基于遗传算法优化支持向量机的电能质量暂态扰动识别新方法 |
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引用本文: | 林琳,彭华,戚佳金,黄南天. 基于遗传算法优化支持向量机的电能质量暂态扰动识别新方法[J]. 水电能源科学, 2016, 34(11): 200-203 |
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作者姓名: | 林琳 彭华 戚佳金 黄南天 |
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作者单位: | 1. 吉林化工学院 信息与控制工程学院, 吉林 吉林 132022; 2. 东北电力大学 电气工程学院,吉林 吉林 132012; 3. 国网杭州供电公司, 浙江 杭州 310009 |
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基金项目: | 国家自然科学基金项目(51307020);2016年吉林省科技发展计划项目(20160411003XH);吉林省社科基金项目(2015A2);吉林省教育厅“十三五”科技项目(吉教科合字[2016]第90号) |
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摘 要: | 针对电能质量识别领域中,采用随机参数的支持向量机(SVM)分类器识别随机暂态扰动信号准确率低、优化耗时长等问题,提出一种基于遗传算法(GA)优化SVM识别电能质量暂态扰动(PQD)的新方法(GASVM)。首先,仿真生成具有随机噪声水平和扰动参数的9种PQD信号;接着,通过S变换,提取出6种信号特征构成输入特征向量,用于训练SVM分类器;再采用GA对SVM进行参数寻优,进而获得优化的GA-SVM分类器;最后,采用GA-SVM识别PQD信号。仿真对比试验表明,新方法能准确识别不同噪声环境下的9种PQD信号,分类准确率及优化所需时间均优于PSO优化SVM方法(PSO-SVM)。
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关 键 词: | 电能质量; 随机噪声; S变换; GA; SVM; 参数优化; 扰动识别 |
New Method of Transient Power Quality Disturbances Recognition Using SVM Optimized by Genetic Algorithm |
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Abstract: | SVM classifier has low accuracy and long time consuming for identifying random disturbance signals in the field of power quality recognition. A new method of transient power quality identification is proposed based on the SVM optimized by genetic algorithm. Firstly, 9 type of PQD signals with random noise level and perturbation parameters are generated by simulation. Then, 6 type of signal features extracted through the S transform are constituted as the input vectors, which are used to train the SVM classifier. Furthermore, GA is applied to optimize the parameters of SVM and the optimized GA-SVM classifier is obtained. Finally, GA-SVM model is applied to identify PQD signals. The simulation experiments show that the new method is able to accurately identify 9 type of PQD signals in different noise environments. The classification accuracy and the optimization time are better than that of the PSO-SVM method. |
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Keywords: | power quality random noise S transform genetic algorithm support vector machine parameter optimization disturbance identification |
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