Open Access Open Access  Restricted Access Subscription or Fee Access

Detection of Power Quality Disturbances and Classification featuring RBFNN-PSO

P. Kanirajan

Abstract


The Radial Basis Function Neural Networks (RBFNN) generated by Particle Swarm Optimization is being used in work to provide a unique method for detecting and classifying power quality disruptions in the electricity system (PSO). The Back Propagation (BP) technique is the one that is most frequently used for training; however it has a high computational cost and a poor convergence rate. The wavelet-extracted component is often used as training data. Following training, the weight attained is utilised to categorise the issues with power quality. There are 8 different categories of disturbance that are considered for classification. The RBFNN trained PSO algorithm's classification accuracy is contrasted with that of the BP method. When compared to BP approaches, the simulation results utilising PSO shows a considerable enhancement in signal identification and categorization.


Full Text:

PDF

References


Kanirajan, P & Suresh Kumar, V 2015, ‘Power quality disturbances detection and classification using wavelet and RBFNN’ In Applied Soft Computing, Elsevier, Vol. 35, pp. 470–481.

Kanirajan, P & Suresh Kumar, V ‘Wavelet-based power quality disturbances detection and classification using RBFNN and Fuzzy Logic’, International Journal of Fuzzy Systems, Springer, Vol.17 (4), pp.623–634.

Kanirajan, P & Suresh Kumar, V 2015, ‘A wavelet based data compression technique for power quality events classification’, WSEAS Trans. on Power system, Vol. 10, pp. 82–88

Kanirajan P, Eswaran and V. Sureshkumar “An Integrated Data Compression Using Wavelet and Neural Network for Power Quality Disturbances” Journal of Electrical Engineering vol.19(5), 2019.

Kanirajan P, M. Joly and Eswaran “A Comparison of Back propagation and PSO for training RBF Neural Network for Wavelet based Detection and Classification of Power Quality Disturbances” Journal of Electrical Engineering “International Journal of Signal Processing, Vol.6 2021.

Chun-Yao Lee and Yi-Xing shen,“Optimal Feature Selection for Power Quality Disturbances Classification”, IEEE Trans.PowerDel.Vol.26.No.4. pp.2342–2351, Oct. 2000.

W. Edward Reid, “Power Quality Issues-Standards and Guidelines”, IEEE Trans. Industry Applications, Vol.32. No.3. pp.625–632, May/June1996.

A. Elmitwally; S. Farghal; M. Kandil; S. Abdelkader and M. Elkateb, Proposed “wavelet-nerofuzzy combined system for power quality violations detection and diagnosis”, Pros. Inst. Elect. Eng., Gen, Transm, Distrib., Vol. 148. No. 1.pp.15–20,Jan.2001.

T. Mcconaghy, H. Lung, E. Bose; V. Vardan, “Classification of Audio radar sgnals using Radial Basis Function Neural Networks”, IEEE Trans. Inst. And Measurements, Vol.52. No. 6. pp.1771–1779, Dec.2003

Chia-Hung Lina and Chia-Hao Wang, “Adaptive Wavelet Networks for Power Quality Detection and Discrimination in a Power system”, IEEE Trans. Power Del. Vol. 21. No.3. pp.1106–1113, July 2006.

S. Santoso. “Power quality assessment via wavelet transform analysis,” IEEE Trans.Power Del., Vol.11.pp.924–930,Apr.1995.

Gauda, M., Salama, M.A., Sultam, M.R. and Chikhani A.Y. “Power quality detection and classification using wavelet multi-resolution signal decomposition”, IEEE Trans. On Power del., Vol.14. pp.1469–1476,1999.

Jaideva C. Goswami and Andrew K. Chan, Fundamentals of wavelets: Theory, Algorithms, and Applications John Wiley & Sons, 1999.

Inigo Monedero; Carlos Leon; Jorge Ropero; Antonio Garcia and Jose Manuel Elena, “Classification of Electrical Disturbances in Real Time using Neural Networks”, IEEE Trans. Power Del., Vol.22. No. 3. pp.1288–1296, July 2007

Masoum; M.A.S, Jamali, S and Ghaftarzadeh, N., “Detection and Classification of power quality disturbances using discrete wavelet transform and wavelet network”, IET Science, measurements & technology.,Vol.4.pp.193–205, 2010.

Z.L. Gaing, “Wavelet-Based neural network for power disturbance recognition and classification”, IEEE Trans. Power Del., Vol.19.No.4. pp.1560–1568.Oct.2004.

S. Mishra; C.N. Bhende and B.K. Panigrahi, “Detection and Classification of Power Quality Disturbances using S-Transform and Probabilstic Neural Networks”, IEEE Trans. Power Del., Vol.23. No.1. pp.280–286 Jan.2008.

A. Garcia-Perez and E. Cabal-Yepez, “Techniques and methodologies for power quality analysis and disturbances classification in power systems A review”, IET Generation Transm Distrib. Vol.5. No.4. Pp.519–529, Apr.2011.

C.I. Chen, “Virtual Multifunction power quality analyzer based on adaptive linear neural network”, IEEE Trans. Ind. Electron, Vol.59. No.8.pp.3321–3329, Aug.2012.

Prakash K. Ray; Soumy a R. Mohanty and Nand Kishor, “Classification of Power Quality Disturbances Dueto Environmental Characteristics in Distributed Generation System”, IEEE Trans.on sustainable energy., Vol.4. No.2. pp.302–313, Apr.2013.

X.Hu, Y. Shi and R. Eberhart, “Recent advances in Particle Swarm Optimization”, Proceedings of the congress on Evolutionary Computation, Portland, OR, USA, 1(2004), pp.90–97.

K.G Narendra; V.K. Stood; K. Khorasani and R. Patel, “Application of Radial basis Function (RBF) Neural Network for fault diagnosis in a HVDC system”, IEEE Trans.on Power Systems., Vol.13. No.1. pp.177–183, Feb.1998.

M. Chester, Neural Networks.A tutorial, London, pp.50–66. Prentce hall, (1993)

R.C. Eberhart and Y. Shi, “Particle swarm Optimization Developments, Applications and Resources”, Proceedngs of the 2001 Congress on Evolutionary Computation, 1(2001), pp.81–86.


Refbacks

  • There are currently no refbacks.