Abstract
As the most important daily management work in power application, electrical energy metering is carried out throughout the whole process of power production, transmission and use. Ensuring the accurate and reliable operation of electric energy measurement is the key to establish a fair, just and orderly electric power marketing market. At present, there are many problems in the inspection process of electric power measurement device, which include large workload, long verification time, sampling accuracy and algorithm performance constraints. In this paper, the remote on-line diagnosis of electrical energy metering decice is studied. Based on a large amount of sampling datas of power measurement, voltage and current, it introduces the parallel computing in data mining. Based on support vector machine, data and task parallelization fault diagnosis modelsare established,the online real-time monitoring and failure warning for electric power measurement device is realized, which contains abnormal characteristics and fault state. The visualization method is used to upload the device failure pictures in time and the diversified fault early warning technology is adopted, in order that the staff can timely analyze the electrical energy metering device in the fault or abnormal condition. In this way, the remote state monitoring and operation management level of the electrical energy metering device are improved.
Published Version
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