Assessment of Power Quality Events by Hilbert Transform Based Neural Network

Now a day‟s power quality (PQ) and power supply related problems have become important problem both for the end user and the utility company. The PQ issues and related phenomena are getting more dominant due to the use of power electronics devices, non-linear loads, industrial grade rectifiers and inverters, etc. This nonlinear equipment‟s not only introduce distortions in the amplitude but also in frequency, phase of the power signal, thereby degrades quality of power. In order to improve power quality, continuous monitoring of the signal is required. For continuous monitoring of the signal, the detection and classification of the power signal in power systems are important. In this work a new Time-frequency analysis method, has been introduced to detect and analyze for the non-stationary and nonlinear power system disturbance signals, known as HilbertHuang transform (HHT). Hilbert-Huang transform is able to find out, the starting time, ending time, instantaneous frequency-time, and instantaneous amplitude- time of the disturbance signal can be obtained precisely. Hilbert Huang transforms decomposition algorithm can be used for accurate detection & localization of point of disturbance of PQ events like voltage sag, swell, sag with harmonic, swell with harmonic, interruption, etc. Similarly the same power quality event was passed through a wavelet technique. Both results are obtained from decomposition of PQ events and pass through a back propagation neural network for proper classification of different types of PQ events. In this work, detection of PQ disturbances by HHT is compared with an advance wavelet transform technique. The localization and detection of PQ events have been thoroughly investigated for each of the power signal disturbances using HHT and wavelet transform. Finally, comparative classification accuracy has been estimated for both types of the decomposition technique for different types of PQ events