Series compensation has been employed to improve power transfer in long-distance transmission systems worldwide. However, this in turn introduces problems in conventional distance protection. The complex variation of line impedance is accentuated, as the capacitor’s own protection equipment operates randomly under fault conditions. This paper proposes two approaches based on travelling waves and artificial neural networks (ANN) for fault type classification and faulted phase selection of series compensated transmission lines. A modal transformation technique, which decomposes the three-phase line into three single-phase lines, is used for this purpose. Algorithms based on two different modal transformations are developed for phase selection and fault classification. Each algorithm is derived from a corresponding truth table. The truth tables are constructed for different types of faults with different faulted phases and different transformation bases. The proposed ANN topology is composed of two levels of neural networks: In level-1, a neural network (ANNF) is used to detect the fault. In level-2, four neural networks (ANNA, ANNB, ANNC and ANNG) are used to identify faulted phase(s), and activated by the output of ANNF if there is a fault. System simulation and test results, which are presented and analyzed in this paper indicate the feasibility of using travelling waves and ANN in the protection of series compensated transmission lines.
Available at: http://works.bepress.com/almoataz_abdelaziz/23/