This paper`s approach based on discrete wavelet transform and Fuzzy c-means clustering for the detection and classification of the complex power quality disturbances. The complex power quality disturbances have been generated in MATLAB by various combinations of the mathematical models single stage power quality disturbances such as voltage sag, voltage swell, momentary interruption, oscillatory transient, impulsive transient, harmonics, notch and spike. The investigated complex power quality disturbances are (voltage sag + harmonics), (voltage swell + harmonics), (momentary interruption + harmonics), (oscillatory transient + voltage sag), (oscillatory transient + harmonics), (impulsive transient + voltage sag), (Impulsive Transient + harmonics) and (oscillatory Transient, Voltage Sag and Harmonics). The DWT based plots up to fourth level of decomposition of the voltage signal with complex PQ disturbance are used for the recognition of the complex PQ disturbances. The DWT based features have been given as input to the Fuzzy c-means clustering for classification purpose of the complex PQ disturbances. It is observed that the proposed algorithm is effective in the detection and classification of the complex power quality disturbances. The proposed approach has been implemented using the MATLAB codes.

Power Quality, Complex power quality disturbance; Discrete wavelet transform; Fuzzy C-means clustering

[1] Saurabh Kamble and Ishita Dupare, “Detection of power quality disturbances using Wavelet Transform and artificial neural network,” International Conference on Magnetics, Machines & Drives, 2014.
[2] Om Prakash Mahela, Abdul Gafoor Shaik and Neeraj Gupta, “A critical review of detection and classification of power quality events,” Renewable and Sustainable Energy Reviews, Vol. 41, pp. 495–505, 2015.
[3] Marcelo A.A. Lima, Augusto S. Cerqueira, Denis V. Courya and Carlos A. Duqueb, “A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis,” International Journal of Electrical Power and Energy Systems, vol. 42, pp. 593–604,June 2012.
[4] Rahul Dubey, S. R.Samantaray, B. Chitti Babu and S. Nandha Kumar, “Detection of power quality disturbances in presence of DFIG wind farm using Wavelet Transform based energy function,” IEEE International Conference, 2011.
[5] Norman C.F.Tse, John Y.C.Chan, Wing-Hong Lau and Loi Lei Lai, “Hybrid Wavelet and Hilbert Transform with frequency-shifting decomposition for power quality analysis,” IEEE Transactions on Instrumentation and Measurement , vol. 61, no. 12, pp-3225-3233, Dec 2012.
[6] E.A. Cano Plataa and H.E. Tacca, “Power load identification,” Journal of the Franklin Institute, vol. 342, pp 97–113, AUG2004.
[7] Alireza Akhbardeha, Sakari Junnilaa, Mikko Koivuluomaa, Teemu Koivistoinenb, Vaino¨ Turjanmaab, Tiit Koobib and Alpo Varria, “Towards a heart disease diagnosing system based on force sensitive chair’s measurement, biorthogonal wavelets and neural networks,” Engineering Applications of Artificial Intelligence, vol. 20, pp. 493–502,Oct 2007.
[8] Zahra Moravej, Jamal Dehghani Ashkezari and Mohammad Pazoki, “An effective combined method for symmetrical faults identification during power swing,” Electrical Power and Energy Systems, vol.64, pp. 24–34, July 2015.
[9] Tarun Kumar Chheepa, Tanuj Manglani, "Power Quality Events Classification using ANN with Hilbert Transform," IJMERT, Vol.6 , No.6 , pp.227-235, June 2017.
[10] Om Prakash Mahela, Abdul Gafoor Shaik, “Recognition of power quality disturbances using S-transform based ruled decision tree and fuzzy C-means clustering classifiers,” Applied Soft Computing, Vol. 59, pp. 243–257, 2017.