Abstract
This paper proposes a novel parallel RBFNN (Radial Basis Function Neural Network) classifier based on S-transform for recognition and classification of PQ (Power Quality) disturbances. S-transform is used to extract feature vectors, while the constructed parallel RBFNN classifier is used to recognize and classify PQ disturbances according to the extracted feature vectors. The parallel RBFNN classifier consists of eight sub-networks, each of which is only able to recognize one type of disturbance. In order to improve the convergence performance of RBFNN and optimize the number of hidden layer nodes, a dynamic clustering algorithm which clusters all training samples to determine the number of hidden layer nodes is proposed. Simulation and test results demonstrate that the method proposed to recognize and classify PQ disturbances is correct and feasible, and that the RBFNN classifier based on the dynamic clustering algorithm has a faster convergence speed and a higher correct identification rate.
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References
Loredana, C., Alessandro, F., Simona, S.: A Distributed System for Electric Power Quality Measurement. IEEE Trans. on Instrumentation and Measurement 51(4), 776–781 (2002)
Kezunovic, M., Liao, Y.: A Novel Software Implementation Concept for Power Quality Study. IEEE Trans. on Power Delivery 17(2), 998–1001 (2002)
Youssef, A.M., Abde-Gali, T.K., El-Saadany, E.F., et al.: Disturbances Classification Utilizing Dynamic Time Warping Classifier. IEEE Trans. on Power Delivery 19(1), 272–278 (2004)
Ghosh, A.K., Lubkeman, D.L.: The Classification of Power System Disturbance Waveforms Using A Neural Network Approach. IEEE Trans. on Power Delivery 10(1), 109–115 (1995)
Dash, P.K., Mishra, S., Salama, M., et al.: Classification of Power System Disturbances Using a Fuzzy Expert System and a Fourier Linear Combiner. IEEE Trans. on Power Delivery 15(2), 472–477 (2000)
Chilukuri, M.V., Dash, P.K.: Multiresolution S-transform-based Fuzzy Recognition System for Power Quality Events. IEEE Trans. on Power Delivery 19(1), 323–330 (2004)
Chung, J., Powers, E.J., Grady, W.M., et al.: Power Disturbance Classifier Utilizing a Ruled-based Method and Wavelet Packet-based Hidden Markov Model. IEEE Trans. on Power Delivery 17(1), 738–743 (2002)
Perunicic, B., Malini, M., Wang, Z., Liu, Y.: Power Quality Disturbance Detection and Classification Using Wavelets and Artificial Neural Networks. In: Proceedings of the 8th ICHQP, vol. 24, pp. 77–82 (1998)
Santoso, S., Grady, M.W., Powers, J.E.: Power Quality Disturbance Waveform Recognition Using Wavelet-based Neural Classifier-Part 2: Application. IEEE Trans. on Power Delivery 15(1), 229–235 (2000)
Borras, D., Castilla, M., Moreno, N.: Wavelet and Neural Structure: A New Tool for Diagnostic of Power System Disturbances. IEEE Trans. on Industry Application 37(1), 184–190 (2001)
Stockwell, R.G., Mansinha, L., Lowe, R.P.: Localization of the Complex Spectrum: The S Transform. IEEE Trans. on Signal Process. 44, 998–1001 (1996)
Lee, I.W.C., Dash, P.K.: S-transform-based Intelligent System for Classification of Power Quality Disturbance Signals. IEEE Trans. on Power Delivery 18(3), 800–805 (2003)
Abe, Y., Figuni, Y.: Fast Computation of RBF Coefficients for Regularly Sampled Inputs. IEE Electronics Letters 39(6), 543–544 (2003)
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Tong, W., Song, X. (2007). A Parallel RBFNN Classifier Based on S-Transform for Recognition of Power Quality Disturbances. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_89
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DOI: https://doi.org/10.1007/978-3-540-72393-6_89
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