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In this study, a novel intelligent relaying scheme is proposed for fault classification and faulted phase selection using magnitude of differential power (MODP). The DP is the multiplication of difference between the magnitude of the real and estimated voltage and current phasors. The MODP for each phase is used as an input data to the data mining model known as decision tree (DT). The DT is used to generate thresholds to detect and classify the faults, since determination of robust thresholds is an important challenge in protective relay engineering. The proposed method has been tested for different operating modes of single‐circuit transmission line in Simulink/MATLAB software. Simulation results indicate that the proposed method is able to detect and classify the faults with acceptable accuracy, in less than half cycle. [ABSTRACT FROM AUTHOR]

The ever increasing power demand and stress on reducing carbon footprint have paved the way forwidespread use of photovoltaic (PV) integrated microgrid. However, thedevelopment of a reliable protection scheme for PV integrated microgrid ischallenging because of the similar voltage‐current profile of PV array faultsand symmetrical line faults. Conventional protection schemes based onpre‐defined threshold setting are not able to distinguish between PV array andsymmetrical faults, and hence fail to provide separate controlling actions forthe two cases. In this regard, a protection scheme based on sparse autoencoder(SAE) and deep neural network has been proposed to discriminate between arrayfaults and symmetrical line faults in addition to perform mode detection, faultdetection, classification and section identification. The voltage‐currentsignals retrieved from relaying buses are converted into grey‐scale images andfurther fed as input to the SAE to perform unsupervised feature learning. Theperformance of the proposed scheme has been evaluated through reliabilityanalysis and compared with artificial neural network, support vector machine anddecision tree based techniques under both islanding and grid‐connected mode ofthe microgrid. The scheme has been also validated for field applications byperforming real‐time simulations on OPAL‐RT digital simulator. [ABSTRACT FROM AUTHOR]

This study deals with the faults' detection and classification in AC transmission lines based on power spectral density (PSD), introducing PSD in time and frequency for analysing transient information under faulted conditions. The discrete wavelet transform is used for scaling current signals in time–frequency at different decomposition levels by approximation and detail coefficients. Then, a wavelet‐covariance matrix is shaped with the aim to obtain its PSD, being this the key to detect and classifying faults. Results show that the proposed method attains the detection in a short time and the classification is accomplished via the Hellinger distance, whose straightforward implementation is carried out in this study. The classification process is compared adopting different classifiers to cope with a set of signals in a time frame. Finally, the proposal is extensively assessed using real and simulated signals stemming from multiple fault cases of radial and interconnected power grids. [ABSTRACT FROM AUTHOR]

In this study, a novel algorithm for detecting and classifying faults in transmission lines is proposed. The algorithm is based on mathematical morphology and initial current travelling waves. A new morphological edge detection (MED) filter to extract the transient features from the original fault signal is designed. This MED filter can fast and accurately detect the arrival time and polarity of travelling waves in all conditions. The appropriate criteria of fault classification and faulted‐phase selection are introduced based on polarity of initial current travelling waves. The simulations based on the electromagnetic transients program and MATLAB have been done to evaluate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a complete protection scheme for detecting, classifying and locating the fault in HVDC transmission lines using support vector machines (SVM). SVM has been used for protective relaying application in HVAC transmission line, however very limited works have been reported for HVDC transmission line. In this work, a ±500 kV HVDC transmission system is developed in PSCAD/EMTDC and the measurement signals obtained are analyzed in MATLAB. The rectifier side AC RMS voltage, DC voltage and current on both the poles are continuously monitored, and given as input to the SVM binary classifier in order to detect the presence of fault in the line. Once a fault is detected, the SVM multi‐class classification module predicts the type of fault and the SVM regression algorithm predicts the location of fault. The feature vector used in the classification and location modules is the standard deviation of the signals over half cycle before and after the occurrence of fault. The method proposed is simple as it requires single‐end data and a direct standard deviation of one cycle data gives very accurate results. The detection and classification modules are 100% accurate whereas the fault location module has a mean error of 0.03%. [ABSTRACT FROM AUTHOR]

In this study, a novel algorithm for detecting and classifying faults in high‐voltage transmission lines is proposed. The algorithm is based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The DWT is used for extracting the currents' high‐frequency components under fault conditions. Signals under each fault condition are scaled in frequency, in order to build a wavelet matrix. By means of the SVD, the maximum singular value is calculated and employed in this proposal. The attained results exhibit that the maximum singular value represents a good indicator for the issue. This novel approach for detecting and classifying faults in power systems is called maximum wavelet singular value. Phase‐to‐ground, two‐phase to ground, and three‐phase faults' simulations under different fault impedances are carried out by DIgSILENT Power Factory. The analysed fault conditions are evaluated demonstrating that the proposal reduces the computational burden and the time detection. [ABSTRACT FROM AUTHOR]

This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half‐cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ2 test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal‐operation so it is reliable, accurate and secure. [ABSTRACT FROM AUTHOR]