A passive islanding detection method using deep neural bidirectional LSTM-CNN

Microgrids based on local generation principle has advantages as reducing power losses and improving voltage profile. One of the biggest operational concerns in presence of a microgrid connected to a power system is unintentional islanding. Unintentional islanding events have potential to cause equipment damage and endanger human safety. According to IEEE Std 1547-2018, unintentional islanding must be detected and controlled in 2s. Therefore, a novel One-Dimensional Convolutional Neural Network-Bidirectional Long Short-Term Memory (1D CNN-BiLSTM) method is introduced for islanding detection. In the proposed method, power system is firstly controlled and observed to select busbars that are sensitive to islanding, then selected critical busbars are used for measurement. The optimally selected current and voltage time series data are then processed by neural network. Simulation results on the IEC 61850-7-420 test system demonstrate that the introduced method achieves a detection time of 10ms and a Non-Detection Zone (NDZ) rate of only 0.02%. These results indicate that the method significantly outperforms existing techniques in terms of both detection speed and reliability.