Metal oxide varistor (MOV) protected series capacitor, when present in a fault loop, modulates the impedance seen by the relay. With series capacitor placed away from the relay, the information on the level of compensation is required for proper protection decision by distance relay. In this study, synchronised data obtained at both ends are applied to calculate the compensation level which is used to adapt the relay setting for backup protection of adjacent line. The mutual coupling in the compensation portion is also considered in the apparent impedance computation. The status of capacitor during fault (bypassed or not) is obtained through impedance calculation during fault and the relay setting is adapted accordingly. Using data simulated through EMTDC/PSCAD for a series compensated line the technique is tested and found to be accurate.
Most of the speech processing applications use triangular filters spaced in mel-scale for feature extraction. In this paper, we propose a new data-driven filter design method which optimizes filter parameters from a given speech data. First, we introduce a frameselection based approach for developing speech-signal-based frequency warping scale. Then, we propose a new method for computing the filter frequency responses by using principal component analysis (PCA). The main advantage of the proposed method over the recently introduced deep learning based methods is that it requires very limited amount of unlabeled speech-data. We demonstrate that the proposed filterbank has more speaker discriminative power than commonly used mel filterbank as well as existing data-driven filterbank. We conduct automatic speaker verification (ASV) experiments with different corpora using various classifier back-ends. We show that the acoustic features created with proposed filterbank are better than existing mel-frequency cepstral coefficients (MFCCs) and speech-signal-based frequency cepstral coefficients (SFCCs) in most cases. In the experiments with VoxCeleb1 and popular i-vector back-end, we observe 9.75% relative improvement in equal error rate (EER) over MFCCs. Similarly, the relative improvement is 4.43% with recently introduced x-vector system. We obtain further improvement using fusion of the proposed method with standard MFCC-based approach.
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