P. Venkata; V. Pandya; A.V. Sant
Abstract
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 ...
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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.
Insulation & High Voltage
M. Hasanpour; M. Ghanbari; V. Parvin-Darabad
Abstract
Mitigating switching overvoltages (SOVs) and conducting well-suited insulation coordination for handling stresses are very important in UHV transmission Lines. The best strategy in the absence of arresters is controlled switching (CS). Although elaborate works on electromagnetic transients are considered ...
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Mitigating switching overvoltages (SOVs) and conducting well-suited insulation coordination for handling stresses are very important in UHV transmission Lines. The best strategy in the absence of arresters is controlled switching (CS). Although elaborate works on electromagnetic transients are considered in the process of designing transmission systems, such works are not prevalent in day-to-day operations. The power utility and/or operator have to carefully monitor the peak values of SOVs so this values not to exceed the safe limits. In this paper, we present a novel CS approach in dealing with EMTP/ATP environment, where trapped charge (TC) is intended to train a radial basis function network (RBFN) meta-model that is implemented to calculate SOVs. A new weighted maximum overvoltage factor proposed to find locations of critical failure risk due to SOVs occurred along transmission lines. Power utilities or design engineers can benefit from the presented meta-model in designing a well-suited insulation level without spending time for taking into account the feasible risk value. Besides, the operators can energize the lines sequentially upon their choice; i.e., a safe and proper energization.
