No abstract

Abstract-The increasing penetration of distributed generators (DGs) in modern day power grids result in varying fault current levels and network scenarios which may affect the conventional over-current protection relays. This necessitates a protection scheme with efficient fault signal estimations and smart decision making capabilities in case of unexpected events. In this paper, a novel, fast and adaptive relay mechanism has been proposed for complete protection of radial distribution systems with DG penetration. A Fast Recursive Discrete Fourier Transform (FRDFT) algorithm is used here for efficient fundamental tracking of varying power system signals. The numerical relay design using FRDFT algorithm is embedded with Fuzzy Logic Decision Making (FLDM) module for obtaining optimal protection settings in case of changing system conditions. The proposed adaptive scheme is tested on a standard IEEE 34-bus distribution system equipped with DGs by simulating various case studies. Simulation results verify that the adaptive relay is able to capture the changing system scenarios and select the protection settings accordingly.Index Terms-Adaptive relay mechanism, distributed generators, fault signal estimation, fuzzy logic decision making, optimal protection settings, over-current protection relays.

With the increase in demand for energy, penetration of alternative sources of energy in the power grid has increased. Photovoltaic (PV) energy is the most common and popular form of energy sources which is widely integrated into the existing grid. As solar energy is intermittent in nature, to ensure uninterrupted and reliable power supply to the prosumers, it is essential to forecast the solar irradiance. Accurate solar forecasting is necessary to facilitate large-scale modelling and deployment of PV plants without disrupting the quality and reliability of the power grid as well as to manage the power demand and supply. There are various methods to predict the solar irradiance such as numerical weather prediction methods, satellite-based approaches, cloud-image based methodologies, data-driven methods, and sensor-network based approaches. This study gives an overall review of the different resources and methods used for forecasting solar irradiance in different time horizons and also gives an extensive review of the sensor networks that are used for determining solar irradiance. The various error metrics and accessible data sets available for the sensor networks are also discussed that can be used for validation purposes.

No abstract

No abstract