Research on anomaly detection of wireless data acquisition in power system based on spark
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 lea...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-07-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722002244 |
| _version_ | 1798037638052052992 |
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| author | Caiyan Pei Shuai Zhang Xiaoning Zeng |
| author_facet | Caiyan Pei Shuai Zhang Xiaoning Zeng |
| author_sort | Caiyan Pei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:29:15Z |
| format | Article |
| id | doaj.art-398c166417a1426cbd1ba656e4084227 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-11T21:29:15Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-398c166417a1426cbd1ba656e40842272022-12-22T04:02:15ZengElsevierEnergy Reports2352-48472022-07-01813921404Research on anomaly detection of wireless data acquisition in power system based on sparkCaiyan Pei0Shuai Zhang1Xiaoning Zeng2School of Mathematics and Information Science and Technology, Hebei Normal University of Science & Technology, Qinhuangdao, 066004, ChinaCorresponding author.; School of Mathematics and Information Science and Technology, Hebei Normal University of Science & Technology, Qinhuangdao, 066004, ChinaSchool of Mathematics and Information Science and Technology, Hebei Normal University of Science & Technology, Qinhuangdao, 066004, ChinaIn 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.http://www.sciencedirect.com/science/article/pii/S2352484722002244Internet of ThingsWireless data acquisitionData anomalySpatial vector model |
| spellingShingle | Caiyan Pei Shuai Zhang Xiaoning Zeng Research on anomaly detection of wireless data acquisition in power system based on spark Energy Reports Internet of Things Wireless data acquisition Data anomaly Spatial vector model |
| title | Research on anomaly detection of wireless data acquisition in power system based on spark |
| title_full | Research on anomaly detection of wireless data acquisition in power system based on spark |
| title_fullStr | Research on anomaly detection of wireless data acquisition in power system based on spark |
| title_full_unstemmed | Research on anomaly detection of wireless data acquisition in power system based on spark |
| title_short | Research on anomaly detection of wireless data acquisition in power system based on spark |
| title_sort | research on anomaly detection of wireless data acquisition in power system based on spark |
| topic | Internet of Things Wireless data acquisition Data anomaly Spatial vector model |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722002244 |
| work_keys_str_mv | AT caiyanpei researchonanomalydetectionofwirelessdataacquisitioninpowersystembasedonspark AT shuaizhang researchonanomalydetectionofwirelessdataacquisitioninpowersystembasedonspark AT xiaoningzeng researchonanomalydetectionofwirelessdataacquisitioninpowersystembasedonspark |