摘要
按需压缩是平衡数据在数量和质量方面的有效手段,针对处理SCADA系统中历史负荷数据量大的问题,提出了一种自适应压缩算法,首先通过计算历史数据库中一段时间内负荷数据的平均变化量来判断采样频率的增减,再利用A*算法搜索采样的最优解,自适应的调节采样频率,达到降低处理过程中数据量的同时不丢失信息量的目的。与传统等间隔采样方法的实验结果进行对比,得出了该方法对于负荷数据变化趋势的捕捉能力较强,减轻了网络和信息处理系统的负担,降低了后期数据清洗、负荷预测等工作的时间,是确保电网安全稳定运行的有效途径。
＆quot;On-demand compression＆quot; is an effective method in balancing the data between the quantity and quality, aim to deal with the problem of historical load data in the SCADA system, this paper puts forward a method called ＆quot;adaptive compression algorithm＆quot;. Firstly, calculating the average quantities of load data during the period of history data base, in this way, to figure out the sample frequency. Then using the A＊ algorithm calculation to search the best answer to sample. ＆quot;Adaptive sample frequency＆quot; reaches to the goal of reducing the data quantity as well as keeping the information quantity. Compared with the results of the traditional sample method, the adaptive compression algorithm has the advantage of grasping the tendency of load data change and decreasing the load in internet and information processing system, what＆#39;s more, it also lowers the time consuming on the data process and load prediction, which offers an effective access to the electric net to have a steady condition to operate.
出处
《控制工程》
CSCD
北大核心
2017年第12期2534-2538,共5页
Control Engineering of China
基金
吉林省科技发展计划项目(20140204049GX)
关键词
按需压缩
负荷数据
A＊算法
自适应采样
On-demand compression
load data
A＊ algorithm
adaptive sampling
