Skip to main content
Log in

Performance enhancement of extreme learning machine for power system disturbances classification

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper proposes an optimal feature and parameter selection approach for extreme learning machine (ELM) for classifying power system disturbances. The relevant features of non-stationary time series data from power disturbances are extracted using a multiresolution S-transform which can be treated either as a phase corrected wavelet transform or a variable window short-time Fourier transform. After extracting the relevant features from the time series data, an integrated PSO and ELM architectures are used for pattern recognition of disturbance waveform data. The particle swarm optimization is a powerful meta-heuristic technique in artificial intelligence field; therefore, this study proposes a PSO-based approach, to specify the beneficial features and the optimal parameter to enhance the performance of ELM. One of the advantages of ELM over other methods is that the parameter that the user must properly adjust is the number of hidden nodes only. In this paper, a hybrid optimization mechanism is proposed which combines the discrete-valued PSO with the continuous-valued PSO to optimize the input feature subset selection and the number of hidden nodes to enhance the performance of ELM. The experimental results showed the proposed algorithm is faster and more accurate in discriminating power system disturbances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Ahila R, Manimala K (2010) S transform based power disturbances classification: ANNs Vs SVMs. J Inst Eng (India) 91:37–44

    Google Scholar 

  • Ahila R, Sudhakaran M, Manimala K (2010) FW_SFS: a novel feature selection method for power quality data mining. Int J Power Energy Syst 30(2):139–147

    Google Scholar 

  • Biswal B, Dash PK, Panigrahi BK, Reddy JBV (2009) Power signal classification using dynamic wavelet network. Appl Soft Comput, pp 118–125

  • Chilukuri MV, Dash PK (2004) Multiresolution S-transform-based fuzzy recognition system for power quality events. IEEE Trans Power Deliv 19(1):323–330

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support vector networks, machine learning, vol 20. Kluwer Academic Publishers, Boston, pp 273–297 (Manufactured in The Netherlands)

  • Fernández-Navarro F, Hervás-Martínez C, Sanchez-Monedero J, Gutiérrez PA (2010) MELM-GRBF: a modified version of the extreme learning machine for generalized radial basis function neural networks. Neurocomputing 74(16):2502–2510

    Google Scholar 

  • Fusheng Z, Zhongxing G, Yaozhong G (1999) FFT algorithm with high accuracy for harmonic analysis in power system. In: Proceedings of CSEE (Chinese Society for Electrical Engineering), vol 19, 3rd edn, pp 63–66

  • Gaing ZL (2004) Wavelet-Based neural network for power disturbance recognition and classification. IEEE Trans Power Deliv 19(4):1560–1568

    Article  Google Scholar 

  • Huang G-B (2003) Learning capability and storage capacity of twohidden-layer feedforward networks. IEEE Trans Neural Netw 14(2):274–281

    Article  Google Scholar 

  • Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks (IJCNN2004), 25–29 July 2004, vol 2. Budapest, Hungary, pp 985–990

  • Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Google Scholar 

  • Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529

    Article  Google Scholar 

  • IEEE recommended practice for monitoring electric power quality (1995). IEEE/Std, pp 1159–1995

  • Jing W, Hong-Chun S, Xue-Yun C (2004) Fractal exponent wavelet analysis of dynamic power quality. In: Proceedings of CSEE (Chinese Society for Electrical Engineering), vol 24, 5th edn, pp 40–45

  • Kezunovic M, Yuan L (2002) A novel software implementation concept for power quality study. IEEE Trans Power Deliv 17(2):544–549

    Article  Google Scholar 

  • Lee IWC, Dash PK (2003) S-Transform-based intelligent system for classification of power quality disturbance signals. IEEE Trans Ind Electron 50(4):800–805

    Article  Google Scholar 

  • Lin W-M, Wu C-H, Lin C-H, Cheng F-S (2008) Detection and classification of multiple power-quality disturbances with wavelet multiclass SVM. IEEE Trans Power Deliv 23(4):2575–2582

    Google Scholar 

  • Przemyslaw J, Tadeusz L (2006) Automated classification of power quality disturbances using SVM and RBF networks. IEEE Trans Power Deliv 21:1663–1669

    Article  Google Scholar 

  • Saraswathi S, Sundaram S, Sundararajan N, Zimmermann M, Nilsen-Hamilton M (2011) ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE ACM Trans Comput Biol Bioinfo 8(2):452–463

    Google Scholar 

  • Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118

    Article  Google Scholar 

  • Slade WH, Ressom HW, Musavi MT, Miller RL (2004) Inversion of ocean color observations using particle swarm optimization. IEEE Trans Geosci Remote Sens 42(9):1915–1923

    Article  Google Scholar 

  • Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S Transform. IEEE Trans Signal Process 44(4):998–1001

    Article  Google Scholar 

  • Tripathy M, Maheshwari RP, Verma HK (2010) Power transformer differential protection based on optimal probabilistic neural network. IEEE Trans Power Deliv 25(1)

  • Yong Z, Hao-Zhong C, Yi-Feng D, Gan-Yun L, Yi-Bin S (2005) S-Transform-based classification of power quality disturbance signals by support vector machines. In: Proceedings of CSEE, vol 25, 4th edn, pp 51–56

  • Yonghai X, Xiangning X, Yihan Y (2001) Power quality disturbance identification using dq conversion-based neural classifier. Automat Elect Power Syst 25(14):24–28

    Google Scholar 

  • Youssef AM, Abdel-Galil TK, El-Saadany EF, Salama MMA (2004) Disturbance classification utilizing dynamic time warping classifier. IEEE Trans Power Deliv 19(1):272–278

    Article  Google Scholar 

  • Zhang R, Huang G-B, Sundararajan N, Saratchandran P (2007) Multicategory classification using an extreme learning machine for microarray gene expression cancer diagnosis. IEEE ACM Trans Comput Biol Bioinfo 4(3):485–495

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Ahila.

Additional information

Communicated by G. Acampora.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ahila, R., Sadasivam, V. Performance enhancement of extreme learning machine for power system disturbances classification. Soft Comput 18, 239–253 (2014). https://doi.org/10.1007/s00500-013-1051-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-013-1051-5

Keywords

Navigation