|
|
Mechanical Characteristic State Evaluation of Vacuum Circuit Breaker based on Multi-sensor Signal Fusion |
Zhou Yang, Wang Baohua |
Automation College, Nanjing University of Science and Technology, Nanjing 210094 |
|
|
Abstract The mechanical properties status of vacuum circuit breakers was usually evaluated by single sensor signal. To solve this problem, an evaluation method was proposed in this paper. This method used D-S evidence theory to combine multi sensor signals. Firstly, we got operating parameters which were measured by the displacement sensor. Then we calculated basic belief assignment of mechanical properties according to fuzzy comprehensive evaluation method. Secondly, we used acceleration sensor to monitor vibration signals. According to the method of wavelet packet-energy spectrum, vibration signals were decomposed and extracted characteristic values. Then based on the similarity principle, the characteristic values were used to determine basic belief assignment of mechanical properties status. Finally, the two basic belief assignments are fused according to D-S evidence theory. And the final fusion evaluation results are obtained. The test results showed the method was correct and effective.
|
Published: 22 June 2016
|
|
|
|
Cite this article: |
Zhou Yang,Wang Baohua. Mechanical Characteristic State Evaluation of Vacuum Circuit Breaker based on Multi-sensor Signal Fusion[J]. Electrical Engineering, 2016, 17(6): 30-25.
|
|
|
|
URL: |
http://dqjs.cesmedia.cn/EN/Y2016/V17/I6/30
|
[1] 张弛. 高压断路器在线监测与故障诊断系统研究[D]. 北京: 北京交通大学, 2007. [2] 陈伟根, 李伟, 陈新岗, 等. SF 6 高压断路器状态分析的模糊综合评判方法[J]. 高压电器, 2004, 40(5): 361-363. [3] 陈伟根, 吴娅, 刘强. 基于突变理论的断路器运行状态模糊综合评判方法[J]. 高压电器, 2007, 43(2): 127-130, 135. [4] 国连玉, 李可军, 梁永亮, 等. 基于灰色模糊综合评判的高压断路器状态评估[J]. 电力自动化设备, 2014, 34(11): 161-167. [5] 陈伟根, 魏延芹, 廖瑞金. 高压断路器运行状态的变权模糊综合评判方法[J]. 高压电器, 2009, 45(3): 73-77. [6] 李海英, 冯冬, 宋建成. 中压真空断路器的模糊— 证据理论在线状态评估模型[J]. 高压电器, 2013, 49(1): 40-45. [7] 韩富春, 张海龙. 高压断路器运行状态的多级模糊综合评估[J]. 电力系统保护与控制, 2009, 37(17): 60-64. [8] 李斌. 永磁机构真空断路器运动特性状态评估方法研究[D]. 沈阳: 沈阳工业大学, 2013. [9] 陈伟根, 范海炉, 王有元, 等. 基于小波能量与神经网络的断路器振动信号识别方法[J]. 电力自动化设备, 2008, 28(2): 29-32. [10] 孙来军, 胡晓光, 纪延超. 改进的小波包-特征熵在高压断路器故障诊断中的应用[J]. 中国电机工程学报, 2007, 27(12): 103-108. [11] 徐建源, 张彬, 林莘, 等. 能谱熵向量法及粒子群优化的RBF神经网络在高压断路器机械故障诊断中的应用[J]. 高电压技术, 2012, 38(6): 1299-1306. [12] 宋锦刚. 基于振动信号小波包提取和相似性原则的高压开关设备振动监测[J]. 电网技术, 2010, 34(4): 189-193. [13] 苗红霞, 王宏华. 基于数据融合的高压断路器故障诊断方法研究[J]. 工矿自动化, 2010, 10(10): 45-48. [14] 程磊, 李正瀛, 尹小根, 等. D-S证据理论在断路器故障诊断中的应用[J]. 高压电器, 2003, 39(3): 48-50, 56. |
|
|
|