access icon free Real-time cross-correlation-based technique for detection and classification of power quality disturbances

© The Institution of Engineering and Technology
Received 05/04/2017, Accepted 01/10/2017, Revised 18/09/2017, Published 03/10/2017

This study presents a novel technique for automated power quality (PQ) disturbance detection and classification in power distribution system using cross-correlation-based approach in conjunction with fuzzy logic. The proposed method requires minimum number of features when compared with conventional approaches for identification of disturbances. Total 17 types of PQ disturbances including eight basic and nine combinations which are very close to real situations are considered for the classification. The scheme is immune to real life uncorrelated noises due to incorporation of cross spectrum analysis in the feature extraction stage. Experimentation under real operating conditions is carried out in the laboratory using data acquisition system in order to test the proposed technique. The proposed scheme is also applied in IEEE 33-bus distribution system and validated by a real-time simulator. The developed classifier achieved 100% accuracy and could comfortably outperform several contemporary methods for PQ disturbance classification.

Inspec keywords: power supply quality; data acquisition; power distribution faults; feature extraction

Other keywords: automated PQ disturbance detection; power distribution system; cross spectrum analysis; disturbances identification; fuzzy logic; automated PQ disturbance classification; cross-correlation-based approach; automated power quality disturbances classification; feature extraction; IEEE 33-bus distribution system; real-time cross-correlation-based technique; automated power quality disturbances detection; data acquisition system

Subjects: Data acquisition systems; Power supply quality and harmonics; Distribution networks

