摘要
变电站电力设备的可听声信号中包含着丰富的振动、放电等信息,对电力设备声音信号的定位及分析是实现设备运行状态评估及故障诊断的有效手段之一。针对变电站全站设备,实现对设备异常声音信号的快速巡检及定位可有效地提升设备故障检测效率。为此基于非接触式声音传感器阵列,从统计分析的角度出发提出了利用极大似然估计法对全站电力设备声源进行定位的方法,并利用变异的粒子群优化算法降低运算量,实现了低信噪比环境下的设备故障声源高效精确定位。仿真分析及实验室测试表明,所提出的设备异常声源定位方法及系统在低信噪比环境中其定位误差比MUSIC方法降低约30%,可快速、有效地定位设备异常声源,为后续设备故障精确定位及诊断提供参考。
The audible sound signal of power equipment in substation contains abundant information such as vibration and discharge.The localization and analysis of sound signal of power equipment is one of the effective means to achieve the operation state evaluation and fault diagnosis of the equipment.For the whole station equipment of the substation,how to realize fast inspection and localization of abnormal sound signal of equipment can effectively improve the efficiency of equipment fault detection.Based on the non-contact sound sensor array,this paper proposes a method of locating the acoustic source of the electrical equipment of the whole station by using the maximum likelihood estimation method from the perspective of statistical analysis.Moreover,a mutant particle swarm optimization is adopted to reduce the amount of computation,thus the accurate location of the equipment fault acoustic source can be realized in the low Signal-Noise Ratio(SNR)environment.Simulation analysis and laboratory experiments show that the locating accuracy of the proposed method and system is greatly improved compared with the traditional method.The positioning error is reduced by about 30%compared with the MUSIC method under low SNR.The method can be adopted to quickly and effectively locate the abnormal acoustic source of the equipment and provide references for the accurate positioning and diagnosis of subsequent equipment faults.
作者
张瑶
罗林根
王辉
盛戈皞
江秀臣
ZHANG Yao;LUO Lingen;WANG Hui;SHENG Gehao;JIANG Xiuchen(Department of Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2020年第9期3145-3153,共9页
High Voltage Engineering
关键词
电力设备
可听声
最大似然估计
变异粒子群算法
定位
power equipment
audible sound
maximum likelihood estimation
mutant particle swarm optimization algorithm
location
作者简介
张瑶,1997-,女,硕士,主要研究方向为电力设备状态监测,E-mail:zhangyaol@sjtu.edu.cn;通信作者:罗林根,1982-,男,博士,副研究员,主要研究方向为输变电设备状态评估、复杂电力系统脆弱性分析等,E-mail:llg523@sjtu.edu.cn。
