Sensitivity-based hybrid approach for voltage instability alleviation using ann

Today an average transmission line is loaded more heavily than ever before, and this has given rise to a serious problem of voltage instability. The authors present a novel method for power system voltage instability estimation and improvement using ANN. The method is based on the fact that reactive power injections at critical buses of the power system help to steer the system away from a developing voltage collapse. The location and quantum of reactive power support has been computed based on sensitivity. The sensitivity matrix is formulated by cascading the output/input sensitivity of multiplayer perceptron model with that of input features versus reactive power injections. The effectiveness of the proposed method is demonstrated on Ward-Hale 6-bus, IEEE 14-bus, and IEEE 30-bus test systems. The method can be effectively used to make the system secure against voltage collapse condition in system planning and online operation.

[1]  Robert J. Thomas,et al.  A posturing strategy against voltage instabilities in electric power systems , 1988 .

[2]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[3]  T. Carlsen,et al.  Voltage stability condition in a power transmission system calculated by sensitivity methods , 1990 .

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

[5]  K. Iba,et al.  Calculation of critical loading condition with nose curve using homotopy continuation method , 1991 .

[6]  Thomas J. Overbye,et al.  Voltage security enhancement using energy based sensitivities , 1991 .

[7]  A. Phadke,et al.  Control of voltage stability using sensitivity analysis , 1992 .

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

[9]  Yuan-Yih Hsu,et al.  Fast voltage estimation using an artificial neural network , 1993 .

[10]  Luis Vargas,et al.  Time dependence of controls to avoid voltage collapse , 2000 .

[11]  Fernando L. Alvarado,et al.  SVC placement using critical modes of voltage instability , 1993 .

[12]  A. Semlyen,et al.  Calculation of the extreme loading condition of a power system for the assessment of voltage stability , 1991, IEEE Power Engineering Review.

[13]  W. R. Lachs.  Dynamic Study of an Extreme System Reactive Power Deficit , 1985, IEEE Power Engineering Review.

[14]  Y.-Y. Hsu,et al.  Short term load forecasting using a multilayer neural network with an adaptive learning algorithm , 1992 .

[15]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.