摘要
配电网故障后的准确分类可以增强故障维修的针对性,因此针对配电网故障分类的研究对快速恢复供电具有重要意义。提出一种基于二进制蚁群算法(BACA)与模糊神经网络(FNN)的故障分类系统,该系统适用于中性点非有效接地的中低压配电系统。利用经验模态分解(EMD)提取故障后2 ms暂态信号中的高频成分,基于数理统计方法构造了推理系统的特征向量,研究了不同故障工况下的特征向量变化规律。利用二进制蚁群算法对模糊神经网络权值进行优化,克服了其搜索速度慢、易陷入局部极小值的缺点。在ATP-EMTP中根据实际情况构造了计算模型,利用计算结果对推理系统进行训练。测试样本与现场实验结果表明,所提出的故障分类方法的准确度高、适应性强。
Accurate fault classification for distribution network can enhance the pertinence of repair, which is of signi- ficance to the rapid restoration of power supply. We proposed a fault classification technique for neutral non-effectively grounded system using the binary-ant-colony-algorithm（BACA）-based fuzzy neural network（FNN）. The high-frequency component of the 2 ms＇ transient signal after the failure was extracted by using empirical mode decomposition（EMD）. Based on the method of mathematical statistics, the feature vectors of the inference system were structured. Then the change regulation of the feature vectors was researched under different fault conditions. BACA was used to optimize the weights of the FNN, which overcame the shortcomings of slow scouting speed and local minimum. A computational model was structured in ATP-EMTP based on the physical truth, then the inference system was trained with the compu- ting results. The test samples and the results of field experiments showed that the fault classification method proposed in this paper had a high accuracy and good adaptability.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2016年第7期2063-2072,共10页
High Voltage Engineering
基金
国家自然科学基金(51407120)
辽宁高端人才资助项目(LT2015019)~~
