| Title | Non-intrusive Runtime Monitoring for Power System Intelligent Terminal Based on Improved Deep Belief Networks (I-DBN) |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | LV, Zhining, HU, Ziheng, NING, Baifeng, DING, Lifu, Yan, Gangfeng, SHI, Xiasheng |
| Conference Name | 2019 4th International Conference on Power and Renewable Energy (ICPRE) |
| Keywords | belief networks, business-level monitoring platform, business-level security monitoring, composability, Cyber Attack Detection, data acquisition, deep belief network, fault detection, fault diagnosis, improved deep belief networks, nonintrusive business-level monitoring platform, nonintrusive data collection, nonintrusive runtime monitoring, power companies, power distribution, power grid, Power industry, Power Management, power meters, power system analysis computing, power system faults, power system intelligent terminal equipment, power system management, power system measurement, power system security, pubcrawl, Resiliency, runtime monitoring, smart power grids |
| Abstract | Power system intelligent terminal equipment is widely used in real-time monitoring, data acquisition, power management, power distribution and other tasks of smart grid. The power system intelligent terminal can obtain various information of users and power companies in the power grid, but there is still a lack of protection means for the connection and communication process of the terminal components. In this paper, a novel method based on improved deep belief network(IDBN) is proposed to accomplish the business-level security monitoring and attack detection of power system terminal. A non-intrusive business-level monitoring platform for power system terminals is established, which uses energy metering intelligent terminals as an example for non-intrusive data collection. Based on this platform, the I-DBN extracts the spatial and temporal attack characteristics of the external monitoring data of the system. Some fault conditions and cyber attacks of the model have been simulated to demonstrate the effectiveness of the proposed detection method and the results show excellent performance. The method and platform proposed in this paper can be extended to other services in the power industry, providing a theoretical basis and implementation method for realizing the security monitoring of power system intelligent terminals from the business level. |
| DOI | 10.1109/ICPRE48497.2019.9034805 |
| Citation Key | lv_non-intrusive_2019 |
Non-intrusive Runtime Monitoring for Power System Intelligent Terminal Based on Improved Deep Belief Networks (I-DBN)