 4, pp. 155–159 (2003)

  20. 20.

    G. Irisarri, A. Sasson, An automatic contingency selection method for on-line security analysis. IEEE Trans. Power Appar. Syst. 100(4), 1838–1844 (1981)

    Google Scholar 

  21. 21.

    T.A. Mikolinnas, B.F. Wollenberg, An advanced contingency selection algorithm. IEEE Trans. Power Appar. Syst. 100(2), 608–617 (1981)

    Google Scholar 

  22. 22.

    T.F. Halpin, R. Fischl, R. Fink, Analysis of automatic contingency selection algorithms. IEEE Trans. Power Appar. Syst. 103(5), 938–945 (1984)

    Google Scholar 

  23. 23.

    K.L. Lo, Z. Meng, Newton-like method for line outage simulation. IEE Proc. Gener. Transm. Distrib. 151, 225–231 (2004)

    Google Scholar 

  24. 24.

    J. Zaborszky, K.-W. Whang, K. Prasad, Fast contingency evaluation using concentric relaxation. IEEE Trans. Power Appar. Syst. 99(1), 28–36 (1980)

    Google Scholar 

  25. 25.

    V. Brandwajn, Efficient bounding method for linear contingency analysis. IEEE Trans. Power Syst. 3(1), 38–43 (1987)

    Google Scholar 

  26. 26.

    V. Brandwajn, M.G. Lauby, Complete bounding method for AC contingency screening. IEEE Trans. Power Syst. 4(2), 724–729 (1989)

    Google Scholar 

  27. 27.

    F.D. Galiana, Bound estimates of the severity of line outages in power system contingency analysis and ranking. IEEE Trans. Power Appar. Syst. 103(9), 2612–2624 (1984)

    Google Scholar 

  28. 28.

    G.C. Ejebe, H.P. Van Meeteren, B.F. Wollenberg, Fast contingency screening and evaluation for voltage security analysis. IEEE Trans. Power Syst. 3(4), 1582–1590 (1988)

    Google Scholar 

  29. 29.

    S.N. Singh, S.C. Srivastava, Improved voltage and reactive power distribution factors for outage studies. IEEE Trans. Power Syst. 12(3), 1085–1093 (1997)

    Google Scholar 

  30. 30.

    R. Diao, S. Member, K. Sun, V. Vittal, R.J.O. Keefe, M.R. Richardson, N. Bhatt, D. Stradford, S.K. Sarawgi, Decision tree-based online voltage security assessment using PMU measurements. IEEE Trans. Power Syst. 24(2), 832–839 (2009)

    Google Scholar 

  31. 31.

    S. Weerasooriya, M.A. El-Sharkawi, M. Damborg, R.J. Marks, Towards static-security assessment of a large-scale power system using neural networks. IEE Proc. C Gener. Transm. Distrib. 139(1), 64 (1992)

    Google Scholar 

  32. 32.

    Q. Zhou, J. Davidson, A.A. Fouad, Application of artificial neural networks in power system security and vulnerability assessment. IEEE Trans. Power Syst. 9(1), 525–532 (1994)

    Google Scholar 

  33. 33.

    M. Khazaei, S. Jadid, Contingency ranking using neural networks by Radial Basis Function method, in 2008 IEEE/PES Transmission and Distribution Conference and Exposition, pp. 25–28, 2008

  34. 34.

    S. Kalyani, K.S. Swarup, Study of neural network models for security assessment in power systems,” vol. 1, no. 2 (2009)

  35. 35.

    A. I.G. Dagmar Niebur, Power system static security assessment using the kohonenneuralnetworkclassifier (1991)

  36. 36.

    D. Niebur, A.J. Germond, Power system static security assessment using the Kohonen neural network classifier. IEEE Trans. Power Syst. 7(2), 865–872 (1992)

    Google Scholar 

  37. 37.

    D. Devaraj, J. Preetha Roselyn, On-line voltage stability assessment using radial basis function network model with reduced input features. In. J. Electr. Power Energy Syst. 33(9), 1550–1555 (2011)

    Google Scholar 

  38. 38.

    D. Devaraj, B. Yegnanarayana, K. Ramar, Radial basis function networks for fast contingency ranking. Int. J. Electr. Power Energy Syst. 24, 387–395 (2002)

    Google Scholar 

  39. 39.

    R. Fischl, Application of neural networks to power system security: technology and trends, in Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN’94), vol. 6, pp. 3719–3723, 1901

  40. 40.

