基于小波变换和分形理论的电能质量扰动监控系统

A Power Quality Disturbance Monitoring System Based on Wavelet Transform and Fractal Theory

介绍了实时电能质量扰动监控系统的结构,详细说明了该系统硬件和软件各个构成模块的工作原理。为实现实时在线监控电能质量扰动,首先需检测出扰动信号,然后进行分析处理。在扰动检测模块中,采用自适应线性神经元实现了对各种扰动的检测,将检测出的扰动信号送入分类模块,采用离散小波多分辨率分析提取不同尺度下的能量分布特征,同时采用分形几何学提取局部方差维数,将二者结合共同构成扰动信号的特征矢量。将提取的特征矢量送入概率神经网络实现网络训练和扰动分类。通过模拟数据测试,该系统的分类率可达到90%。另外,该系统是在CAN总线变电站自动化系统上实现的,通过调整数据的传输格式也可将其应用到其它传输平台的变电站,实现对电能质量扰动的监控。

The structure of a real-time power quality disturbance monitoring system is presented and the working principle of the modules constituting hardware and software of this system are described in detail. To realize online power quality disturbance, at first the disturbance signal should be detected then analyzed and dealt with. In disturbance detection module, the detection of various disturbances is implemented by adaptive linear neuron and the detected disturbance signals are sent into classification module; by use of discrete wavelet multi-resolution analysis and Parseval＇s theorem the energy distribution features under different scales are extracted, meanwhile by use of fractal geometry the local variance dimensions are extracted; combining these two items, the characteristic vector of disturbance signal is composed. Sending the composed characteristic vector into probabilistic neural network （PNN） the network training and disturbance classification are realized. Results of simulated data tests show that the classification rate of the proposed system can achieve 90%. In addition, the proposed system is implemented in substation automation system using CAN field-bus, by means of changing the data transmission format the proposed system can be applied to substations adopting other transmission platforms to realize the monitoring of power quality disturbance.