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作 者:余南华[1] 李兰芳[1] 王玲[1] 杨洪耕[2] 谭丹[2]
机构地区:[1]广东电网公司电力科学研究院,广东省广州市510080 [2]四川大学电气信息学院,四川省成都市610065
出 处:《中国电机工程学报》2014年第25期4395-4401,共7页Proceedings of the CSEE
基 金:国家高技术研究发展计划(863计划)(2012AA050212)~~
摘 要:电力系统谐波监测是电能质量监测系统的主要工作内容,谐波电流检测能为分析设备运行状态提供依据,提出了一种基于云模型检测谐波电流是否异常的方法。根据正常运行条件下谐波电流的日95%概率值和日最大值分别建立正态云模型,利用云模型的熵来衡量正常运行方式时谐波电流的波动范围,根据正态云外隶属曲线的'3nE外边界确定谐波电流的异常阈值。同时提出了谐波电流异常检测的修正算法及简化公式。将需检测的谐波电流与谐波异常阈值进行比较,可实现谐波电流的异常检测;基于云模型确定谐波电流异常阈值可以克服基于总体样本方法确定的异常阈值受样本随机不确定性影响较大的问题,同时考虑了正常数据的波动问题,确定出的异常阈值更符合客观实际,不易造成正常数据的误判。实际工程算例验证了该方法的正确性,同时说明了总体样本方法的不足。Monitoring harmonics of power system are the main tasks of power quality monitoring system, and the harmonic current detection can provide the basis for the analysis of equipment operating status, so a method of abnormal detection for harmonic current based on cloud model was proposed. The normal cloud models were constructed by the daily 95% probability value and daily maximum of harmonic current under the normal operating conditions respectively. The entropy of the cloud model was applied to measure the normal range of the harmonic current, and the abnormal threshold was determined by the normal cloud outside belonging curve's 3E'n boundary~ At the same time, the revised algorithm and simplified formulas for abnormal detection of harmonic current were proposed. Abnormal detection of harmonic current can be implemented by comparing the harmonic current needing to be detected with abnormal threshold. Determining the abnormal threshold value of harmonic current based on cloud model can overcome the problem that the abnormal threshold determined by the general sample method is influenced by sample random uncertainty problem. Also, this method can take into account the fluctuations of normal data, thus the abnormal threshold is more in line with the objective reality, and it is not easy to cause misjudgment of the normal data. Practical engineering examples prove the method is correct and the disadvantages of the general sample method are also illustrated.
关 键 词:云模型 谐波电流 异常检测 异常阈值 总体样本方法
分 类 号:TM73[电气工程—电力系统及自动化]
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