A modular neuro-wavelet based non-unit protection scheme for zone identification and fault location in six-phase transmission line

To overcome the constraints on land availability, infrastructure and environmental problems, six-phase transmission lines have been proposed as a potential alternative to increase the power transfer capability of existing transmission lines without major modification in the existing structure of three-phase double-circuit system. The non-availability of a proper protection scheme due to large number of possible faults has been the prime reason behind the low popularity and acceptance of six-phase system. In this regard, the present work proposes a protection scheme for six-phase transmission line based on the hybridization of discrete wavelet transform and modular artificial neural network. The fault information (approximate coefficients) in the voltage and current signals is captured using discrete wavelet transform. The standard deviation of the coefficients of voltage and current signals in each phase is then computed and given as input to modular artificial neural network, which aims at identifying the faulty section/zone and estimate its location. Test results exhibit that the proposed scheme effectively discriminates the faulted section and estimates the fault location with maximum error of 0.675 %. It offers primary protection to the total line length and also provides remote backup protection for the adjacent reverse section of the line using data at relaying point only and thus avoids the need of a communication link.