جستجوی مقالات مرتبط با کلیدواژه « طبقه بندی خطا » در نشریات گروه « برق »

The enhanced power transfer capability is possible with the six-phase transmission system but it did not gain popularity due to the lack of a proper protection scheme to secure the line from 120 types of different possible short circuit faults. This work presents a protection scheme with discrete wavelet transform (db4 mother wavelet) and an artificial neural network (ANN). The Levenberg-Marquardt algorithm is used for training the ANNs. This protection scheme requires only the pre-processed current information of the sending end bus. For fault detection and classification of all 120 fault types, a single ANN module is implemented with six inputs and six outputs. For fault location estimation in each phase, 11 ANN modules with six outputs are implemented, one for each of the 11 types of combination of faults. The MATLAB/ SIMULINK simulation results of the proposed protection technique implemented on the six-phase Allegheny power transmission system show that it is effective and efficient in detecting and classifying all the faults with varying fault parameters with an accuracy of 99.76%. It is found that the performance of the fault location estimation modules is better with the training data and moderate with the testing data.

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.

In this paper, a single-ended fault location method is presented based on a circuit breaker operation using the frequencies of traveling waves. The proposed method receives the required data from voltage traveling waves with the aid of Fast Fourier Transform (FFT) and Wavelet Transform. Then, the Artificial Neural Network (ANN) identifies the fault type and determines its location. For the evaluation of the proposed method, numerous simulations were done by varying parameters including fault resistance, fault inception angle, fault location, the presence of noise in waves, different sampling frequencies, and different structures of the power system in PSCAD/EMTDC software. Then, by using the matrix data obtained from voltage signals, the training process of the proposed algorithm is implemented in MATLAB software. The given results show the acceptable accuracy of the proposed technique in the classification of fault type and in the determination of fault location comparing with the previous studies. Also, the maximum error of the proposed method is 1.29 percent. It stands for the robustness of the proposed scheme and is higher than those of the previous studies in the situations that may affect fault identification process.