Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines

Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance

A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines

Discrimination of different DC faults near a converter end of a DC section consisting of a filter, a smoothing reactor, and a transmission line is not an easy task. The faults occurring in the AC section can be easily distinguished, but the internal and near-side external faults in the DC section are very similar, and the relay may cause false tripping. This work proposes a method to distinguish external and internal faults occurring in the DC section. The inputs are the voltage signals at the start of the transmission line and the end of the converter filter. The difference in voltage signals is calculated and given to an intelligent controller to detect and discriminate the faults. The intelligent controller is designed using machine learning (ML) and deep learning (DL) techniques for fault detection. The long short-term memory (LSTM-) based relay gives better results than other ML methods. The proposed method can distinguish internal from external faults with 100% accuracy. Another advantage is that a primary relay is suggested that detects faults quickly within a fraction of milliseconds. Nevertheless, another advantage is that a backup relay has been designed in case the primary relay cannot operate. Results show that the LSTM-based protection scheme provides higher sensitivity and reliability under different operation modes than the conventional traveling wave-based relay

A novel transmission line relaying scheme for fault detection and classification using wavelet transform and linear discriminant analysis

This paper proposes fault detection and classification scheme for transmission line protection using WT and linear discriminant analysis (LDA). Current signals of each phase are used for the detection and identification of faulty phases and zero sequence currents are used for the detection of ground. Current signals are processed using discrete wavelet transform with DB-4 wavelet up to level 3. Approximate coefficients are reconstructed using wavelet reconstruction. Performance of the proposed based scheme is tested by variations of parameters such as fault type, location, fault resistance, fault inception angle and power flow angle. The scheme is applicable for both single circuit and double circuit transmission line. All shunt faults and multi-location faults which occur in different locations at the same time are also detected and classified by the proposed scheme within one cycle time. The simulation results show that the proposed scheme is not affected by non-linear high impedance fault and CT saturation

Time domain complete protection scheme for parallel

In this paper, time domain relaying schemes for complete protection of parallel transmission lines using wavelet and artificial neural network (ANN) are presented. Four different ANN networks are designed for detection of the fault, fault section identification, classification of fault and location of fault in time domain. The 3rd level approximate discrete wavelet transform (DWT) coefficients of signals of one end are used as input to ANN network. Proposed method is tested with varying fault location, inception angle, fault type and fault resistance. The test results show that the fault is detected and located within 5 ms time accurately. This scheme offers primary protection as well as backup protection to the lines

Protection of parallel transmission lines including inter-circuit faults using Naïve Bayes classifier

Parallel transmission lines are difficult to protect due to mutual coupling between circuits. This paper proposes a Naïve Bayes classifier (NBC) based fault detection and classification technique for protection of parallel transmission line involving inter-circuit faults. NBC is a good classification tool for larger data sets as the training process takes less time with greater accuracy. Input given to the fault detection module is the fundamental components of three phase current signals of both circuits. Input given to the fault phase identification and fault classification module is the fundamental component of three phase current signals and zero sequence currents of both the circuits. Seven separate classifiers are designed for fault phase identification for A1, B1, C1, A2, B2, C2 and G. From fault phase identification module faults are classified. Accuracy of the proposed method is 100% for fault detection and 99.99% for classification of fault from all the tested fault cases. Response time of the proposed method is within 10 ms for all the fault cases studied

Enhancing the performance of motor imagery classification to design a robust brain computer interface using feed forward back-propagation neural network

This paper proposes a feed forward back-propagation neural network (FFBPNN) based method to enhance the performance of the motor imagery classification. The dataset consists of fifty nine channels of EEG signals which are first normalised using minmax method and then given as input to the FFBPNN network. Experimental outcomes of the FFBPNN are recorded in term of ‘0’s or ‘1’s for two classes of motor imagery signals. The accuracy of the proposed FFBPNN method has been measured using confusion matrix, mean square error and percentage accuracy. However, accuracy of the FFBPNN based method is recorded up to 99.8%. Hence the proposed method gives better accuracy of the classification which will ultimately help in designing robust BCI. Keywords: Artificial neural network, Back-propagation neural network, Pattern classification, Motor imagery, Brain computer interfac

Locating Faults in Thyristor-Based LCC-HVDC Transmission Lines Using Single End Measurements and Boosting Ensemble

Most of the fault location methods in high voltage direct current (HVDC) transmission lines usemethods which require signals from both ends. It will be difficult to estimate fault location if the signal recorded is not correct due to communication problems.Hence a robust method is required which can locate fault with minimum error. In this work, faults are located using boosting ensembles in HVDC transmission lines based on single terminal direct current (DC) signals. The signals are processed to obtain input features that vary with the fault distance. These input features are obtained by taking maximum of half cycle current signals after fault and minimum of half cycle voltage signals after fault from the root mean square of DC signals. The input features are input to a boosting ensemble for estimating the location of fault. Boosting ensemble method attempts to correct the errors from the previous models and find outputs by combining all models. The boosting ensemble method has been also compared with the decision tree method and thebagging-based ensemble method. Fault locations are estimated using three methods and compared to obtain an optimal method. The boosting ensemble method has better performance than all the other methods in locating the faults. It also validated varying fault resistance, smoothing reactors, boundary faults, pole to ground faults and pole to pole faults. The advantage of the method is that no communication link is needed. Another advantage is that it allowsreach setting up to 99.9% and does not exhibitthe problem of over-fitting. Another advantage is that the percentage error in locating faults is within 1% and has a low realization cost. The proposed method can be implemented in HVDC transmission lines effectively as an alternative to overcome the drawbacks of traveling wave methods