References

    1. 1)
      • 11. Kanirajan, P., Suresh Kumar, V.: ‘Power quality disturbance detection and classification using wavelet and RBFNN’, Appl. Soft Comput., 2015, 35, pp. 470–481.
    2. 2)
      • 1. Granados-Lieberman, D., Romero-Troncoso, R.J., Osornio-Rios, R.A., et al: ‘Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review’, IET Gener. Transm. Distrib., 2011, 5, (4), pp. 519–529.
    3. 3)
      • 13. Kumar, R., Singh, B., Shahani, D.T.: ‘Recognition of single-stage and multiple power quality events using Hilbert–Huang transform and probabilistic neural network’, Electr. Power Compon. Syst., 2015, 43, (6), pp. 607–619.
    4. 4)
      • 21. Khadse, C.B., Chaudhari, M.A., Borghate, V.B.: ‘Conjugate gradient back-propagation based artificial neural network for real time power quality assessment’, Electr. Power Energy Syst., 2016, 82, pp. 197–206.
    5. 5)
      • 20. Dasgupta, A., Debnath, S., Das, A.: ‘Transmission line fault detection and classification using cross-correlation and K-nearest neighbor’, Int. J. Knowl. Based Intell. Eng. Syst., 2015, 19, (10), pp. 183–189.
    6. 6)
      • 10. Sabarimalai Manikandan, M., Samantaray, S.R., Kamwa, I.: ‘Detection and classification of power quality disturbances using sparse signal decomposition on hybrid dictionaries’, IEEE Trans. Instrum. Meas., 2015, 64, (1), pp. 27–38.
    7. 7)
      • 14. Eristi, H., Demir, Y.: ‘A new algorithm for automatic classification of power quality events based on wavelet transform and SVM’, Electr. Power Syst. Res., 2009, 79, pp. 569–575.
    8. 8)
      • 4. Barros, J., Apraiz, M., Diego, R.I.: ‘A virtual measurement instrument for electrical power quality analysis using wavelets’, Measurement, 2009, 42, (2), pp. 298–307.
    9. 9)
      • 9. Valtierra-Rodriguez, M., de Jesus Romero-Troncoso, R., Osornio-Rios, R.A., et al: ‘Detection and classification of single and combined power quality disturbances using neural networks’, IEEE Trans. Ind. Electron., 2014, 61, (5), pp. 2473–2482.
    10. 10)
      • 26. Dalai, S., Chatterjee, B., Dey, D., et al: ‘Rough-set-based feature selection and classification for power quality sensing device employing correlation techniques’, IEEE Sens. J., 2013, 13, (2), pp. 563–573.
    11. 11)
      • 22. Yadav, A., Swetapadma, A.: ‘Enhancing the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy inference system’, IET Gener. Transm. Distrib, 2015, 9, (6), pp. 580–591.
    12. 12)
      • 8. Cho, S.H., Jang, G., Kwon, S.H.: ‘Time-frequency analysis of power quality disturbances via the Gabor–Wigner transform’, IEEE Trans. Power Deliv., 2010, 25, (1), pp. 494–499.
    13. 13)
      • 16. Sobrinho, A.S.F., Flauzino, R.A., Liboni, L.H.B., et al: ‘Proposal of a fuzzy based PMU for detection and classification of disturbances in power distribution networks’, Electr. Power Energy Syst., 2017, 94, pp. 27–40.
    14. 14)
      • 12. Dalai, S., Dey, D., Chatterjee, B., et al: ‘Cross-spectrum analysis-based scheme for multiple power quality disturbance sensing device’, IEEE Sens. J., 2015, 15, (7), pp. 3989–3997.
    15. 15)
      • 27. Deokar, S.A., Waghmare, L.M.: ‘Integrated DWT-FFT approach for detection and classification of power quality disturbances’, Electr. Power Energy Syst., 2014, 61, pp. 594–605.
    16. 16)
      • 18. Moravej, Z., Banihashemi, S.A., Velayati, M.H.: ‘Power quality events classification and recognition using a novel support vector algorithm’, Energy Convers. Manage., 2009, 50, pp. 3071–3077.
    17. 17)
      • 2. Coppola, L., Liu, Q., Buso, S., et al: ‘Wavelet transform as an alternative to the short-time Fourier transform for the study of conducted noise in power electronics’, IEEE Trans. Ind. Electron., 2008, 55, (2), pp. 880–887.
    18. 18)
      • 17. Lee, C.-Y., Shen, Y.-X.: ‘Optimal feature selection for power-quality disturbances classification’, IEEE Trans. Power Deliv., 2011, 26, (4), pp. 2342–2351.
    19. 19)
      • 3. Decanini, J.G.M.S., Tonelli-Neto, M.S., Malange, F.C.V., et al: ‘Detection and classification of voltage disturbances using a fuzzy-ARTMAP-wavelet network’, Elect. Power Syst. Res., 2011, 81, (12), pp. 2057–2065.
    20. 20)
      • 15. Eristi, H., Yıldırım, O., Eristi, B., et al: ‘Automatic recognition system of underlying causes of power quality disturbances based on S-transform and extreme learning machine’, Electr. Power Energy Syst., 2014, 61, pp. 553–562.
    21. 21)
      • 7. Abdelsalam, A.A., Eldesouky, A.A., Sallam, A.A.: ‘Characterization of power quality disturbances using hybrid technique of linear Kalman filter and fuzzy-expert system’, Elect. Power Syst. Res., 2012, 83, (1), pp. 41–50.
    22. 22)
      • 24. Reddy, M.V., Sodhi, R.: ‘A rule-based S-transform and AdaBoost based approach for power quality assessment’, Electr. Power Syst. Res., 2016, 134, pp. 66–79.
    23. 23)
      • 19. Biswal, B., Biswal, M., Mishra, S., et al: ‘Automatic classification of power quality events using balanced neural tree’, IEEE Trans. Ind. Electron., 2014, 61, (1), pp. 521–530.
    24. 24)
      • 25. Singh, U., Singh, S.N.: ‘Application of fractional Fourier transform for classification of power quality disturbances’, IET Sci. Meas. Technol., 2017, 11, (1), pp. 67–76.
    25. 25)
      • 23. Behera, H., Dash, P., Biswal, B.: ‘Power quality time series data mining using S transform and fuzzy expert system’, Appl. Soft Comput., 2010, 10, (3), pp. 945–955.
    26. 26)
      • 5. Nguyen, T., Liao, Y.: ‘Power quality disturbance classification utilizing S-transform and binary feature matrix method’, Elect. Power Syst. Res., 2009, 79, (4), pp. 569–575.
    27. 27)
      • 6. Uyar, M., Yildirim, S., Gencoglu, M.T.: ‘An expert system based on S-transform and neural network for automatic classification of power quality disturbances’, Expert Syst. Appl., 2009, 36, (3), pp. 5962–5975.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.0507
Loading

Related content

content/journals/10.1049/iet-gtd.2017.0507
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading