A Self Adaptive Genetic Based Algorithm for the Identification and Elimination of Bad Data
The identification and elimination of bad
measurements is one of the basic functions of a robust state estimator
as bad data have the effect of corrupting the results of state
estimation according to the popular weighted least squares method.
However this is a difficult problem to handle especially when dealing
with multiple errors from the interactive conforming type. In this
paper, a self adaptive genetic based algorithm is proposed. The
algorithm utilizes the results of the classical linearized normal
residuals approach to tune the genetic operators thus instead of
making a randomized search throughout the whole search space it is
more likely to be a directed search thus the optimum solution is
obtained at very early stages(maximum of 5 generations). The
algorithm utilizes the accumulating databases of already computed
cases to reduce the computational burden to minimum. Tests are
conducted with reference to the standard IEEE test systems. Test
results are very promising.
[1] A. Monticelli, State estimation in electric power systems. A generalized
Approach, Boston: Kluver Academic Publishers, 1999, ch.9.
[2] A. A. Abur, "A bad data identification method for linear programming
state estimation", IEEE Trans. Power Systems, vol. PWRS-5, No.
3,pp.894-901, August 1990
[3] W. W. Kotiuga and M. Vidyassagar, "Bad data rejection properties of
weighted least absolute value techniques applied to static state estimateion",
IEEE Trans. Power Apparatus and Systems, vol. PAS-101,
No.2,pp. 511-523, May 1991.
[4] A. Monticelli, F. WU and M.Yen,"Multiple bad data identification for
state estimation by combinatorial optimization", IEEE Trans. Power
Delivery, vol. PWRD-1, No.3, pp. 361-369, July 1986.
[5] N. H. Abbasy and W. El-Hassawy,"Power system state estimation: ANN
application to bad data detection and identification", Proc. 4th IEEE
AFRICON Conference, September 1996, vol.2, pp.611-615.
[6] S. Gastoni, G. P. Granelli and M. Montagna, "Multiple bad data
processing by genetic algorithms", Proc. IEEE Bologna PowerTech
Conference, June 2003.
[7] G. R. Krumpholz, K.A. Clements and P.W. Davis, "Power Systems
Observability: A Practical Algorithm Using Network Topology", IEEE
Transactions on Power Apparatus and Systems, vol. PAS-99, No.4,
August 1980, pp. 1534-1542.
[1] A. Monticelli, State estimation in electric power systems. A generalized
Approach, Boston: Kluver Academic Publishers, 1999, ch.9.
[2] A. A. Abur, "A bad data identification method for linear programming
state estimation", IEEE Trans. Power Systems, vol. PWRS-5, No.
3,pp.894-901, August 1990
[3] W. W. Kotiuga and M. Vidyassagar, "Bad data rejection properties of
weighted least absolute value techniques applied to static state estimateion",
IEEE Trans. Power Apparatus and Systems, vol. PAS-101,
No.2,pp. 511-523, May 1991.
[4] A. Monticelli, F. WU and M.Yen,"Multiple bad data identification for
state estimation by combinatorial optimization", IEEE Trans. Power
Delivery, vol. PWRD-1, No.3, pp. 361-369, July 1986.
[5] N. H. Abbasy and W. El-Hassawy,"Power system state estimation: ANN
application to bad data detection and identification", Proc. 4th IEEE
AFRICON Conference, September 1996, vol.2, pp.611-615.
[6] S. Gastoni, G. P. Granelli and M. Montagna, "Multiple bad data
processing by genetic algorithms", Proc. IEEE Bologna PowerTech
Conference, June 2003.
[7] G. R. Krumpholz, K.A. Clements and P.W. Davis, "Power Systems
Observability: A Practical Algorithm Using Network Topology", IEEE
Transactions on Power Apparatus and Systems, vol. PAS-99, No.4,
August 1980, pp. 1534-1542.
@article{"International Journal of Electrical, Electronic and Communication Sciences:55570", author = "A. A. Hossam-Eldin and E. N. Abdallah and M. S. El-Nozahy", title = "A Self Adaptive Genetic Based Algorithm for the Identification and Elimination of Bad Data", abstract = "The identification and elimination of bad
measurements is one of the basic functions of a robust state estimator
as bad data have the effect of corrupting the results of state
estimation according to the popular weighted least squares method.
However this is a difficult problem to handle especially when dealing
with multiple errors from the interactive conforming type. In this
paper, a self adaptive genetic based algorithm is proposed. The
algorithm utilizes the results of the classical linearized normal
residuals approach to tune the genetic operators thus instead of
making a randomized search throughout the whole search space it is
more likely to be a directed search thus the optimum solution is
obtained at very early stages(maximum of 5 generations). The
algorithm utilizes the accumulating databases of already computed
cases to reduce the computational burden to minimum. Tests are
conducted with reference to the standard IEEE test systems. Test
results are very promising.", keywords = "Bad Data, Genetic Algorithms, Linearized Normal
residuals, Observability, Power System State Estimation.", volume = "2", number = "9", pages = "1911-6", }