The power systems are complex and not always easy to understand. Now a days Distributed Generation (DG) based on renewable energy resources enrolled an exponential progress encouraged by the policy makers, global concerns about CO2 emissions, energy shortage, and anxiety for clean strength generation. This leads to a quick and constant upgrading of improvement in this particular region, bringing these systems at a point where their unwavering quality and fitness isn't any longer examined. The greatest challenge for electrical engineers is to provide a better protection scheme to detect the high impedance faults (HIFs). HIF conventionally happen when the primary conductor makes unwanted electrical contact with a rod surface, sod, tree limb, which restricts the flow of fault current to a level underneath that consistently detectable by conventional overcurrent devices. A novel and efficient protection scheme have to able to detect the HIFs and which can operate power system adequately, and protects the equipment as well as the public from hazardous over voltages. This paper pursues is to analysis of literature associated to HIF appearance. In this work, fuzzy logic technique used for HIF detection are assessed. The proposed HIF model gives more exact and consistent compared to other methods.
High Impedance Fault detection in a solar photovoltaic (PV) and wind generator integrated power system is described in this paper using discrete wavelet transform and a neural network with radial basis function (NNRBF). For this paper, the integration of solar photovoltaic and wind systems was modelled in a MATLAB/Simulink environment to create an IEEE 13-bus system. Microgrids (MG’s) are mostly powered by renewable energy. Uncertainty about renewables has shifted attention to ensuring a steady supply and long-term viability. It has been addressed in the paper whether or not a small-scale distant end source connection may be made at the terminal of a radial distribution feeder. Some typical power system problems compromise the reliability of the grid’s power supply. To solve this problem, this study suggests a criterion algorithm based on the neural network with radial basis function (NNRBF), and a defect detection method based on the discrete wavelet transform (DWT). The MATLAB/Simulink model of the system is then used to produce fault and travelling wave signals. The db4 wavelet is used to deconstruct the travelling wave signals into detail and approximate signals, which are then combined with the data from the two-terminal travelling wave localization approach for fault detection. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). The results show that both the criterion algorithm and the fault detection algorithm are reliable in their assessments of whether or not faults exist in the power system, and that neither algorithm is particularly sensitive to variations in fault type, fault detection, fault initial angle, or transition resistance. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). Overhead distribution system faults are simulated in Matlab/Simulink, and the technique is rigorously validated across a wide range of system situations. It has been shown through simulations that the proposed method can be relied upon to successfully and dependably protect high impedance fault (Hi-Z).
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