Deep learning hybrid method for islanding detection in distributed generation

The increasing penetration of distributed energy brings significant uncertainty and noises to microgrid operation, which enlarge the difficulty of microgrid monitoring. For as much as the detection of islanding is prone to be interfered by grid disturbance, island detection device may make misjudgment thus causing the consequence of distributed generations (DGs) out of service. The detection device must provide with the ability to differ islanding from grid disturbance. In this paper, the concept of deep learning is introduced into the classification of islanding and grid disturbance for the first time. A novel deep learning framework is proposed to detect and classify islanding or grid disturbance. The framework is a hybrid of wavelet transformation, multi-resolution singular spectrum entropy, and deep learning architecture. As a signal processing method after wavelet transformation, multi-resolution singular spectrum entropy combines multi-resolution analysis and spectrum analysis with entropy as output, from which we can extract the intrinsic different features between islanding and grid disturbance. With the features extracted, a deep learning based algorithm is proposed to classify islanding and grid disturbance. Simulation results indicate that the method can achieve its goal while being highly accurate, so the DGs mistakenly withdrawing from power grids can be avoided.