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Data-Mining Model Based Intelligent Differential Microgrid Protection Scheme
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DOI：10.1109/JSYST.2014.2380432.png)
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Abstract
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This paper presents a data-mining-based intelligent differential protection scheme for the microgrid. The proposed scheme preprocesses the faulted current and voltage signals using discrete Fourier transform and estimates the most affected sensitive features at both ends of the respective feeder. Furthermore, differential features are computed from the corresponding features at both ends of the feeder and are used to build the decision tree-based data-mining model for registering the final relaying decision. The proposed scheme is extensively validated for fault situations in the standard IEC microgrid model with wide variations in operating parameters for radial and mesh topology in grid-connected and islanded modes of operation. The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.
Keywords:
Decision tree (DT)
discrete Fourier transform (DFT) preprocessor
distributed generations (DGs)
fault detection
microgrid protection
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