v. pandya

This paper proposes a complete diagnostic analysis of faults in a typical modern power system's transmission line using the support vector machine (SVM) with time-series parameters and frequency series parameters as features. The training and testing data of the proposed method are collected by simulating all types of faults with all possible variations on a transmission line (TL) in the IEEE-9 bus system using the PSCAD/EMTDC software. While simulating one type of fault, fault resistances and fault inception angles are also varied to account for the various behaviours of the fault. The three-phase instantaneous currents and voltages on both sides of TL are recorded at 32 samples per cycle. A thirty-two sample moving window is used to compute time-series and frequency-series parameters applied as features to the SVM. Ten-fold cross-validation is used to evaluate the performance of the proposed algorithm with evaluation metrics such as accuracy, precision, recall and F1 score. Features generation, training and testing of the proposed method, and performance comparison are done using PYTHON software. The proposed method has achieved an average accuracy of 99.996%, even in the most contaminated environment of 30 dB noise. Compared with the performance of the other popular machine learning algorithms, the proposed method has achieved more accuracy. The performance of the proposed method is also tested with different noise levels, which account for the measurement errors of 30 dB, 35 dB and 40 dB.

This paper reports support vector machine (SVM) based fault detection and classification in microgrid while considering distortions in voltages and currents, time and frequency series parameters, and differential parameters. For SVM-based fault classification, the data set is formed by analysing the operation of the standard IEC microgrid model, with and without grid interconnection, under different fault and non-fault scenarios. Fault scenarios also include different locations, resistances, and incident angles of fault. Whereas, for non-fault scenarios, the variation in load is considered. Voltages and currents from both ends of the distribution line (DL) are sampled at 1920 Hz. The time and frequency series parameters, total harmonic distortion (THD) in current and voltage, and differential parameters are determined. The SVM algorithm uses these parameters to detect and classify faults. The performance of this developed SVM based algorithm is compared with that of different machine learning algorithms. This comparative analysis reveals that SVM detects and classifies the faults on the microgrid with an accuracy of over 99.99%. The performance of the proposed method is also tested with 30 dB, 35 dB, and 40 dB noise in the generated data, which represent measurement errors.