Power system probabilistic security assessment using Bayes classifier

This paper presents a method for power system security assessment based on the Bayes classifier. This method can be applied to calculate probabilistic security indices as well as on-line security assessment. In general, the determination of security breach is a cumbersome and time-consuming process due to dynamic and steady-state effects. These effects can be incorporated by considering transient stability, satisfaction of system load without violation of constraints, and voltage stability studies. The variation of the system load, as well as contingencies, may cause system transition to a different operating state. It is impractical if not impossible to evaluate all these situations, such as contingencies resulting from load variation. The straightforward Monte Carlo simulation, one of the possible methods in power system reliability analysis, requires the evaluation of a system operating state for each sampled state and is computation-intensive. In the proposed method, first the joint probability density of feature vectors has been obtained by using some training data. Once this joint distribution is obtained, the Bayes classifier provides the assessment of system security without complicated contingency analyses and can reduce the computational burden. Security status of a given feature vector can be determined by a posteriori probability rule called Bayes rule, which can be implemented in on-line security assessment or power system reliability studies.

[1]  J.D. McCalley,et al.  An overview of risk based security assessment , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[2]  Anil K. Jain,et al.  Artificial neural networks and statistical pattern recognition : old and new connections , 1991 .

[3]  A. O. Ekwue,et al.  Application of Kohonen self-organising neural network to static security assessment , 1995 .

[4]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[5]  James D. McCalley,et al.  A Bayesian approach for short-term transmission line thermal overload risk assessment , 2002 .

[6]  David C. Yu,et al.  Bayesian network model for reliability assessment of power systems , 1999 .

[7]  P. Luh,et al.  Forecasting power market clearing price and its discrete PDF using a Bayesian-based classification method , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[8]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[9]  H. Glavitsch,et al.  Estimating the Voltage Stability of a Power System , 1986, IEEE Transactions on Power Delivery.

[10]  D. Niebur,et al.  Power system static security assessment using the Kohonen neural network classifier , 1991 .

[11]  K. A. Clements,et al.  Identification of parallel flows in power networks operating under deregulated environments , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[12]  Roy Billinton,et al.  Probabilistic transient stability studies using the method of bisection [power systems] , 1996 .

[13]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[14]  Peter W. Sauer,et al.  Power System Dynamics and Stability , 1997 .

[15]  K. Carlsen,et al.  Operating under stress and strain [electrical power systems control under emergency conditions] , 1978, IEEE Spectrum.

[16]  Chanan Singh,et al.  Steady state and dynamic security assessment in composite power systems , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..