    R. Napoli, F. Piglione, On-line static security assessment of power systems by a progressive learning neural network, Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96), vol. 3, 1996

  41. 41.

    D.S. Javan, H.R. Mashhadi, M. Rouhani, Static security assessment using radial basis function neural networks based on growing and pruning method, in 2010 IEEE Electrical Power & Energy Conference, pp. 1–6, 2010

  42. 42.

    H.M. Khattab, S.F. Abdelaziz, A.Y. Mekhamer, M.A.L. Badr, E.F. El-Saadany, Gene expression programming for static security assessment of power systems. IEEE Power Energy Soc. Gener. Meet. 2012, 1–8 (2012)

    Google Scholar 

  43. 43.

    A.Y. Abdelaziz, S.F. Mekhamer, M.A.L. Badr, H.M. Khattab, Probabilistic neural network classifier for static voltage security assessment of power systems. Electr. Power Compon. Syst. 40(2), 147–160 (2011)

    Google Scholar 

  44. 44.

    T.S. Sidhu, Contingency screening for steady-state security analysis by using FFT and artificial neural networks”. IEEE Trans. Power Syst. 15(I), 421–426 (2000)

    Google Scholar 

  45. 45.

    S.N. Singh, L. Srivastava, J. Sharma, Fast voltage contingency screening using radial basis function neural network. Electr. Power Syst. Res. 18(4), 1359–1366 (2003)

    Google Scholar 

  46. 46.

    R.K. Misra, S.P. Singh, Efficient ANN method for post-contingency status evaluation. Int. J. Electr. Power Energy Syst. 32(1), 54–62 (2010)

    Google Scholar 

  47. 47.

    K.S. Swarup, Artificial neural network using pattern recognition for security assessment and analysis. Neurocomputing 71(4–6), 983–998 (2008)

    Google Scholar 

  48. 48.

    S. Kalyani, K. Shanti Swarup, Classification and assessment of power system security using multiclass SVM. IEEE Trans. Syst. Man Cybern. Appl. Rev. 41(5), 753–758 (2011)

    Google Scholar 

  49. 49.

    S.-W. Lin, K.-C. Ying, S.-C. Chen, Z.-J. Lee, Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst. Appl. 35(4), 1817–1824 (2008)

    Google Scholar 

  50. 50.

    Z. Shaowu, W. Lianghong, Y. Xiaofang, T. Wen, Parameters selection of SVM for function approximation based on differential evolution, in Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007), 2007

  51. 51.

    S.-J. Huang, Static security assessment of a power system using query-based learning approaches with genetic enhancement. IEEE Proc. Gener. Transm. Distrib. 148(4), 319 (2001)

    Google Scholar 

  52. 52.

    S. Noman, S.M. Shamsuddin, A.E. Hassanien, Hybrid learning enhancement of RBF network with particle swarm optimization. Foundations 1, 381–397 (2009)

    Google Scholar 

  53. 53.

    S. Kalyani, K.S. Swarup, Particle swarm optimization based K-means clustering approach for security assessment in power systems. Expert Syst. Appl. 38(9), 10839–10846 (2011)

    Google Scholar 

  54. 54.

    A. Mohamed, S. Maniruzzaman, A. Hussain, Static security assessment of a power system using genetic-based neural network. Electr. Power Compon. Syst. 29(12), 1111–1121 (2001)

    Google Scholar 

  55. 55.

    S. Hashemi, M.R. Aghamohammadi, Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network. Int. J. Electr. Power Energy Syst. 49, 86–94 (2013)

    Google Scholar 

  56. 56.

    K. Morison, L. Wang, H. Hamadani, New tools for blackout prevention, in IEEE PES Power Systems Conference and Exposition, PSCE - Proceedings, pp. 319–324, 2006

  57. 57.

    U. Kerin, G. Bizjak, E. Lerch, O. Ruhle, R. Krebs, Dynamic security assessment using time-domain simulator, in IEEE/PES Power Systems Conference and Exposition, pp. 1–6, 2009

  58. 58.

    Y. Zhang, L. Wehenkel, P. Rousseaux, M. Pavella, SIME: a hybrid approach to fast transient stability assessment and contingency selection. Int. J. Electr. Power Energy Syst. 19(3), 195–208 (1997)

    Google Scholar 

  59. 59.

