State variable technique islanding detection using time-frequency energy analysis for DFIG wind turbine in microgrid system

This paper introduces a new combined technique for wind turbine islanding detection using the trajectory of state variables and wavelet transform in microgrid system. The proposed relay is utilized of energy variation state of time-frequency transform coefficients of local signals in two-dimensional space. In order to improve of the proposed relay performance, a signal selection method based on the correlation concept between islanding and non-islanding signals. From of all patterns, the best of them with high correlation in islanding status is selected for the proposed relay learning. A neuro-fuzzy machine is used as learning of selected patterns to avoid threshold selection. The proposed technique is utilized to wind turbine islanding detection in a microgrid system contains various types of distributed generation including wind turbine, Combined Heat and Power (CHP) and photovoltaic system. Many islanding and non-islanding situation including motor starting, various load and capacitor switching in various operation conditions in the studied microgrid are simulated. Millstone characteristics of the proposed technique are included passive-based technique, negligible None Detection Zone (NDZ), suitable for microgrid application, performance increment in detection and fast detection time. Operation results of the proposed relay in various conditions confirm the performance of the proposed detection relay.