Biblio

The widely adopted dynamic line rating (DLR) mechanism can improve the operation efficiency for industrial and commercial power systems. However, the predicted environmental parameters used in DLR bring great uncertainty to transmission line capacity estimation and may introduce system security risk if over-optimistic estimation is adopted in the operation process, which could affect the electrical safety of industrial and commercial power systems in multiple cases. Therefore, it becomes necessary to establish a system operation security assessment model to reduce the risk and provide operational guidance to enhance electrical safety. This paper aims to solve the electrical safety problems caused by the transmission line under DLR mechanism. An operation security assessment method of transmission lines considering DLR uncertainty is proposed to visualize the safety margin under the given operation strategy and optimally setting transmission line capacity while taking system safety into account. With the help of robust optimization (RO) techniques, the uncertainty is characterized and a risk-averse transmission line rating guidance can be established to determine the safety margin of line capacity for system operation. In this way, the operational security for industrial and commercial power systems can be enhanced by reducing the unsafe conditions while the operational efficiency benefit provided by DLR mechanism still exist.

For hours-ahead assessment of power systems with a high penetration level of renewable generation, a large number of uncertain scenarios should be checked to ensure the frequency security of the system after the severe power disturbance following HVDC blocking. In this situation, the full time-domain simulation is unsuitable as a result of the heavy calculation burden. To fulfill the quick assessment of the frequency security, the online frequency security assessment framework based on deep learning is proposed in this paper. The Deep Belief Network (DBN) method is used to establish the framework. The sample generation method is researched to generate representative samples for the purposed of higher assessment accuracy. A large-scale AC-DC interconnected power grid is adopted to verify the validity of the proposed assessment method.

The intelligent power grid is composed of a large number of industrial control equipment, and most of the industrial control equipment has security holes, which are vulnerable to malicious attacks and affect the normal operation of the power grid. By analyzing the security vulnerability of the firmware of industrial control equipment, the vulnerability can be detected in advance and the power grid's ability to resist attack can be improved. In this paper, a kind of industrial control device firmware protocol vulnerabilities associated technology, through the technology of information extraction from the mass grid device firmware device attributes and extract the industrial control system, the characteristics of the construction of industrial control system device firmware and published vulnerability information correlation, faster in the industrial control equipment safety inspection found vulnerabilities.