Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

[1]  K. Parthasarathy,et al.  Optimal reactive power dispatch algorithm for voltage stability improvement , 1996 .

[2]  T. Ohtaka,et al.  Voltage stability preventive control using a new voltage stability index , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[3]  D. Thukaram,et al.  Artificial neural network application to power system voltage stability improvement , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[4]  Y. Tamura,et al.  Relationship Between Voltage Instability and Multiple Load FLow Solutions in Electric Power Systems , 1983, IEEE Transactions on Power Apparatus and Systems.

[5]  D. Hill,et al.  Voltage stability indices for stressed power systems , 1993 .

[6]  A.C.Z. de Souza,et al.  Comparison of performance indices for detection of proximity to voltage collapse , 1996 .

[7]  A.C.Z. de Souza,et al.  New techniques to speed up voltage collapse computations using tangent vectors , 1997 .

[8]  A. A. El-Keib,et al.  Application of artificial neural networks in voltage stability assessment , 1995 .

[9]  Ronald R. Mohler,et al.  Neural-network-based load modeling and its use in voltage stability analysis , 2003, IEEE Trans. Control. Syst. Technol..

[10]  B. Jeyasurya,et al.  On-line voltage stability monitoring using artificial neural network , 2004, 2004 Large Engineering Systems Conference on Power Engineering (IEEE Cat. No.04EX819).

[11]  M. La Scala,et al.  A neural network-based method for voltage security monitoring , 1996 .

[12]  Manjaree Pandit,et al.  Fast voltage contingency selection using fuzzy parallel self-organizing hierarchical neural network , 2003 .

[13]  Xiaoxin Zhou,et al.  Accuracy Analysis of Static Voltage Stability Indices Based On Power Flow Model , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[14]  D. Thukaram,et al.  Voltage Stability Improvement: Case Studies of Indian Power Networks. , 1998 .

[15]  Venkataramana Ajjarapu,et al.  The continuation power flow: a tool for steady state voltage stability analysis , 1991 .

[16]  B. Jeyasurya.  Artificial neural networks for on-line voltage stability assessment , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[17]  S. C. Srivastava,et al.  ANN based voltage stability margin prediction , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).