LAUSR

In distributed generation systems, islands of power generation will disturb grid recovery and power quality. Therefore, various islanding detection technologies have been proposed, and most are remote detection, local active, and passive methods. Most islanding detection technologies are currently implemented with high computational complexity, inaccurate detection, affecting power quality (active methods), and other problems. This paper proposes an islanding detection method based on S transform (ST) and Adaptive-Network-based Fuzzy Inference System (ANFIS). It uses ST for electrical signal processing and then extracts features which are used as the inputs of the ANFIS system. ANFIS starts training and learning to obtain classification capabilities and accurately determines the islanding events and outputs the results. The innovation of this method is the combination of ST and ANFIS structure. ST has better time-frequency characteristics and signal processing capability. ANFIS can automatically start training and learning in order to enhance the capability. The combination of these two methods can greatly improve the accuracy of islanding detection. The ST and ANFIS based islanding detection method proposed in this paper are simulated in MATLAB Simulink. According to the different characteristics of islanding and non-islanding conditions, full consideration is given to the comprehensiveness of the condition design, and 20 sets of data are designed. Through the training of 12 sets of data, and the testing of 8 sets of data, with equal quantity for the islanding and non-islanding cases, the output of this method is completely correct. Also, the proposed method is compared to ANFIS based and artificial neural network based islanding detection methods. The results show that the proposed ST-ANFIS based islanding detection method can accurately detect the islanding and non-islanding conditions with higher accuracy.In distributed generation systems, islands of power generation will disturb grid recovery and power quality. Therefore, various islanding detection technologies have been proposed, and most are remote detection, local active, and passive methods. Most islanding detection technologies are currently implemented with high computational complexity, inaccurate detection, affecting power quality (active methods), and other problems. This paper proposes an islanding detection method based on S transform (ST) and Adaptive-Network-based Fuzzy Inference System (ANFIS). It uses ST for electrical signal processing and then extracts features which are used as the inputs of the ANFIS system. ANFIS starts training and learning to obtain classification capabilities and accurately determines the islanding events and outputs the results. The innovation of this method is the combination of ST and ANFIS structure. ST has better time-frequency characteristics and signal processing capability. ANFIS can automatically start tra...