实时电能质量波形识别与可编程数字信号处理器

M. Wang, G. Rowe, A. Mamishev
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引用次数: 4

摘要

电能质量(PQ)监测是电力公司和许多工业用电用户面临的一个重要问题。本文提出了一种基于dsp的基于PQ分类算法的硬件监控系统。该算法采用德州仪器(TI) TMS320VC5416数字信号处理器(DSP)和TI THS1206 12位6 MSPS模数转换器实现。采用TI TMS320VC5416 DSP starter kit (DSK)作为主机板,THS1206安装在子卡上。所实现的PQ分类算法由特征提取和分类两个过程组成。特征提取将PQ信号投影到时频表示(TFR)上,该表示旨在最大化类之间的可分离性。分类器包括heaviside函数线性分类器和具有前馈结构的神经网络。根据DSP的结构对算法进行了优化,以满足电力系统中60hz正弦电压/电流信号的5周期分段分类的硬实时性约束。分类输出可以串行传输到操作员界面或控制机制,用于记录和解决问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time power quality waveform recognition with a programmable digital signal processor
Power quality (PQ) monitoring is an important issue to electric utilities and many industrial power customers. This paper presents a DSP-based hardware monitoring system based on a recently proposed PQ classification algorithm. The algorithm is implemented with a Texas Instruments (TI) TMS320VC5416 digital signal processor (DSP) with the TI THS1206 12-bit 6 MSPS analog to digital converter. A TI TMS320VC5416 DSP starter kit (DSK) is used as the host board with the THS1206 mounted on a daughter card. The implemented PQ classification algorithm is composed of two processes: feature extraction and classification. The feature extraction projects a PQ signal onto a time-frequency representation (TFR), which is designed for maximizing the separability between classes. The classifiers include a Heaviside-function linear classifier and neural networks with feedforward structures. The algorithm is optimized according to the architecture of the DSP to meet the hard realtime constraints of classifying a 5-cycle segment of the 60 Hz sinusoidal voltage/current signals in power systems. The classification output can be transmitted serially to an operator interface or control mechanism for logging and issue resolution.
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