This paper presents an improved approach for locating and identifying faults for UHV overhead Transmission line by using GA-ANFIS. The proposed method uses one end data to identify the fault location. The ANFIS can be viewed either as a Fuzzy system, neural network or fuzzy neural network FNN. The integration with neural technology enhances fuzzy logic system on learning capabilities are proposed to analyze the UHV system under different fault conditions. The performance variation of two controllers in finding fault location is analyzed. This paper analyses various faults under different conditions in an UHV using Matlab/simulink. The proposed method is evaluated under different fault conditions such as fault inception angle, fault resistance and fault distance. Simulation results confirm that the proposed method can be used as an efficient for accurate fault location on the transmission line.

The paper presents analysis of the possibility of using selected hash functions submitted for the SHA-3 competition in the SDEx encryption method. The group of these functions will include the finalists of the SHA-3 competition, i.e. BLAKE, Grøstl, JH, Keccak, Skein. The aim of the analysis is to develop more secure and faster cryptographic algorithm compared to the current version of the SDEx method with SHA- 512 and the AES algorithm. When considering the speed of algorithms, mainly the software implementation will be taken into account, as it is the most commonly used.