[논문]Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network

Chang, Wen-Yeau

doi:10.5370/jeet.2014.9.1.293

[국내논문] Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network 원문보기

Journal of electrical engineering & technology, v.9 no.1, 2014년, pp.293 - 300

Chang, Wen-Yeau (Dept. of Electrical Engineering, St. John's University)

Abstract ▼ AI-Helper

This paper proposes a novel pattern recognition approach based on the radial basis function (RBF) neural network for identifying insulation defects of high-voltage electrical apparatus arising from partial discharge (PD). Pattern recognition of PD is used for identifying defects causing the PD, such as internal discharge, external discharge, corona, etc. This information is vital for estimating the harmfulness of the discharge in the insulation. Since an insulation defect, such as one resulting from PD, would have a corresponding particular pattern, pattern recognition of PD is significant means to discriminate insulation conditions of high-voltage electrical apparatus. To verify the proposed approach, experiments were conducted to demonstrate the field-test PD pattern recognition of cast resin current transformer (CRCT) models. These tests used artificial defects created in order to produce the common PD activities of CRCTs by using feature vectors of field-test PD patterns. The significant features are extracted by using nonlinear principal component analysis (NLPCA) method. The experimental data are found to be in close agreement with the recognized data. The test results show that the proposed approach is efficient and reliable.

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문제 정의

To verify the proposed approach, a practical experiment is conducted to demonstrate the effectiveness of the PD pattern recognition scheme. The experimental tests were carried out on model CRCTs.
This paper has proposed an RBF neural network based pattern recognition technique for PD of high-voltage equipment. The effectiveness of the proposed technique has been verified using experimental results.

제안 방법

The significant features of statistical operators are extended extracted by using the NLPCA scheme. After feature extraction, this paper proposes the application of RBF neural network to recognize partial discharge patterns of CRCT.
In order to investigate the PD features and to verify the classification capabilities of the proposed RBF neural network based pattern recognition approach for different PD types commonly occurring in high-voltage electrical apparatus, a PD dataset is needed. The PD dataset for this study was collected from laboratory PD tests on a series of model CRCTs.
The statistical feature extraction methods were used to extract 24 statistical features for patterns. But since some of the statistical features are futile for pattern recognition, feature extraction is necessary in the PD pattern recognition to reduce dimension of original data and make effective discrimination of the statistical feature patterns for different PD status.
The experimental tests were carried out on model CRCTs. The test results show that the proposed approach is able to accurately recognize the testing defects.
Five types of experimental models with artificial defects are purposely embedded to produce five common PD events in CRCT. The proposed approach has been implemented according to the field-test PD patterns collected from our laboratory. The input data for this PD recognition system are the peak pulse magnitude distribution H_qmax(φ), the average pulse magnitude distribution H_qn(φ), and the number of pulse distributions H_n(φ).
After setting up the training sets, the training procedure of RBF neural network was started. The training data consist of 150 feature vectors, which are randomly chosen from the 250 feature vectors of sample data.
To verify the training results of RBF neural network, the training data were applied to the trained RBF neural network again. Table 1 shows the test results of the training data.

대상 데이터

In order to investigate the PD features and to verify the classification capabilities of the proposed RBF neural network based pattern recognition approach for different PD types commonly occurring in high-voltage electrical apparatus, a PD dataset is needed. The PD dataset for this study was collected from laboratory PD tests on a series of model CRCTs. The materials and process used to manufacture these high-voltage CRCTs were exactly the same as that of making the field equipment.
To verify the proposed approach, a practical experiment is conducted to demonstrate the effectiveness of the PD pattern recognition scheme. The experimental tests were carried out on model CRCTs. The test results show that the proposed approach is able to accurately recognize the testing defects.
The significant features are extracted from statistical features by using the NLPCA scheme. There are 10 extracted features for feature vector extracted from 24 statistical features using the NLPCA scheme in this experiment. After the feature extraction process, all the features in the feature vectors were normalized to set up the training sets.
After setting up the training sets, the training procedure of RBF neural network was started. The training data consist of 150 feature vectors, which are randomly chosen from the 250 feature vectors of sample data. The remaining 100 feature vectors were used as the testing data.
The training data consist of 150 feature vectors, which are randomly chosen from the 250 feature vectors of sample data. The remaining 100 feature vectors were used as the testing data.

이론/모형

But since some of the statistical features are futile for pattern recognition, feature extraction is necessary in the PD pattern recognition to reduce dimension of original data and make effective discrimination of the statistical feature patterns for different PD status. In this paper, the significant features are extracted from statistical features by using the NLPCA scheme [11]. The NLPCA is based on the structure of dual multiplayer neural networks model (DMNN), which contains five layers of neurons, as shown in Fig.
The procedure of estimating the weights between the hidden and output layer is described briefly as follows. After the Gaussian function centres and widths are computed from training vectors, the connection weights between the hidden and output layers can be calculated using the pseudo-inverse matrix method.
Upon applying S_k, K_u, P_e, D_a and C_c to both positive and negative cycles of H_qmax(φ), H_qn(φ), and H_n(φ), a total of 24 statistical features have been extracted from a PD pattern. The significant features are extracted from statistical features by using the NLPCA scheme. There are 10 extracted features for feature vector extracted from 24 statistical features using the NLPCA scheme in this experiment.

