随着汽车智能电网和智能交通的逐步合并,其安全性和稳定性逐渐受到关注,这对现有电力设备的维护和保障提出了挑战。因此本文提出一种多特征融合的电动的英语翻译

随着汽车智能电网和智能交通的逐步合并,其安全性和稳定性逐渐受到关注,这

随着汽车智能电网和智能交通的逐步合并,其安全性和稳定性逐渐受到关注,这对现有电力设备的维护和保障提出了挑战。因此本文提出一种多特征融合的电动汽车智能电网OLTC故障特征选择。该方法主要是依赖于多特征融合和改进的灰狼学习算法优化核极限学习机的特征选择法。首先,针对传统OLTC故障特征较为单一和诊断精度较低的缺陷,本文将多尺度加权列熵和能量相关系数进行特征融合构建高维特征;然后采用核主成分分析对初始矩阵进行筛选,形成初始子集;最后采用IGWO对核极限学习机优化的同时进行特征选择。试验结果证明了该方法的有效性,可以加速汽车智能电网和智能交通的合并。
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结果 (英语) 1: [复制]
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With the gradual merger of automotive smart grids and smart transportation, their safety and stability have gradually received attention, which poses challenges to the maintenance and guarantee of existing power equipment. Therefore, this paper proposes a multi-feature fusion electric vehicle smart grid OLTC fault feature selection. This method is mainly based on the feature selection method of multi-feature fusion and improved gray wolf learning algorithm optimization kernel extreme learning machine. First of all, for the defects of traditional OLTC with single fault features and low diagnostic accuracy, this paper will use multi-scale weighted column entropy and energy correlation coefficients to perform feature fusion to construct high-dimensional features; Subset; Finally, IGWO is used to optimize the core extreme learning machine while performing feature selection. The test results prove the effectiveness of this method, which can accelerate the merger of smart grid and intelligent transportation of automobiles.
正在翻译中..
结果 (英语) 2:[复制]
复制成功!
With the gradual merger of automotive smart grid and intelligent transportation, the safety and stability of automobile sitcoms have been paid attention to, which poses a challenge to the maintenance and security of existing power equipment. Therefore, this paper presents a multi-feature fusion of electric vehicle smart grid OLTC fault characteristics selection. This method mainly relies on multi-feature fusion and improved gray wolf learning algorithm to optimize the characteristic selection method of the nuclear limit learning machine. Firstly, in view of the defects of the traditional OLTC fault characteristics which are relatively single and the diagnostic accuracy is low, this paper combines multi-scale weighted column entropy and energy correlation coefficients to construct high-dimensional features, then uses the analysis of nuclear main components to filter the initial matrix to form an initial subset, and finally uses IGWO to optimize the nuclear limit learning machine while selecting features. The test results prove the effectiveness of this method and can accelerate the integration of smart grid and intelligent transportation.
正在翻译中..
结果 (英语) 3:[复制]
复制成功!
With the gradual integration of smart grid and intelligent transportation, the security and stability of smart grid are gradually concerned, which challenges the maintenance and guarantee of existing power equipment. Therefore, this paper proposes a multi feature fusion feature selection for OLTC fault of EV smart grid. This method mainly relies on multi feature fusion and improved gray wolf learning algorithm to optimize the feature selection method of kernel limit learning machine. Firstly, aiming at the defect of single fault feature and low diagnosis accuracy of traditional OLTC, this paper combines multi-scale weighted column entropy and energy correlation coefficient to build high-dimensional features; then, kernel principal component analysis is used to screen the initial matrix to form the initial subset; finally, igwo is used to optimize the kernel limit learning machine and select features at the same time. The experimental results show that the method is effective and can speed up the combination of smart grid and intelligent transportation.<br>
正在翻译中..
 
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