THREE PHASE TRANSMISSION LINE FAULT DETECTION

Electrical Engineering

Electrical power transmission system is very important part for transmitted the electrical power. In transmission system there are different type of fault occurred. The good state of the power line can be restored by accurate fault detection. Moreover, accurate fault prediction plays an important role in network planning and operation. A novel approach for long transmission line fault detection, classification and prediction is presented in this paper. Different types of transmission line faults like line-to-ground (LG), line-to-line (LL), Line-line-ground (LLG), Line-line-line (LLL), Line-line-line-ground (LLLG), etc. have been introduced. The fault detection and classification are done online using a deep neural network (DNN) for better accuracy. This approach can deal with non-linear problems with very high accuracy. In addition to this, the proposed uses the instantaneous measure of the fault parameters like current, voltage, etc. online.Hence a perfect and most accurate fault prediction method can definitely improve the reliability and hence power quality of the system. The data extracted from the MATLAB workspace is analyzed in detail using seven different types of machine learning methods like logistic regression, SVM, Naïve Bayes, etc. in the Python platform individually. A comparative analysis is made to identify the most suitable predictive method for the proposed system

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