Abstract
ABSTRACT Detection and classification of power system instabilities due to the raised transient effect during switching operation of different kinds of load, capacitor bank, large sized induction motor is challenging in microgrid network because of inconstant direction of current and complexity of network. However, this classification study is a mandatory measure to maintain quality power in modern microgrid network. A novel decision tree (DT) classification based machine learning technique is proposed in this study to do this significant task. The test transient signals during several switching operation have been extracted from a real microgrid network in India. The statistical attributes are computed from the coefficients of discrete wavelet transform (DWT) of transient voltage signals. The significant features are selected according to the performance of the processed DWT. The fine, medium, and coarse DT classifiers considering different number of splits and ensembled tree based on bagging have been trained in the next stage of algorithm. The performances of each classifier are assessed in terms of confusion matrix and receiver operating characteristics curve. The accuracy and training time of each classifier are compared to recommend a specific method to detect and classify the similar type of transient events. A 99.4% accuracy has been achieved with a very lower detection time from fine DT.
Published Version
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