성능/효과

The experimental results and the analysis using 250 sets of field-test PD patterns from high-voltage CRCTs are presented in Section 6. From the test results, the effectiveness of the proposed scheme to improve the recognition accuracy has been demonstrated. The paper is concluded in Section 7.

참고문헌 (14)

J. Y. Jeong, D. S. Kang, J. H. Sun, J. C. Heo and C. H. Park, "Assessment of 23 kV Capacitive Coupler for On-line Partial Discharge Measurements," Journal of Electrical Engineering & Technology, vol. 4, no.1, pp. 123-130, March 2009.

원문보기 상세보기
A. Rodrigo, P. Llovera, V. Fuster and A. Quijano, "Study of Partial Discharge Charge Evaluation and the Associated Uncertainty by Means of High Frequency Current Transformers," IEEE Trans. Dielectrics and Electrical Insulation, vol. 19, no. 2, pp. 434-442, April 2012.

상세보기
M. Oskuoee, A.R. Yazdizadeh and H.R. Mahdiani, "A New Feature Extraction and Pattern Recognition of Partial Discharge in Solid Material by Neural Network," in Proceedings of the Eighth International Conference on Natural Computation, Chongqing, China, May 2012.
J. Tang, F. Liu, Q.H. Meng, X.X. Zhang and J.G. Tao, "Partial Discharge Recognition Through an Analysis of SF6 Decomposition Products Part 2: Feature Extraction and Decision Tree-Based Pattern Recognition," IEEE Trans. Dielectrics and Electrical Insulation, vol. 19, no. 1, pp. 37-44, February 2012.

상세보기
W.Y. Chang and H.T. Yang, "Application of Self Organizing Map Approach to Partial Discharge Pattern Recognition of Cast-Resin Current Transformers," WSEAS Trans. Computer Research, vol. 3, no. 3, pp. 142-151, March 2008.
S.W. Gao, M. Zhang, C.H. Yuan, X.T. Sun and C. Zhang, "Wavelet Packet Analyzing of Power Transformer Partial Discharge Signals," in Proceedings of the 2011 International Conference on Control, Automation and Systems Engineering, Singapore, July 2011.
W.Y. Chang, "Application of Grey Clustering Approach and Genetic Algorithm to Partial Discharge Pattern Recognition," WSEAS Trans. Systems, vol. 8, no. 12, pp. 1273-1283, December 2009.
R.J. Liao, K. Wang, L.J. Yang, T.C. Zhou and S.X. Zheng, "Study on Thermal Aging Condition Assessment of Oil-paper Insulation Based on Statistical Features of Partial Discharge," in Proceedings of the IEEE 9th International Conference on Properties and Applications of Dielectric Materials, Harbin, China, July 2009.
N.C. Sahoo and M.M.A. Salama, "Trends in Partial Discharge Pattern Classification: A Survey," IEEE Trans. Dielectrics and Electrical Insulation, vol. 12, no. 2, pp. 248-264, April 2005.

상세보기
R.E. James and B.T. Phung, "Development of Computer-based Measurements and Their Application to PD Pattern Analysis," IEEE Trans. Dielectrics and Electrical Insulation, vol. 2, no. 5, pp. 838-856, October 1995.

상세보기
T.A. Reddy, K.R. Devi and S.V. Gangashetty, "Nonlinear Principal Component Analysis for Seismic Data Compression," in Proceedings of the 1st International Conference on Recent Advances in Information Technology, Dhanbad, India, March 2012.
T. Stamkopoulos, K. Diamantaras, N. Maglaveras, and M. Strintzis, "ECG Analysis Using Nonlinear PCA Neural Networks for Ischemia Detection," IEEE Trans. Signal Processing, vol. 46, no. 11, pp. 3058-3066, November 1998.

상세보기
Ali Karami, "Radial Basis Function Neural Network for Power System Transient Energy Margin Estimation," Journal of Electrical Engineering & Technology, vol. 3, no.4, pp. 468-475, December 2008.

원문보기 상세보기
Y. Zhang, Q. Zhou, C. Sun, S. Lei, Y. Liu and Y, Song, "RBF Neural Network and ANFIS-based S hort-term Load Forecasting Approach in Real-time P rice Environment," IEEE Trans. Power Systems, vol. 23 no. 3, pp. 853-858, August 2008.

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[국내논문] Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network 원문보기

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[국내논문] Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network 원문보기

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