An application of sequential neural-network approximation for sitting and sizing passive harmonic filters

This paper presents a method which is combined by sequential neural-network approximation and orthogonal arrays (SNAOA) for reducing harmonic distortion with passive harmonic filters and determining the optimal locations for harmonic filters among existent capacitor busses in the power network. An orthogonal array is first conducted to obtain the initial solution set. The set is then treated as the initial training sample. Next, a back-propagation sequential neural network is trained to simulate the feasible domain for seeking the optimal filter design. A restart strategy is also incorporated into the SNAOA so that the searching process may have a better opportunity to reach a near global optimum solution. In order to determine a set of weights of objective function to represent the relative importance of each term, the simplest and most efficient form of triangular membership functions has been considered. To illustrate the performance of the SNAOA, a practical harmonic mitigation problem in a 36-bus radial distribution system is studied. The results show that the SNAOA performs better than the original scheme and satisfies the harmonic limitations with respect to the objective of minimizing total harmonic distortion of voltages and the cost of commercially available discrete sizes for sitting and sizing passive harmonic filters.

[1]  Jeffrey S. Racine,et al.  Semiparametric ARX neural-network models with an application to forecasting inflation , 2001, IEEE Trans. Neural Networks.

[2]  Yu-Lung Lo,et al.  Integrated Taguchi method and neural network analysis of physical profiling in the wirebonding process , 2002 .

[3]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[4]  C. R. Rao,et al.  Factorial Experiments Derivable from Combinatorial Arrangements of Arrays , 1947 .

[5]  Tsai-Fu Wu,et al.  Photovoltaic inverter systems with self-tuning fuzzy control based on an experimental planning method , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[6]  R. Yager.  On a general class of fuzzy connectives , 1980 .

[7]  George van Schoor,et al.  Training and optimization of an artificial neural network controlling a hybrid power filter , 2003, IEEE Trans. Ind. Electron..

[8]  Yeh-Liang Hsu,et al.  A sequential approximation method using neural networks for engineering design optimization problems , 2003 .

[9]  C. S. Moo,et al.  Integrated design of EMI filter and PFC low-pass filter for power electronic converters , 2003 .

[10]  Daniel C. St. Clair,et al.  Using Taguchi's method of experimental design to control errors in layered perceptrons , 1995, IEEE Trans. Neural Networks.

[11]  Ward Jewell,et al.  Effects of harmonics on equipment , 1993 .

[12]  Hung-Lu Wang,et al.  Sensitivity-based approach for passive harmonic filter planning in a power system , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[13]  Shih-Shong Yen,et al.  Mitigation of harmonic disturbance at pumped storage power station with static frequency converter , 1997 .

[14]  Yaow-Ming Chen,et al.  Active power line conditioner with a neural network control , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[15]  J. J. Hopfield,et al.  “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[16]  Hsiung Cheng Lin,et al.  A NEW NEURAL NETWORK BASED ALGORITHM FOR REAL TIME HARMONICS FILTERING , 1999 .

[17]  Tien-Ting Chang,et al.  An efficient approach for reducing harmonic voltage distortion in distribution systems with active power line conditioners , 2000 .