    F.A. Rahimi, M.G. Lauby, J.N. Wrubel, K.L. Lee, Evaluation of the transient energy function method for on-line dynamic security analysis. IEEE Trans. Power Syst. 8(2), 497–507 (1993)

    Google Scholar 

  60. 60.

    A.A. Fouad, V. Vittal, The transient energy function method. Int. J. Electr. Power Energy Syst. 10(4), 233–246 (1988)

    Google Scholar 

  61. 61.

    C.L. Chang, Y.Y. Hsu, A new approach to dynamic contingency selection. IEEE Trans. Power Syst. 5(4), 1524–1528 (1990)

    Google Scholar 

  62. 62.

    C.A. Baone, N. Acharya, S. Veda, N.R. Chaudhuri, Fast contingency screening and ranking for small, pp. 1–5, 2014

  63. 63.

    H.K.H. Kim, C.S.C. Singh, Steady state and dynamic security assessment in composite power systems, in Proceedings of the International Symposium on Circuits and Systems, 2003. ISCAS’03, vol. 3, pp. III–320–III–323, 2003

  64. 64.

    M. Tang, Q. Zhu, K. Yang, H. Zhang, X. Li, Simplified algorithm of voltage security correction based on sensitivity analysis method, no. 1, pp. 0–3, 2012

  65. 65.

    E. Ciapessoni, D. Cirio, S. Massucco, A. Morini, A. Pitto, F. Silvestro, Risk-based dynamic security assessment for power system operation and operational planning. Energies 10(4), 475 (2017)

    Google Scholar 

  66. 66.

    M.M. Vamsichadalavada, V. Vittal, G.C. Gebe, G.D. Irisarri, J. Tong, G. Pieper, An on-line contingencyfiltering scheme or dynamic security assessment. IEEE Trans. Power Syst. 12(1), 153–161 (1997)

    Google Scholar 

  67. 67.

    C. Fu, A. Bose, Contingency ranking based on severity indices in dynamic security analysis. IEEE Trans. Power Syst. 14(3), 980–986 (1999)

    Google Scholar 

  68. 68.

    M. Mallaki, H.R. Chevari, Error reduction in contingency ranking of power systems using combined indexes, in Asia-Pacific Power and Energy Engineering Conference, APPEEC, 2010

  69. 69.

    G. Li, S.M. Rovnyak, Integral square generator angle index for stability ranking and control. IEEE Trans. Power Syst. 20(2), 926–934 (2005)

    Google Scholar 

  70. 70.

    J. Kim et al., Study of the effectiveness of a Korean smart transmission grid based on synchro-phasor data of K-WAMS. IEEE Trans. Smart Grid 4(1), 411–418 (2013)

    Google Scholar 

  71. 71.

    I. Kamwa, S.R. Samantaray, G. Joos, Development of rule-based classifiers for rapid stability assessment of wide-area post disturbance records, in IEEE PES General Meeting, Minneapolis, MN, pp. 1–1, 2010

  72. 72.

    A. Tiwari, V. Ajjarapu, Event identification and contingency assessment for voltage stability via PMU, in Power Symposium, 2007. NAPS ‘07. 39th North American, Las Cruces, NM, pp. 413–420, 2007

  73. 73.

    G. Ravikumar, S.A. Khaparde, Taxonomy of PMU data based catastrophic indicators for power system stability assessment. IEEE Syst. J. 99, 1–13 (2016)

    Google Scholar 

  74. 74.

    J.M.G. Alvarez, P.E. Mercado, Online inference of the dynamic security level of power systems using fuzzy techniques. IEEE Trans. Power Syst. 22(2), 717–726 (2007)

    Google Scholar 

  75. 75.

    J.M. Gimenez Alvarez, P.E. Mercado, A new approach for power system online DSA using distributed processing and fuzzy logic. Electr. Power Syst. Res. 77(2), 106–118 (2007)

    Google Scholar 

  76. 76.

    V. Brandwajn, A.B.R. Kumar, A. Ipakchi, A. Böse, S.D. Kuo, Severity indices for contingency screening in dynamic security assessment. IEEE Trans. Power Syst. 12(3), 1136–1142 (1997)

    Google Scholar 

  77. 77.

    J.M.G. Alvarez, Critical contingencies ranking for dynamic security assessment using neural networks, in 2009 15th International Conference on Intelligent System Applications to Power Systems, 2009

  78. 78.

