Abstract：Computing performance is one of the key issues existing in the applications of big power data,such as fault diagnosis and prediction.Distributed storage and parallel computing are currently as the effective measures to accelerate the data-intensive applications.This paper describes an open distributed processing service(ODPS)from Ali Cloud,is used to store and accelerate the analytic process of monitoring big data about electrical equipment.Taking the phase resolved partial discharge(PRPD)processing of a partial discharge(PD)signal as example,a method for storing the signal with high sampling rate and time series data,and extracting the feature of the signal through the extended MapReduce model(MR2)of ODPS is proposed in this paper.The paralleled PRPD procedure(ODPS-PRPD)implements amounts of PD signals parallel basic parameters calculation and discharge type recognition,statistics features.To verify the effectiveness of the proposed method,a large number of partial discharge signals of four types from laboratory tests are respectively analyzed on ODPS and Hadoop.Because ODPS-PRPD stores the large amounts of middle data in the primary memory,its computing procedure is much faster.The results show that ODPS-PRPD has obviously better performance in data reliabltity,service anailabilty and cost than that of Hadoop.
朱永利, 李莉, 宋亚奇, 王刘旺. ODPS平台下的电力设备监测大数据存储与并行处理方法[J]. 电工技术学报, 2017, 32(9): 199-210.
Zhu Yongli, Li Li, Song Yaqi, Wang Liuwang. Storage and Parallel Processing of Big Data of Power Equipment Condition Monitoring on ODPS Platform. Transactions of China Electrotechnical Society, 2017, 32(9): 199-210.
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2017, Vol. 32 
