应用于智能电网故障检测的关联规则挖掘算法优化

摘要

针对于目前故障检测方法在智能电网应用中存在较大误差的问题，介绍了一种基于贝叶斯网络和关联规则数据挖掘的算法模型，通过Hash技术优化Apriori算法，对原数据挖掘，去除不期望的候选项集，并通过贝叶斯网络训练样本，减少检测误差，最终得到电网故障检测结果。仿真结果表明，这种基于贝叶斯网络和关联规则挖掘算法的故障检测模型，比传统算法在电网故障检测方面更有效率，且检测误差大幅降低。%Aiming at the problem that larger error always exists during the application of fault detection test method in smart grid, this paper introduced an algorithm model based on Bayesian network and association rule mining. With mining the original data and removing the undesired candidate, Apriori algorithm was optimized by Hash technology; also Bayesian network was introduced for sample training to decrease detection error, so as to finally obtain the result of power network fault detection. Simulation results show that compared with traditional algorithm, the proposed fault detection model, which is based on Bayesian network and association rules mining, is more efficient with lower detection error in power grid fault detection.