A genetic based algorithm for measurement of power system disturbances

This paper introduces genetic algorithms (GA) as a powerful tool for monitoring and supervising power system disturbances generated due to dynamic performance of power systems. Monitoring power system disturbances involves monitoring fundamental voltage magnitude and its frequency as well as harmonic and sub-harmonic voltage magnitudes and their frequencies under different operating conditions for power quality evaluation purposes. The proposed method is based on genetic algorithms optimization technique. The method uses digital set of measurements for the voltage or current waveforms at power system bus to perform the estimation process digitally. The algorithm is tested using different simulated data to monitor power quality. Three different study cases are considered in this work. In the first part, the estimation of voltage flicker levels and its frequency is presented and discussed. In the second part, the frequency of a bus voltage signal that is contaminated with harmonics is estimated. The harmonic contents are also estimated in this case. In the third part, the analysis of a damped sub-harmonic signal is presented. Effects of number of samples, sampling frequency and the sample window size are studied. Effects of GA parameters and operators, such as population size, crossover, mutation probabilities and niching are also studied. Results are reported and discussed.

[1]  M. T. Chen,et al.  Digital algorithms for measurement of voltage flicker , 1997 .

[2]  Shyh-Jier Huang,et al.  Application of Morlet wavelets to supervise power system disturbances , 1999 .

[3]  M. Sachdev,et al.  A Least Error Squares Technique For Determining Power System Frequency , 1985, IEEE Transactions on Power Apparatus and Systems.

[4]  G. S. Christensen,et al.  Least Absolute Value Based on Linear Programing Algorithm for Measurement of Power System Frequency from a Distorted Bus Voltage Signal , 1992 .

[5]  W.K.A. Goncalves,et al.  Expert system for the analysis of power quality , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[6]  M. E. El-Hawary,et al.  Measurement of Voltage Flicker Magnitude and Frequency in a Power System For Power Quality Analysis , 1999 .

[7]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[8]  Witold Pedrycz,et al.  Application of genetic algorithms for control design in power systems , 1998 .

[9]  Chao-Shun Chen,et al.  Stochastic voltage flicker analysis and its mitigation for steel industrial power systems , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[10]  John Douglas.  Solving problems of power quality , 1993 .

[11]  H. Wayne Beaty,et al.  Electrical Power Systems Quality , 1995 .

[12]  Kenneth de Jong,et al.  Adaptive System Design: A Genetic Approach , 1980, IEEE Trans. Syst. Man Cybern..

[13]  A. Y. Chikhani,et al.  Power quality detection and classification using wavelet-multiresolution signal decomposition , 1999 .

[14]  S. K. Basu,et al.  Solving Capacitor Placement Problems in Distribution Systems Using Genetic Algorithms , 1999 .

[15]  Ganapati Panda,et al.  Frequency estimation of distorted power system signals using extended complex Kalman filter , 1999 .

[16]  Khashayar Khorasani,et al.  Fault identification in an AC-DC transmission system using neural networks , 1991 .

[17]  K. P. Poon,et al.  Analysis of power system dynamic oscillations with heat phenomenon by Fourier transformation , 1990 .

[18]  Alexander McEachern.  Handbook of Power Signatures , 1988 .