    M.J. Hossain, H.R. Pota, M.A. Mahmud, R.A. Ramos, Investigation of the impacts of large-scale wind power penetration on the angle and voltage stability of power systems. IEEE Syst. J. 6(1), 76–84 (2012)

    Google Scholar 

  79. 79.

    U. Kerin, E. Lerch, Dynamic security indication in power systems with large amount of renewables, in 2012 IEEE Power and Energy Society General Meeting, pp. 1–6, 2012

  80. 80.

    Y. Xu, Z.Y. Dong, Z. Xu, K. Meng, K.P. Wong, An intelligent dynamic security assessment framework for power systems with wind power. IEEE Trans. Ind. Inf. 8(4), 995–1003 (2012)

    Google Scholar 

  81. 81.

    D. Gautam, V. Vittal, T. Harbour, Impact of increased penetration of DFIG-based wind turbine generators on transient and small signal stability of power systems. IEEE Trans. Power Syst. 24(3), 1426–1434 (2009)

    Google Scholar 

  82. 82.

    N. Mithulananthan, R. Shah, K.Y. Lee, Small-disturbance angle stability control with high penetration of renewable generations. IEEE Trans. Power Syst. 29(3), 1463–1472 (2014)

    Google Scholar 

  83. 83.

    A.J. Santos, Optimal-power-flow solution by Newton’s method applied to an augmented Lagrangian function. IEE Proc. Gener. Transm. Distrib. 142(1), 33 (1995)

    MathSciNet  Google Scholar 

  84. 84.

    M.K. Mangoli, K.Y. Lee, Y. Moon Park, Optimal real and reactive power control using linear programming. Electr. Power Syst. Res. 26(1), 1–10 (1993)

    Google Scholar 

  85. 85.

    O. Alsac, J. Bright, M. Prais, B. Stott, Further developments in LP-based optimal power flow. IEEE Trans. Power Syst. 5(3), 697–711 (1990)

    Google Scholar 

  86. 86.

    A. Monticelli, M.V.F. Pereira, S. Granville, Security-constrained optimal power flow with post-contingency corrective rescheduling. IEEE Trans. Power Syst. 2(1), 175–180 (1987)

    Google Scholar 

  87. 87.

    O. Alsac, B. Stott, Optimal load flow with steady-state security. IEEE Trans. Power Appar. Syst. 93(3), 745–751 (1974)

    Google Scholar 

  88. 88.

    F.G.M. Lima, F.D. Galiana, I. Kockar, J. Munoz, Phase shifter placement in large-scale systems via mixed integer linear programming. IEEE Trans. Power Syst. 18(3), 1029–1034 (2003)

    Google Scholar 

  89. 89.

    M. Niu, C. Wan, Z. Xu, A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems. J. Mod. Power Syst. Clean Energy 2(4), 289–297 (2014)

    Google Scholar 

  90. 90.

    R.N. Banu, D. Devaraj, Multi-objective GA with fuzzy decision making for security enhancement in power system. Appl. Soft Comput. J. 12(9), 2756–2764 (2012)

    Google Scholar 

  91. 91.

    L.L. Lai, J.T. Ma, R. Yokoyama, M. Zhao, Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Int. J. Electr. Power Energy Syst. 19(5), 287–292 (1997)

    Google Scholar 

  92. 92.

    A.G. Bakirtzis, P.N. Biskas, C.E. Zoumas, V. Petridis, Optimal power flow by enhanced genetic algorithm. IEEE Trans. Power Syst. 17, 229–236 (2002)

    Google Scholar 

  93. 93.

    M.R. AlRashidi, M.E. El-Hawary, Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects. IEEE Trans. Power Syst. 22(4), 2030–2038 (2007)

    Google Scholar 

  94. 94.

    M.A. Abido, Optimal power flow using particle swarm optimization. Int. J. Electr. Power Energy Syst. 24(7), 563–571 (2002)

    Google Scholar 

  95. 95.

    H.R.E.-H. Bouchekara, M.A. Abido, Optimal power flow using differential search algorithm. Electr. Power Compon. Syst. 42, 1683–1699 (2014)

    Google Scholar 

  96. 96.

    N. Amjady, H. Sharifzadeh, Security constrained optimal power flow considering detailed generator model by a new robust differential evolution algorithm. Electr. Power Syst. Res. 81(2), 740–749 (2011)

    Google Scholar 

  97. 97.

