A novel approach for detection and classification of power quality (PQ) disturbances is proposed. The distorted waveforms (PQ events) are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio. The DWT is also used to decompose the signal of PQ events and to extract its useful information. Proper feature vectors are selected and applied in training the wavelet network classifier. The effectiveness of the proposed method is tested using a wide spectrum of PQ disturbances including dc offset, harmonics, flicker, interrupt, sag, swell, notching, transient and combinations of these events. Comparison of test results with those generated by other existing methods shows enhanced performance with a classification accuracy of 98.18%. The main contribution of the paper is an accurate (because of proper selection of feature vectors), fast (e.g. a new de-noising approach with proposed identification criterion) and robust (at different signal-to-noise ratios) wavelet network-based algorithm (as compared to the conventional wavelet-based algorithms) for detection/classification of individual, as well as combined PQ disturbances.

The paper describes a very accurate fault location technique which uses post-fault voltage and current derived at both line ends. Fault location is independent of fault resistance and the method does not require any knowledge of source impedance. It maintains high accuracy for untransposed lines and no fault type identification is required. The paper presents the theory of the technique and the results of simulation studies to determine its performance.

In this study, a novel digital algorithm is introduced for recognition of arcing (transient) faults and determination of dead time for adaptive auto-reclosing. The algorithm distinguishes between arcing and permanent faults by using the zero sequence voltage measured at the relaying point. If the fault is recognised as an arcing fault, then the third harmonic of the zero sequence voltage is used to evaluate the extinction time of secondary arc and to initiate reclosing signal. The proposed algorithm uses an adaptive threshold level and therefore no significant adjustment is needed for different transmission systems. Moreover, its performance is independent to fault location, line parameters and the system pre-fault operating conditions. The algorithm has been successfully tested for various faults and operating conditions on a 400 kV overhead line using the electro-magnetic transient program (EMTP). The test results have demonstrated validity of the algorithm in determining the secondary arc extinction time and blocking unsuccessful automatic reclosing during permanent faults.