滑流:高性能无损压缩流同步波形监测数据

S. Blair, Jason J. A. Costello
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引用次数: 3

摘要

电网因脱碳而发生的根本性变化需要先进的监测和自动化分析。从电压和电流传感器捕获同步波形数据,有时被称为连续波点(CPOW)监测,提供了除相量测量单元(pmu)的同步相量之外的其他功能。然而,波形处理、传输和存储的明显缺点是数据带宽和存储要求高。因此,对流同步波形数据的访问通常仅限于变电站局域网(LANs)。本文报道了一个解决这些问题的平台,从而以一种方便实用的方式提供广域波形监测。展示了为流波形数据设计的无损数据压缩方法如何显著降低数据带宽要求并提高端到端效率和延迟。数据带宽需求可以降低到原来的5-15%。同样的方法可以应用于实时流和离线数据存储,与其他行业格式(如COMTRADE和PQDIF)相比,文件大小更小。它支持任意采样率,每条消息的任意采样数量,以及要发送的测量量的任意配置。该方案的一个实施方案,称为Slipstream,已经开源,以使行业采用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Slipstream: High-Performance Lossless Compression for Streaming Synchronized Waveform Monitoring Data
Fundamental changes in power grids due to decarbonization require advanced monitoring and automated analysis. Capturing synchronized waveform data from voltage and current sensors, sometimes referred to Continuous Point on Wave (CPOW) monitoring, offers several capabilities beyond synchrophasors from Phasor Measurement Units (PMUs). However, the obvious drawbacks in manipulating, transferring, and storing waveform are the high data bandwidth and storage requirements. Therefore, access to streaming synchronized waveform data is typically restricted to substation local area networks (LANs). This paper reports on a platform to address these issues and therefore to deliver wide-area waveform monitoring in a way which is convenient and practical. It is shown how a lossless data compression method designed for streaming waveform data can significantly reduce data bandwidth requirements and improve end-to-end efficiency and latency. Data bandwidth requirements can be reduced to 5-15% of the original size. The same approach can be applied to both real-time streaming and offline data storage, with reduced file size compared to other industry formats such as COMTRADE and PQDIF. It supports any sampling rate, any number of samples per message, and arbitrary configurations of measurement quantities to be sent. An implementation of the scheme, called Slipstream, has been open sourced to enable industry adoption.
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