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A rule-based S-Transform and AdaBoost based approach for power quality assessment

机译：基于规则的S-Transform和AdaBoost基于方法的电能质量评估

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This paper proposes a new technique for power quality assessment, using rule-based S-Transform (ST) as a feature extraction tool and Adaptive Boost (AdaBoost) as a classifier. Since detection capability of ST highly depends on the nature of Gaussian window, a simple rule-base is created for selecting a suitable window under various nonstationary signal conditions. Keeping a reasonable time-frequency localization of the disturbance signals in view, the rule-base is prepared using statistical based entropy measure. In classification stage, decision stumps are used as weak classifiers and the strong classifier is constructed as a linear combination of weak classifiers in the AdaBoost algorithm. The performance of the proposed methodology is further improved using adaptable initial weights and a simple strategy to avoid overfitting of classifier. The efficacy of the proposed method is demonstrated using synthetic as well as Real Time Digital Simulator test data. The results reveal that the proposed rule-based ST and AdaBoost based method performs better than the other methods viz., SVM and Decision Tree (DT), under varied noise conditions as well as under varied amount of data used for training. (C) 2016 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的电能质量评估技术，它使用基于规则的S变换（ST）作为特征提取工具，并使用自适应Boost（AdaBoost）作为分类器。由于ST的检测能力高度取决于高斯窗口的性质，因此创建了一个简单的规则库，用于在各种非平稳信号条件下选择合适的窗口。考虑到干扰信号的合理时频定位，使用基于统计的熵测度来准备规则库。在分类阶段，在AdaBoost算法中，决策树桩用作弱分类器，而强分类器则构造为弱分类器的线性组合。通过使用自适应初始权重和避免分类器过度拟合的简单策略，可以进一步提高所提出方法的性能。使用合成以及实时数字仿真器测试数据证明了该方法的有效性。结果表明，所提出的基于规则的ST和基于AdaBoost的方法在变化的噪声条件下以及在用于训练的数据量不同的情况下，其性能优于其他方法，即SVM和决策树（DT）。 （C）2016 Elsevier B.V.保留所有权利。

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