This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naive Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.

中文翻译：

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基于 K 近邻和朴素贝叶斯的谐波源识别诊断分析

本文提出了机器学习 (ML) 算法在识别和诊断电力系统中的谐波源方面的比较，称为 k 最近邻 (KNN) 和朴素贝叶斯 (NB)。该方法采用单点测量，使用S变换对测量信号进行分析并提取为电压和电流参数。根据 S 变换分析的时频表示 (TFR) 估计的电压和电流特征用作 ML 的输入。考虑了谐波源定位的四种重要情况，而在诊断过程中使用了谐波电压 (HV) 和谐波电流 (HC) 源类型-负载。为了确定最佳 ML，所提出方法的性能测量包括准确度、精密度、特异性、灵敏度、和 F-measure 计算。所提出方法的充分性在 IEEE 4-bust test feeder 上进行了测试和验证，每个 ML 算法执行 10 次，以防止任何过拟合结果。