电力系统及其自动化学报2024，Vol.36Issue(4)：1-8,18,9.DOI:10.19635/j.cnki.csu-epsa.001248
基于遗传优化聚类的GRU无损电力监测数据压缩
GRU Neural Network Lossless Compression of Power Monitoring Data Based on Genetic Optimization Clustering
摘要
Abstract
Aimed at the problems of large volume and difficult storage of monitoring data records at power dispatching centers,a gated recurrent unit(GRU)neural network lossless data compression method based on genetic optimization K-means clustering is proposed.First,a distributed cluster is built to cluster the multi-dimensional raw power data into data blocks with a high similarity,in which the genetic algorithm is used to find the best cluster and improve the effect of data clustering.Then,the probability distribution model of data coding is trained by the GRU neural network,and the data is coded and compressed by combining with arithmetic coding.Finally,several power datasets are analyzed as examples to show that the proposed compression algorithm can achieve high proportional compression of data and opti-mize the clustering performance.关键词
电力数据/遗传算法/聚类分析/循环神经网络/分布式集群压缩Key words
power data/genetic algorithm/clustering analysis/recurrent neural network(RNN)/distributed cluster compression分类
信息技术与安全科学引用本文复制引用
屈志坚,帅诚鹏,吴广龙,梁家敏,李迪..基于遗传优化聚类的GRU无损电力监测数据压缩[J].电力系统及其自动化学报,2024,36(4):1-8,18,9.基金项目
江西省自然科学基金重点项目(20232ACB204025) （20232ACB204025）
江西省高层次高技能领军人才培养工程资助项目(202223323) （202223323）
轨道交通基础设施性能监测与保障国家重点实验室开放课题资助项目(HJGZ2022203) （HJGZ2022203）
