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This paper introduces a new noniterative fault-location algorithm for long transmission lines compensated by a series flexible ac transmission systems (FACTS) device. In the proposed algorithm, synchronous voltage and current samples from both ends of the transmission line are used and the distributed parameter line model in the time domain is applied. Due to the difficulties in the modeling of the series FACTS devices during a fault, the presented method does not use the model of the series devices. In this paper, the fault-location problem is converted to an optimization one in which the location and resistance of the fault are the decision variables. The proposed method includes three stages. Two fault locations are obtained, corresponding to the first two stages. In stage three, the obtained results from stages 1 and 2 are compared, and the correct fault location is selected. The proposed algorithm is a noniterative one and it is suitable for transmission lines compensated by any series FACTS device which could have the overvoltage protection and can be in any operating mode. The performance of the proposed algorithm is evaluated under different structural and fault conditions. The simulation results confirm the accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new method for online symmetrical components and phase-angle extraction from high-voltage transmission-line faults. This method is based on the Adaline neural networks and the instantaneous power theory, also known as the p-q method. A new current decomposition is proposed in order to derive the direct, inverse, and homopolar current components. The average and oscillating terms of powers in the \alpha \beta frame are separated by using four Adaline neural networks. The Adalines use a cosine and sine as inputs in order to learn the linear combination of the powers. The resulting symmetrical components are used by three other Adalines for phase-angle estimation between direct and inverse current components. These phase angles permit classifying the fault types. The neural networks use an online learning process-based Widrow–Hoff algorithm and can adapt their weight parameters to the power-supply evolution. Simulation results show the performance and the robustness of this method and provide a perspective for protection relay improvement. [ABSTRACT FROM AUTHOR]

This paper presents a new two-terminal impedance-based fault-location algorithm, which takes into account the distributed parameter line model. The algorithm utilizes unsynchronized measurements of voltages and currents from two ends of a line and is formulated in terms of the fundamental frequency phasors of symmetrical components of the measured signals. First, an analytical synchronization of the unsynchronized measurements is performed with use of the determined synchronization operator. Then, the distance to fault is calculated as for the synchronized measurements. Simultaneous usage of two kinds of symmetrical components for determining the synchronization operator makes that the calculations are simple, noniterative, and at the same time highly accurate. The developed fault-location algorithm has been thoroughly tested using signals of Alternate Transients Program-Electromagnetic Transients Program versatile simulations of faults on a transmission line. The presented evaluation shows the validity of the developed fault-location algorithm and its high accuracy. [ABSTRACT FROM AUTHOR]