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This paper presents a Wavelet based alienation technique to detect and classify various faults on transmission line. The proposed scheme analyses the absolute values of three phase current signals over a half cycle to obtain detail coefficients. These detail coefficients of half a cycle are compared with those of previous half cycleto compute alienation coefficients which are further utilized to detect and classify the faults. The proposed technique was able to discriminate non-fault transients such as capacitance, inductance and load switching, from fault transients. The increase in the sensitivity of protection scheme, due to utilization of wavelet based detail decomposition, has been established by case studies. The proposed algorithm is tested for different locations and various types of faults. The algorithm is proved to be successful in detecting and classifying various types of faults in a half cycle.

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Electric power quality, which is a current interest to several power utilities all over the world, is often severely affected by harmonics and transient disturbances. There is no unique model which can assess the power quality problem and to identify and classify them properly. Existing automatic recognition methods need improvement in terms of their versatility, reliability, and accuracy. The FUZZY LOGIC based tools have been applied for the PQ classification. This paper addresses Power quality problem classification by wavelet and fuzzy expert system. Major Key issues and challenges related to these advanced techniques in automatic classification of PQ problems are highlighted. New intelligent system technologies using DSP, expert systems, AI and machine learning provide some unique advantages in intelligent classification of PQ distortions.

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