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This article presents a new algorithm for fault detection in grid-tied microgrids with inverter-interfaced distributed generators (DGs). To support the Grid codes, the DGs require a low voltage ride through (LVRT) capability. The control strategy used in the DGs results in large changes in the fault characteristics of the microgrid. Hence, it is required to study the fault characteristics of the DGs in a microgrid under various operating conditions. The study presents fault detection for microgrids with PQ-controlled DGs having LVRT capability under different DG voltage and different fault conditions (high impedance and low impedance). The fault location has been identified using the phase change in the positive sequence current at specific DG voltages. The reliability of the proposed scheme has been validated under diverse fault conditions through extensive simulations in the Matlab/ Simulink environment, and the comparison with other fault detection techniques proves the efficacy of the proposed scheme for fault detection in microgrids. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm for the detection and classification of faults in transmission lines in the presence of distributed generation. The proposed algorithm uses wavelet transform-based detail and approximation coefficients of voltage and current signals over one cycle after fault inception at one end of the transmission line. The line impedance calculated from the ratio of the voltage and current approximation coefficients is fed to the fuzzy inference system to classify faults. The proposed algorithm has been tested successfully in a real-time digital simulator for nonlinear high impedance faults in the presence of wind farm, considering the effects of CT saturation. All types of faults with the variation of pre-fault loading condition, line parameters, line length and source impedance can be correctly classified using the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study aims to develop a new relaying scheme for the complete protection of transmission line based on only voltage data. Fault detection and classification is obtained from the fundamental component of the voltage signal collected from one end of the line. Magnitudes of positive sequence component of voltage signals from both ends of the line have been considered for fault location. As only magnitude of voltage signal is used, the requirement for data synchronization can be avoided, and the error caused by current transformers (CT) is eliminated. Moreover, positive sequence components exist for all types of faults, unlike negative sequence components. Hence, all types of faults can be located. Fuzzy decision approach has been proposed to avoid computationally expensive training of expert systems. The proposed scheme has been tested for different dynamic situations in MATLAB/Simulink software. The superiority of the scheme has been established by considering coupling capacitor voltage transformer (CCVT) transient error, weak infeed condition, change of transmission voltage level and line loading. [ABSTRACT FROM AUTHOR]