Modified Differential Golomb Arithmetic Lossless Compression Algorithm for Smart Grid Applications

Ahmed Aleshinloye, Abdul Bais
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引用次数: 2

Abstract

The advancement of the electric grid has led to tremendous growth in data generated from the installed sensors. Efficient storage and transmission of this data pose a challenge for the utilities. Thus, it is required to have a data compression technique to reduce the data size. There are state of the art compression algorithms that can be applied to reduce the amount of data for storage and transmission in the smart grid environment. Some of these algorithms exploit characteristics of the load profile data, where consecutive data samples have very small differences. However, performance of these algorithms deteriorate when there are frequent large differences. We propose a modification that improves compression performance when there are large value differences. The algorithm is evaluated on smart meter load profile data at different data resolution. We show that the proposed changes improve performance by 2−20% for different resolutions.
应用于智能电网的改进差分Golomb算法无损压缩
电网的进步导致安装的传感器产生的数据急剧增长。有效存储和传输这些数据对公用事业公司提出了挑战。因此,需要一种数据压缩技术来减小数据的大小。有一些最先进的压缩算法可以用于减少智能电网环境中存储和传输的数据量。其中一些算法利用负载剖面数据的特征,其中连续数据样本具有非常小的差异。然而,当存在频繁的较大差异时,这些算法的性能会下降。我们提出了一种改进方法,可以在存在较大值差异时提高压缩性能。在不同数据分辨率的智能电表负荷剖面数据上对该算法进行了评估。我们表明,在不同的分辨率下,所提出的更改可将性能提高2 - 20%。
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