电力电缆是用来输送和分配电能的，是电力网的重要组成部分。一旦电力电缆发生故障必将引起局部甚至全部地区的大面积停电，势必给国民经济和人民生活带来巨大的影响，因此，保证电力电缆的安全运行是保证供电网络可靠运行的先决条件。所以，基于TMS320F2812的数据采集模块与电力电缆故障识别研究具有重要意义。 本论文以DSP和计算机为硬件平台，以小波分析和人工神经网络为理论基础，设计了基于TMS320F2812的数据采集模块，对故障识别方法进行了深入研究，实现了电力电缆典型故障的识别。论文所做的主要工作有： 为了获得电力电缆发生故障时的故障电压或故障电流信号，论文首先设计并完成了基于TMS320F2812的数据采集传输模块。数据采集传输模块的设计包括DSP最小系统、DSP外围扩展电路和外设通信接口电路的原理图与PCB图设计，并详细论述了各个部分的工作原理。然后，基于CCS2.21环境，完成了数据采集传输模块的主程序、DSP初始化程序、中断定时处理程序、A/D中断响应程序及UART传输程序的设计，仔细分析了各程序的工作步骤，给出了各个部分的流程图。最后，对数据采集传输模块的硬件和软件功能进行了测试。实验证明了数据采集传输模块的设计正确性。 针对电力电缆故障识别问题，论文首先研究了人工神经网络和小波分析的相关理论知识，建立了两个典型的电缆故障仿真模型。紧接着，构建了反馈型Elman人工神经网络模型，利用Matlab语言编写了故障识别程序，成功实现了电缆的断线和短路故障的识别。最后，利用小波包分解---能量法提取了电缆故障特征信号，构建了松散型小波神经网络结构，对电力电缆的单相短路接地故障、两相短路故障、三相短路接地故障进行了正确的故障识别和分类。通过两个实例表明，仿真模型有效，两个分类器性能良好。

Power cable is used to transmit and allocate the power energy and it is very important component of power network. It will cause power disruption in local or whole areas once power cable has fault, and then it will bring enormously economic loss for government and the life of people. Therefore, ensuring the normal running of power cable is the precondition to guarantee the normal running of power network. So, they are very important that studying the data acquisition and identifying the fault of power cable. Based on the hardware platform of DSP system & computer and the theory of wavelet analysis & artificial neural network, this paper designs the data acquisition module by using TMS320F2812. It gives a profound study in fault recognition technology. It achieves fault recognition and classification of the typical power cable fault. The contribution of this paper consists of the following parts. In order to obtain the signal of fault voltage or current when power cable has fault, the data acquisition module is firstly designed and realizied by using TMS320F2812. This module includes the schematic circuit diagram, the PCB of the smallest system of DSP, the peripheral extended circuit and the interface circuit of I/O communication. All the working principles of each part are discussed. Secondly, the main programs of the data acquisition module, the DSP initialization, the interrupt timing disposal, the A/D acknowledge interrupt and the UART transmission are accomplished with environment of CCS2.21. The working processes of all programs are analyzed in detail and the flow charts are given. Thirdly, the hardware and software function of data acquisition transmission module are testes. The experiment proves that data acquisition module works very well and the design is correct. To the problem of power cable fault recognition, for one thing, this paper studies the relative theories of artificial neural network and wavelet analysis, constructs two typical simulative models for producing fault signal. For another, this paper builds the feedback artificial neural network module of Elman, writes the program of fault recognition with Matlab, successfully fulfill the recognition of cable open circuit fault and short circuit fault. Finally, the loose module of wavelet neural network is built by using the fault feature signal based on wavelet package decomposing---energy method. The neural networks model can correctly recognize and classify the cable fault patterns among the 1 phase short circuit and grounding fault, 2 phase short circuit, 3 phase short circuit and grounding fault. Two examples show that the simulative model is valid and two classifiers have good performances.