Analysis of Critical Conditions in Electric Power Systems by Feed Forward and Layer Recurrent Neural Networks

In this paper, critical conditions in electric power systems are monitored by applying various neural networks. In order to accomplish the stated goal, the authors tried several combinations of Feed Forward Neural Network and Layer Recurrent Neural Networks by imparting appropriate training schemes through supervised learning in order to formulate a comparative analysis on their performance. Once, training goes successful, the neural network learns how to deal with a set of newly presented data through validation and testing mechanism so as to evolve the best network structure and learning criteria. The proposed methodology has been tested on the standard IEEE 30-bus test system with the support of MATLAB based neural network toolbox. The results presented in this paper signify that the multi-layered feed forward neural network with Levenberg-Marquardt back propagation algorithm gives best training performance of all possible cases considered in this paper, thus validating the proposed methodology.

[1]  Seema Singh,et al.  Congestion management in competitive power market : A bibliographical survey , 2005 .

[2]  Devendra K. Chaturvedi,et al.  Possible applications of neural nets to power system operation and control , 1992 .

[3]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[4]  Mohammed E. El-Hawary,et al.  Electric Power Applications of Fuzzy Systems , 1998 .

[5]  Xue Li,et al.  Comparison of Levenberg-Marquardt Method and Path Following Interior Point Method for the Solution of Optimal Power Flow Problem , 2012 .

[6]  Benjamin Engel,et al.  Electric Power Applications Of Fuzzy Systems , 2016 .

[7]  Venkataramana Ajjarapu,et al.  Bibliography on voltage stability , 1998 .

[8]  Maryam Mirzaei,et al.  Comparative Analysis of Probabilistic Neural Network, Radial Basis Function, and Feed-forward Neural Network for Fault Classification in Power Distribution Systems , 2011 .

[9]  Ilamathi Balamurugan,et al.  ANN - SQP Approach For NOx Emission Reduction In Coal Fired Boilers , 2012 .

[10]  Athula D. Rajapakse,et al.  Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network , 2010, IEEE Transactions on Power Systems.

[11]  Robert J. Marks,et al.  Neural Networks and Their Application to Power Engineering , 1991 .

[12]  A.G. Phadke,et al.  Dynamic simulation of voltage collapse , 1989, Conference Papers Power Industry Computer Application Conference.

[13]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

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

[15]  Alireza Bahmanyar,et al.  Power system voltage stability monitoring using artificial neural networks with a reduced set of inputs , 2014 .

[16]  Damitha K. Ranaweera.  Comparison of neural network models for fault diagnosis of power systems , 1994 .

[17]  Tao Xu,et al.  Electrical Power and Energy Systems , 2015 .

[18]  T.V. Cutsem,et al.  A method to compute reactive power margins with respect to v , 1991, IEEE Power Engineering Review.

[19]  M. Kitagawa,et al.  An investigation of voltage instability problems , 1992 .

[20]  M. El-sherbiny.  Proposed Terms and Definitions for Power System Stability Task Force on Terms & Definitions System Dynamic Performance Subcommittee Power System Engineering Committee , 1982, IEEE Power Engineering Review.

[21]  Charles Concordia.  ‘Voltage stability’ simplified , 1992 .

[22]  Tharam S. Dillon.  Artificial neural network applications to power systems and their relationship to symbolic methods , 1991 .

[23]  J. Deuse,et al.  Dynamic simulation of voltage collapses , 1993 .

[24]  W. W. Price,et al.  Planning and operations benefits of high fidelity voltage collapse simulations , 1993 .

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

[26]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[27]  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.