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Islanding Detection for Inverter-Based Distributed Generation Using Support Vector Machine Method
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DOI：10.1109/TSG.2014.2338736.png)
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In this paper, a new islanding detection method for single phase inverter-based distributed generation is presented. In the first stage of the proposed method, a parametric technique called autoregressive signal modeling is utilized to extract signal features from voltage and current signals at the point of common coupling with the grid. In the second stage, advanced machine learning technique based on support vector machine, which takes calculated features as inputs is utilized to predict islanding state. The extensive study is performed on the IEEE 13 bus system and feature vectors corresponding to various islanding and nonislanding conditions are used for support vector machine classifier training and testing. Simulation results show that the proposed method can accurately detect system islanding operation mode 50 ms after the event starts. Further, the robustness of the proposed method is analyzed by examining its performances in the systems with multiple distributed generations, and when system loading condition, grid disturbance types, and characteristics are altered.
Keyword:
Autoregressive (AR) signal modeling
inverter-based distributed generation (DG)
islanding detection
smart grid
support vector machine (SVM)
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