A SIPSS-Lasso-BPNN scheme for online voltage stability assessment

Load active power at the voltage collapse point (PLL) is a useful index for online voltage stability assessment. This paper proposes a SIPSS-Lasso-BPNN scheme to offline fitting and online forecasting the load active power at the voltage collapse point PLL. The scheme consists of a SIPSS (Similarity Index of Power System State) based screening method, a Lasso (Least absolute shrinkage and select operator) method and a back propagation neural network (BPNN). The SIPSS based screening method screens the training samples according to their similarity indexes of power system states. The Lasso method selects the principal input features which are most explanatory to PLL via the shrunken regression analysis. The training samples are reduced by the above two methods. The BPNN is used to offline fit and online forecast the PLL through the reduced training samples. The test results on the New England 39 bus system shows that the SIPSS-Lasso-BPNN scheme can significantly improve the efficiency of BPNN offline training and guarantee the forecasting accuracy.

[1]  Athula D. Rajapakse,et al.  Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network , 2010 .

[2]  Benjamin Jeyasurya,et al.  Multicontingency voltage stability monitoring of a power system using an adaptive radial basis function network , 2008 .

[3]  Jaydev Sharma,et al.  Comparison of feature selection techniques for ANN-based voltage estimation , 2000 .

[4]  B. Jayasekara,et al.  Derivation of an Accurate Polynomial Representation of the Transient Stability Boundary , 2006, IEEE Transactions on Power Systems.

[5]  Thierry Van Cutsem,et al.  Voltage Stability of Electric Power Systems , 1998 .

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

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

[8]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[9]  Hsiao-Dong Chiang,et al.  CPFLOW: a practical tool for tracing power system steady-state stationary behavior due to load and generation variations , 1995 .

[10]  Gevork B. Gharehpetian,et al.  Application of core vector machines for on-line voltage security assessment using a decisiontree-based feature selection algorithm , 2009 .

[11]  Mahmoud Moghavvemi,et al.  Current state of neural networks applications in power system monitoring and control , 2013 .

[12]  D. Devaraj,et al.  On-line voltage stability assessment using radial basis function network model with reduced input features , 2011 .

[13]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[14]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

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

[16]  Saikat Chakrabarti.  Voltage stability monitoring by artificial neural network using a regression-based feature selection method , 2008, Expert Syst. Appl..

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

[18]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[19]  P. Kundur,et al.  Power system stability and control , 1994 .