In this paper, through the combinat

In this paper, through the combination of multi-feature fusion and IGWO-KNN algorithm, Multi-Feature Fusion Based Mechanical Fault Diagnosis for On-Load Tap Changers in Smart Grid with Electric Vehicles is proposed. The method is novel to some extent, and the experimental results are promising, But there are still many detailed problems in this manuscript should be solved before publication <br>1 Please try to supplement some relevant literature on weight Inertia weight and important WOA variants. <br>2. This paper puts forward the application of OLTC in the EV smart grid, but when OLTC fault affects EV smart grid, please further elaborate in the introduction.<br>3. In Figure 12 and figure 13, please supplement the feature selection calculation results of whale algorithm improved by inertia weight sum or reverse learning algorithm alone. <br>4. Whether the method can be applied to other faults except contact faults, such as whether the spring can also be used when the spring is loosening, and it is recommended to supplement if conditions permit.

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将多特征融合与IGWO-KNN算法相结合，提出了基于多特征融合的电动汽车智能电网有载分接开关机械故障诊断方法。该方法在一定程度上是新颖的，实验结果是有希望的，但该手稿中仍有许多细节问题需要在出版前解决<br>1请尝试补充一些关于体重惯性体重和重要的WOA变异的相关文献。<br>2。本文提出了OLTC在电动汽车智能电网中的应用，但当OLTC故障影响电动汽车智能电网时，请在介绍中进一步阐述。<br>三。在图12和图13中，请补充单独使用惯性权和或反向学习算法改进的whale算法的特征选择计算结果。<br>四。该方法是否适用于除接触故障外的其它故障，如弹簧松动时是否也能使用，有条件时建议补充。<br>