基于TLSmote-SVM的非均衡用户窃漏电诊断算法

针对支持向量机（support vector machines, SVM）检测异常用电用户精度受样本非均衡性和核函数选择影响的问题, 提出一种基于TLSmote-SVM（tomekLink-smote-SVM）的窃漏电诊断模型. 首先基于用电用户数据分布, 利用Smote方法扩充少数类样本, 同时采用Tomek-link剔除噪声; 然后对用户用电特征指标降维后优选SVM核函数; 最后将该算法应用于非均衡用户窃漏电诊断实验, 并与传统SVM和Smote-SVM进行对比, 实验结果表明, 该算法可显著提高窃漏电用户的检测精度.

Diagnosis Algorithm of Theft and Leakage of Electricity for Unbalanced Users Based on TLSmote-SVM

Aiming at the problem that the accuracy of the support vector machines （SVM） in detecting abnormal users of electricity was affected by the imbala nce of the samples and the selection of kernel function, we proposed a diagnosis model of theft and leakage of electricity based on Smote-SVM. Firstly, based on the distribution of users of electricity, the Smote method was used to expand a few samples, and the TomekLink was used to eliminate noise. Secondly, the SVM kernel function was optimized after dimensionality reduction. Finally, the algorithm was applied to the diagnosis experiment of the theft and leakage of electricity for unbalanced users, and compared with the traditional SVM and Smote-SVM algorithms. The experimental results show that the algorithm can significantly improve the detection accuracy of the users of theft and leakage of electricity.