A new islanding detection technique based on rate of change of reactive power and radial basis function neural network for distributed generation

Accurate and fast islanding detection of distributed generation is extremely important for its effective operation in distribution systems. For this purpose, several islanding detection methods have been suggested. Among them, hybrid islanding detection techniques are preferred due to their minimum effects on the power system. However, hybrid islanding detection techniques also suffer from two main limitations. They still degrade the power quality and also take comparatively large time to detect the islanding phenomenon. Thus, fast detection and power quality degradation issues are still not solved by hybrid islanding detection techniques. To address this issue, this paper suggests a new islanding detection technique based on rate of change of reactive power (ROCORP) and radial basis function neural network (RBFNN). The proposed technique uses ROCORP as the RBFNN input. The appropriate database of several islanding and non-islanding events is generated by performing the offline simulations on 26 Bus Malaysian distribution system for training the RBFNN. The simulation results shows that it can detects islanding and non-islanding events very fast without degrading the power quality of the system and is independent of threshold limitations.