    H.A. Hejazi, H.R. Mohabati, S.H. Hosseinian, M. Abedi, Differential evolution algorithm for security-constrained energy and reserve optimization considering credible contingencies. IEEE Trans. Power Syst. 26(3), 1145–1155 (2011)

    Google Scholar 

  98. 98.

    C. Thitithamrongchai, B. Eua-Arporn, Security-constrained optimal power flow: a parallel self-adaptive differential evolution approach. Electr. Power Compon. Syst. 36, 280–298 (2008)

    Google Scholar 

  99. 99.

    M. Varadarajan, K.S. Sworup, Solving multi-objective optimal power flow Using differential evolution. IET Gener. Transm. Distrib. 2(5), 720–730 (2008)

    Google Scholar 

  100. 100.

    P. Somasundaram, K. Kuppusamy, R.P. Kumudini Devi, Evolutionary programming based security constrained optimal power flow. Electr. Power Syst. Res. 72, 137–145 (2004)

    Google Scholar 

  101. 101.

    S. Duman, U. Güvenç, Y. Sönmez, N. Yörükeren, Optimal power flow using gravitational search algorithm. Energy Convers. Manag. 59, 86–95 (2012)

    Google Scholar 

  102. 102.

    B.V. Rao, G.V.N. Kumar, Electrical power and energy systems optimal power flow by BAT search algorithm for generation reallocation with unified power flow controller. Int. J. Electr. Power Energy Syst. 68, 81–88 (2015)

    Google Scholar 

  103. 103.

    Z. Ye, M. Wang, W. Liu, S. Chen, Fuzzy entropy based optimal thresholding using bat algorithm. Appl. Soft Comput. J. 31, 381–395 (2015)

    Google Scholar 

  104. 104.

    S. Biswal, A.K. Barisal, A. Behera, T. Prakash, Optimal power dispatch using BAT algorithm, in 2013 International Conference on Energy Efficient Technologies for Sustainability, pp. 1018–1023, 2013

  105. 105.

    J.A. Momoh, J.Z. Zhu, G.D. Boswell, S. Hoffman, Power system security enhancement by OPF with phase shifter. IEEE Trans. Power Syst. 16(2), 287–293 (2001)

    Google Scholar 

  106. 106.

    D. Devaraj, B. Yegnanarayana, Genetic-algorithm-based optimal power flow for security enhancement. IEEE Proc. Gener. Transm. Distrib. 152(6), 899 (2005)

    Google Scholar 

  107. 107.

    C. Venkaiah, D.M. Vinod Kumar, Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power re-scheduling of generators. Appl. Soft Comput. J. 11(8), 4921–4930 (2011)

    Google Scholar 

  108. 108.

    G. Baskar, M.R. Mohan, Contingency constrained economic load dispatch using improved particle swarm optimization for security enhancement. Electr. Power Syst. Res. 79(4), 615–621 (2009)

    Google Scholar 

  109. 109.

    S. Armaghani, N. Amjady, O. Abedinia, Security constrained multi-period optimal power flow by a new enhanced artificial bee colony. Appl. Soft Comput. J. 37, 382–395 (2015)

    Google Scholar 

  110. 110.

    K. Pandiarajan, C.K. Babulal, Overload alleviation in electric power system using fuzzy logic, 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET), no. January, pp. 417–423, 2011

  111. 111.

    K. Teeparthi, D.M. Vinod Kumar, Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm. Neural Comput. Appl. 29, 855–871 (2016)

    Google Scholar 

  112. 112.

    S. Gerbex, R. Cherkaoui, A. J. Germond, Optimal location of FACTS devices to enhance power system security, in 2003 IEEE Bologna PowerTech - Conference Proceedings, vol. 3, pp. 61–68, 2003

  113. 113.

    S.H. Song, J.U. Lim, S. Il Moon, Installation and operation of FACTS devices for enhancing steady-state security. Electr. Power Syst. Res. 70(1), 7–15 (2004)

    Google Scholar 

  114. 114.

    J.G. Singh, S.N. Singh, S.C. Srivastava, Enhancement of power system security through optimal placement of TCSC and UPFC, in 2007 IEEE Power Engineering Society General Meeting, PES, 2007

  115. 115.

    J.G. Singh, S.N. Singh, S.C. Srivastava, L. Soder, Power system security enhancement by optimal placement of UPFC, in Proceedings of the 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2010, pp. 228–235, 2010

  116. 116.

