Research on Lean Analysis Algorithm for Equipment Centralized Monitoring in Big Data Era

• Published in: Journal of physics. Conference series Vol. 1437; no. 1; pp. 12085 - 12090
• Main Authors: Chen, Tan, yong, Wang, Xiaomin, Lu, Jiang, Wu, Guangcheng, Zhang, Ziwei, Bai
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.01.2020
• Subjects: Algorithms; Big Data; Classification; Conditional probability; Defects; Evaluation; Monitoring; Normalizing (statistics); Physics; Smart grid; Substations
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/1437/1/012085

The equipment monitoring brought by the smart grid big data is difficult to effectively supervise the operation status of the whole network substation, and the typical defects (familial defects) are difficult to classify and locate. This paper proposes the substation operation state evaluation algorithm and typical defect classification algorithm. The operating state evaluation algorithm of the substation is based on different operation data generated by the substation. By normalizing the data of different dimensions, the substation is divided into different operating state levels. The typical defect classification algorithm establishes and maintains the historical experience database, and calculates the conditional probability of each information item to realize the correlation between the signal and the defect, and finally judge whether the signal is from a typical defect. These two algorithms are effective means for equipment monitoring professionals to realize intelligent supervision of substations and equipment in the era of big data.