| Autor: |
Caiyan Pei, Shuai Zhang, Xiaoning Zeng |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Energy Reports, Vol 8, Iss , Pp 1392-1404 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2352-4847 |
| DOI: |
10.1016/j.egyr.2022.01.224 |
| Popis: |
In the era of big data, the network data of power system is more and more complex. Due to the limitation of data storage and processing capacity, the abnormal data detection of power grid terminal information system has the problems of low accuracy and high false alarm rate. The original machine learning algorithm with good detection effect is limited by the processing capacity and storage space of the traditional platform, and the detection effect and efficiency are significantly reduced. This paper takes improving the detection accuracy of abnormal data as the main research target, and designs an abnormal data behavior analysis program based on the Internet of Things under the Spark framework combined with improved Support Vector Machine (SVM) and random forest algorithm. The parallel SA_SVM_RF anomaly data behavior detection model based on Spark is mainly studied and applied to real-time detection. Combined with the respective advantages of Internet of Things technology and machine learning in anomaly data detection, the detection capability and rate of power grid anomaly data detection model are further improved. Experimental tests show that the proposed program is superior to traditional methods in data anomaly detection efficiency and quality, and has certain research significance in the field of power grid security. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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