    N. Yorino, E.E. El-Araby, H. Sasaki, S. Harada, A new formulation for FACTS allocation for security enhancement against voltage collapse. IEEE Trans. Power Syst. 18(1), 3–10 (2003)

    Google Scholar 

  117. 117.

    Y. Lu, A. Abur, Static security enhancement via optimal utilization of thyristor-controlled series capacitors. IEEE Trans. Power Syst. 17(2), 324–329 (2002)

    Google Scholar 

  118. 118.

    A. Abur, Improving system static security via optimal placement of thyristor controlled series capacitors (TCSC), in 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194), vol. 2, pp. 516–521, 2001

  119. 119.

    G.I. Rashed, H.I. Shaheen, X.Z. Duan, S.J. Cheng, Evolutionary optimization techniques for optimal location and parameter setting of TCSC under single line contingency. Appl. Math. Comput. 205(1), 133–147 (2008)

    MATH  Google Scholar 

  120. 120.

    G.I. Rashed, Y. Sun, Optimal placement of Thyristor controlled series compensation for enhancing power system security based on computational intelligence techniques. Procedia Eng. 15, 908–914 (2011)

    Google Scholar 

  121. 121.

    S.M.R. Slochanal, M. Saravanan, A.C. Devi, Application of PSO technique to find optimal settings of TCSC for static security enhancement considering installation cost, in 2005 International Power Engineering Conference, pp. 1–394, 2005

  122. 122.

    K. Shanmukha Sundar, H.M. Ravikumar, Selection of TCSC location for secured optimal power flow under normal and network contingencies. Int. J. Electr. Power Energy Syst. 34(1), 29–37 (2012)

    Google Scholar 

  123. 123.

    S.K. Rautray, S. Choudhury, S. Mishra, P. K. Rout, A particle swarm optimization based approach for power system transient stability enhancement with TCSC, in Procedia Technology; 2nd International Conference on Communication, Computing & Security ICCCS-2012], vol. 6, pp. 31–38, 2012

  124. 124.

    M. Güçyetmez, E. Çam, A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems. Electr. Eng. 98, 145–157 (2015)

    Google Scholar 

  125. 125.

    S. Heier, Grid Integration of Wind Energy Conversion Systems (Wiley, London, 2014)

    Google Scholar 

  126. 126.

    S. Li, D.C. Wunsch, E.A. O’Hair, M.G. Giesselmann, Using neural networks to estimate wind turbine power generation. IEEE Trans. Energy Convers. 16(3), 276–282 (2001)

    Google Scholar 

  127. 127.

    I.G. Damousis, M.C. Alexiadis, J.B. Theocharis, P.S. Dokopoulos, A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans. Energy Convers. 19(2), 352–361 (2004)

    Google Scholar 

  128. 128.

    B.G. Brown, R.W. Katz, A.H. Murphy, Time series models to simulate and forecast wind speed and wind power. J. Clim. Appl. Meteorol. 23(8), 1184–1195 (1984)

    Google Scholar 

  129. 129.

    J. Hetzer, D.C. Yu, K. Bhattarai, An economic dispatch model incorporating wind power. IEEE Trans. Energy Convers. 23(2), 603–611 (2008)

    Google Scholar 

  130. 130.

    X. Liu, Economic load dispatch constrained by wind power availability: a wait-and-see approach. IEEE Trans. Smart Grid 1(3), 347–355 (2010)

    Google Scholar 

  131. 131.

    H. Chen, J. Chen, X. Duan, Multi-stage dynamic optimal power flow in wind power integrated system, in Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, vol. 2005, pp. 1–5, 2005

  132. 132.

    G.C.G. Chen, J.C.J. Chen, X.D.X. Duan, Power flow and dynamic optimal power flow including wind farms, in 2009 International Conference on Sustainable Power Generation and Supply, pp. 1–6, 2009

  133. 133.

    B.C. Pal, R.A. Jabr, Intermittent wind generation in optimal power flow dispatching. IET Gener. Transm. Distrib. 3(1), 66–74 (2009)

    Google Scholar 

  134. 134.

    L. Shi, C. Wang, L. Yao, Y. Ni, M. Bazargan, Optimal power flow solution incorporating wind power. IEEE Syst. J. 6(2), 233–241 (2012)

    Google Scholar 

  135. 135.

    A. Panda, M. Tripathy, Optimal power flow solution of wind integrated power system using modified bacteria foraging algorithm. Int. J. Electr. Power Energy Syst. 54, 306–314 (2014)

    Google Scholar 

  136. 136.

