低压电气设备运行状态信号特征检测系统设计
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摘要
传统电气设备运行信号特征检测系统存在并行误码率较高、低功耗检测时间过长等弊端。为解决上述问题,设计新型低压电气设备运行状态信号特征检测系统。理性连接信号处理加速器及特征检测模块,通过模拟电气运行客户端执行条件的方式,完成新型特征检测系统的硬件运行环境搭建。在此基础上,仲裁低压电气设备的运行状态,并根据仲裁结果分配检测信号的特征,为其匹配最为适宜的数据库条件,完成新型特征检测系统的软件运行环境搭建,实现低压电气设备运行状态信号特征检测系统的顺利运行。对比实验结果表明,随着电气设备运行数据总量的增加,并行误码率始终不超过65%,低功耗检测时间的最大值仅能达到30 s左右,与传统系统相比,新型低压电气设备运行状态信号特征检测系统具备更强的实际应用性。
The traditional electrical equipment operation signal characteristic detection system has the disadvantages of high parallel bit error rate,low power consumption detection time is too long. To solve these problems,a new signal feature detection system for low-voltage electrical equipment is designed.Connecting signal processing accelerator and feature detection module rationally,the hardware running environment of the new feature detection system is built by simulating the execution conditions of the electrical operation client. On this basis,the operation status of low-voltage electrical equipment is arbitrated,and the characteristics of detection signals are allocated according to the arbitration results to match the most appropriate database conditions,and the software running environment of the new feature detection system is built to realize the smooth operation of the low-voltage electrical equipment operation status signal feature detection system. The experimental results show that with the increase of the total amount of electrical equipment operation data,the parallel bit error rate does not exceed 65%,and the maximum low-power detection time can only reach about 30 seconds. Compared with the traditional system,the new low-voltage electrical equipment operation status signal feature detection system has a stronger practical application.
The traditional electrical equipment operation signal characteristic detection system has the disadvantages of high parallel bit error rate,low power consumption detection time is too long. To solve these problems,a new signal feature detection system for low-voltage electrical equipment is designed.Connecting signal processing accelerator and feature detection module rationally,the hardware running environment of the new feature detection system is built by simulating the execution conditions of the electrical operation client. On this basis,the operation status of low-voltage electrical equipment is arbitrated,and the characteristics of detection signals are allocated according to the arbitration results to match the most appropriate database conditions,and the software running environment of the new feature detection system is built to realize the smooth operation of the low-voltage electrical equipment operation status signal feature detection system. The experimental results show that with the increase of the total amount of electrical equipment operation data,the parallel bit error rate does not exceed 65%,and the maximum low-power detection time can only reach about 30 seconds. Compared with the traditional system,the new low-voltage electrical equipment operation status signal feature detection system has a stronger practical application.
引文
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[3]王毅,段大程,程昊,等.基于神经网络的低压电力线载波通信信号调制识别研究[J].电测与仪表,2016,53(11):51-55.
[4]刘隆华,黄洪全,黄启哲,等.基于电弧外特性的故障定位信号发生器优化设计[J].电力系统自动化,2016,40(9):100-105.
[5]黄军,张春阳,曹志刚,等.基于双稳态永磁操作机构的智能低压真空断路器设计[J].科学技术与工程,2017,17(6):197-201.
[6]郝文斌,王彬宇,苟瑞,等.基于主成分分析与独立成分分析的热释电红外信号特征提取技术[J].中国测试,2016,42(11):113-118.
[7]欧阳森,李翔,刘平,等.低压脱扣器电子电路电压暂降敏感特性试验研究[J].电力自动化设备,2017,37(2):211-218.
[8]陈海龙,王长龙,左宪章,等.基于金属磁记忆信号垂向特征分析的损伤状态识别[J].仪器仪表学报,2017,38(6):1516-1522.
[9]王子洋,范利锋,王永千,等.基于信号特征分析的植物体表电信号记录模式选择[J].农业工程学报,2018,34(5):137-143.
[10]易术春,王威,苏三庆,等.利用磁记忆信号特征参数表征拉伸应力状态[J].振动.测试与诊断,2017,37(4):667-672.
[11]陈坚,陈红,蔡晓霞,等.基于Myriad预处理和分形盒维数的信号检测方法[J].电子信息对抗技术,2017(6):35-41.
[12]陆兴华,陈锐俊,池坤丹.基于时频分析的人体红外热信号检测算法[J].国外电子测量技术,2016(2):55-58.
[13]班晓东.基于电子侦察的时频分析算法及应用研究[J].电子科学技术,2016(5):630-633.
[14]王佩,仇兆炀,祝俊,等.雷达侦收自适应信号处理架构研究[J].现代雷达,2017(11):39-44,52.
[15]徐伟,余建宇,陈矛.基于多维特征的雷达信号脉内调制识别技术[J].太赫兹科学与电子信息学报,2018(1):81-86.
[16]陈喆,王荣,周文颖,等.非平稳信号度量方法综述[J].数据采集与处理,2017(4):667-683.