中文摘要：

针对变压器差动保护装置易受励磁涌流误动作问题,提出了基于EMD-SVD-KELM与参数优选的励磁涌流辨识方法。首先,以经验模态分解（EMD）和奇异值分解（SVD）为工具,对励磁涌流和故障电流信号进行预处理,提取出识别特征量,并作为后续核函数极限学习机（KELM）学习输入量;然后,因学习机性能受参数C和γ影响较大,以均分训练样本所得多个模型的平均准确率作为适应度评价函数,为KELM参数优选提供评价标准。通过EMTDC仿真计算生成训练样本和测试样本,利用多种优化算法对KELM进行训练和测试。最终,试验结果表明,与网格搜索（GS）、遗传算法（GA）、粒子群算法（PSO）相比,改进粒子群算法（IPSO）能够更加迅捷地搜索到最优参数,同时学习后的IPSO-KELM模型能够正确识别涌流和故障电流,验证了文中所提方法的正确性和可行性。

英文摘要：

Aiming at the issue that transformer differential protection devices are easily making a fault operation by inrush current, an identification technique of inrush current based on EMD-SVD-KELM and parameter optimization is presented. Firstly, with the help of empirical mode decomposition（EMD） and singular value decomposition（SVD）, inrush current and fault current signals are preprocessed to extract feature quantities for identification. These feature quantities can be as learning inputs of the following extreme learning machine with kernel（KELM）. Then, parameter C and γ have a greater impact on learning machine performance, so average accuracy rate of multiple models resulting from average training samples can be fitness function to provide evaluation criteria for the parameter optimization of KELM. EMTDC is used to generate the training samples and testing samples. Simultaneously, KELM is trained and tested by various optimization algorithms. Finally experimental results show that comparing with grid search（GS）,genetic algorithm（GA） and particle swarm optimization（PSO）, improved particle swarm optimization（IPSO）is better to quickly search optimal parameters. Meanwhile the trained IPSO-KELM model can properly identify inrush current and fault current, which verify correctness and feasibility of the proposed method in this paper.