    S.P. Singh, J. Rokadia, C. Mishra, Optimal power flow in the presence of wind power using modified cuckoo search. IET Gener. Transm. Distrib. 9(7), 615–626 (2015)

    Google Scholar 

  137. 137.

    R. Zárate-Miñano, F. Milano, A.J. Conejo, An OPF methodology to ensure small-signal stability. IEEE Trans. Power Syst. 26, 1050–1061 (2011)

    Google Scholar 

  138. 138.

    Peijie Li, Hua Wei, Bin Li, Yude Yang, Eigenvalue-optimisation-based optimal power flow with small-signal stability constraints. IET Gener. Transm. Distrib. 7, 440–450 (2013)

    Google Scholar 

  139. 139.

    K. Tangpatiphan, A. Yokoyama, Adaptive evolutionary programming with neural network for transient stability constrained optimal power flow, in 15th International Conference on Intelligent System Applications to Power Systems, pp. 1–6, 2009

  140. 140.

    H. Ahmadi, H. Ghasemi, M. HaddadiA, H. Lesani, Two approaches to transient stability-constrained optimal power flow. Int. J. Electr. Power Energy Syst. 47, 181–192 (2013)

    Google Scholar 

  141. 141.

    R. Zarate-Minano, T. Van Cutsem, F. Milano, J. ConejoA, Securing transient stability using time-domain simulations within an optimal power flow. IEEE Trans. Power Syst. 25, 243–253 (2010)

    Google Scholar 

  142. 142.

    D. Gan, R.J. Thomas, R.D. Zimmerman, Stability-constrained optimal power flow. IEEE Trans. Power Syst. 15(2), 535–540 (2000)

    Google Scholar 

  143. 143.

    Y. Yuan, J. Kubokawa, H. Sasaki, A solution of optimal power flow with multicontingency transient stability constraints. IEEE Trans. Power Syst. 18(3), 1094–1102 (2003)

    Google Scholar 

  144. 144.

    K.Y. Chan, S.H. Ling, K.W. Chan, H.H.C. Iu, G. T.Y. Pong, Solving multi-contingency transient stability constrained optimal power flow problems with an improved GA, in 2007 IEEE Congress on Evolutionary Computation, pp. 2901–2908, 2007

  145. 145.

    A.G. Bakirtzis, P.N. Biskas, C.E. Zoumas, V. Petridis, Optimal power flow by enhanced genetic algorithm. IEEE Trans. Power Syst. 17(2), 229–236 (2002)

    Google Scholar 

  146. 146.

    N. Mo, Z.Y. Zou, K.W. Chan, T.Y.G. Pong, Transient stability constrained optimal power flow using particle swarm optimisation. IET Gener. Transm. Distrib. 1(3), 476 (2007)

    Google Scholar 

  147. 147.

    M. Anitha, S. Subramanian, R. Gnanadass, Optimal power flow constrained by transient stability based on improved particle swarm optimisation. Int. J. Intell. Syst. Technol. Appl. 5(1/2), 68 (2008)

    Google Scholar 

  148. 148.

    H.R. Cai, C.Y. Chung, K.P. Wong, Application of differential evolution algorithm for transient stability constrained optimal power flow. IEEE Trans. Power Syst. 23(2), 719–728 (2008)

    Google Scholar 

  149. 149.

    D. Wu, D. Gan, J.N. Jiang, An improved micro-particle swarm optimization algorithm and its application in transient stability constrained optimal power flow. Int. Trans. Electr. Energy Syst. 24(3), 395–411 (2014)

    Google Scholar 

  150. 150.

    S.W. Xia, B. Zhou, K.W. Chan, Z.Z. Guo, An improved GSO method for discontinuous non-convex transient stability constrained optimal power flow with complex system model. Int. J. Electr. Power Energy Syst. 64, 483–492 (2015)

    Google Scholar 

  151. 151.

    K. Ayan, U. Kılıç, B. Baraklı, Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow. Int. J. Electr. Power Energy Syst. 64, 136–147 (2015)

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Kiran Teeparthi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Teeparthi, K., Vinod Kumar, D.M. Power System Security Assessment and Enhancement: A Bibliographical Survey. J. Inst. Eng. India Ser. B 101, 163–176 (2020). https://doi.org/10.1007/s40031-020-00440-1

Download citation


  • Power system security
  • Contingency analysis
  • Security enhancement
  • Static security assessment
  • Dynamic security assessment