With the proliferation of electric

With the proliferation of electric vehicles (EVs), the supportingfacilities and infrastructure become new components inconjunction with conventional electrical appliances. These novelappliances, e.g., charging piles and energy storage devices, bringnew features as well as challenges to the existing power grid. Toenhance the accuracy of mechanical fault identification for on-loadtap changers (OLTCs) in smart grid with EVs, a feature selectionmethod for OLTC mechanical fault identification is proposed inthis paper. This method relies on the multi-feature fusion and thejoint application of the K-nearest neighbors algorithm (KNN) andthe improved whale optimization algorithm (IWOA). By multi-featurefusion, the high-dimensional set of time-domain and frequencydomaincharacteristics as well as energy and composite multi-scale permutation entropy can be constructed. As a result,the maximum correlation minimum redundancy (mRMR) principle can be used to screen the sensitive feature subsets.Finally, IWOA is used to optimize the sensitive feature subsets, and KNN is used to classify the different types of optimalfeature subsets. The experimental results show that the proposed method is at least 8% more accurate than the existingmethods. The high-accuracy nature of the proposed method can accelerate the promotion of EVs and the establishmentof intelligent transportation environments.

0/5000

源语言: -

目标语言: -

结果 (英语) 1: [复制]

复制成功！

With the proliferation of electric vehicles (EVs), the supporting facilities and infrastructure become new components in conjunction with conventional electrical appliances. These novel appliances, eg, charging piles and energy storage devices, bring new features as well as challenges to the existing power grid . To enhance the accuracy of mechanical fault identification for on-load tap changers (OLTCs) in smart grid with EVs, a feature selection method for OLTC mechanical fault identification is proposed in this paper. This method relies on the multi-feature fusion and the joint application of the K-nearest neighbors algorithm (KNN) and the improved whale optimization algorithm (IWOA). By multi-feature fusion, the high-dimensional set of time-domain and frequencydomain characteristics as well as energy and composite multi-scale permutation entropy can be constructed. As a result, the maximum correlation minimum redundancy (mRMR) principle can be used to screen the sensitive feature subsets. Finally, IWOA is used to optimize the sensitive feature subsets, and KNN is used to classify the different types of optimal feature subsets. The experimental results show that the proposed method is at least 8% more accurate than the existing methods. The high -accuracy nature of the proposed method can accelerate the promotion of EVs and the establishment of intelligent transportation environments.

正在翻译中..

结果 (英语) 2:[复制]

复制成功！

With the thes of the spark of electric vehicles (EVs), the supporting facilities and infrastructure es new components in sane with sie eelectrical appliances. These novel Appliances, e.g., charging piles and energy storage devices, bring New features as well as challenges to the existing power grid. To enhance the accuracy of the mon dy'm fault for on-load Tap changers (OLTCs) in smart with grid EVs, a feature selection method for OLTC mon dydd is proposed in this paper. This method team on the multi-feature-fusion and the the the joint application of the K-nearest neighbors (knN) and The improved whale optimization algorithm (IWOA). By multi-feature Fusion, the high-dimensional set of time-domain and frequencydomain Features as well as energy and composite multi-scale pertati entropy can be sged. As a result, The rheolyse lysau sr.resy (mRMR) principle be be used to screen the sensitive sensitive subsets. Finally, IWOA is used to optimize the sensitive sensitive photos, and KNN is used to do the sic feature subsets. The tha oedd sydd sydd sy'n at es 8% more tha ann an oedd the methods. The high-dy-accuracy nature of the proposed method saccelerate the promotion of eVs and the establishment of the intelligent transportation s.

正在翻译中..

结果 (英语) 3:[复制]

复制成功！

随着电动汽车（EV）的普及 设施和基础设施成为 与传统电器连接。这些小说 充电桩和储能装置等电器 新的特点以及对现有电网的挑战。到 提高有载机械故障识别的精度 电动汽车智能电网中的分接开关 文中提出了OLTC机械故障的识别方法 这篇论文。该方法依赖于多特征融合和 K近邻算法（KNN）与 改进的鲸鱼优化算法（IWOA）。按多个特征 融合，时域和频域的高维集合 可以构造特征、能量和复合多尺度置换熵。因此， 最大相关最小冗余（mRMR）原理可用于敏感特征子集的筛选。 最后，利用IWOA对敏感特征子集进行优化，利用KNN对不同类型的最优特征进行分类 特征子集。实验结果表明，该方法比现有的方法精度至少提高8% 方法。该方法的高精度特性可以加速电动汽车的推广和建立 智能交通环境。

正在翻译中..

其它语言

本翻译工具支持: 世界语, 丹麦语, 乌克兰语, 乌兹别克语, 乌尔都语, 亚美尼亚语, 伊博语, 俄语, 保加利亚语, 信德语, 修纳语, 僧伽罗语, 克林贡语, 克罗地亚语, 冰岛语, 加利西亚语, 加泰罗尼亚语, 匈牙利语, 南非祖鲁语, 南非科萨语, 卡纳达语, 卢旺达语, 卢森堡语, 印地语, 印尼巽他语, 印尼爪哇语, 印尼语, 古吉拉特语, 吉尔吉斯语, 哈萨克语, 土库曼语, 土耳其语, 塔吉克语, 塞尔维亚语, 塞索托语, 夏威夷语, 奥利亚语, 威尔士语, 孟加拉语, 宿务语, 尼泊尔语, 巴斯克语, 布尔语(南非荷兰语), 希伯来语, 希腊语, 库尔德语, 弗里西语, 德语, 意大利语, 意第绪语, 拉丁语, 拉脱维亚语, 挪威语, 捷克语, 斯洛伐克语, 斯洛文尼亚语, 斯瓦希里语, 旁遮普语, 日语, 普什图语, 格鲁吉亚语, 毛利语, 法语, 波兰语, 波斯尼亚语, 波斯语, 泰卢固语, 泰米尔语, 泰语, 海地克里奥尔语, 爱尔兰语, 爱沙尼亚语, 瑞典语, 白俄罗斯语, 科西嘉语, 立陶宛语, 简体中文, 索马里语, 繁体中文, 约鲁巴语, 维吾尔语, 缅甸语, 罗马尼亚语, 老挝语, 自动识别, 芬兰语, 苏格兰盖尔语, 苗语, 英语, 荷兰语, 菲律宾语, 萨摩亚语, 葡萄牙语, 蒙古语, 西班牙语, 豪萨语, 越南语, 阿塞拜疆语, 阿姆哈拉语, 阿尔巴尼亚语, 阿拉伯语, 鞑靼语, 韩语, 马其顿语, 马尔加什语, 马拉地语, 马拉雅拉姆语, 马来语, 马耳他语, 高棉语, 齐切瓦语, 等语言的翻译.