Aiming at power system security threats such as failures and external attacks, power system security simulation technologies study system behaviors through simulation experiments or numerical calculation. As information technologies such as automatic control, network communication, and artificial intelligence are widely used, power system has developed into an cyber-physical system(CPS) which is deeply coupled by physical system and information system.The faults caused by physical damage or network attack are interrelated in the power system and can spread across domains, which result in new security threats. Power system security simulation is facing new challenges. This paper reviews security incidents with significant impact on the power system in the past, and analyzes the requirement and development of power system security simulation technologies from three dimensions, i.e., engineering security, network security, and cyber-physical integrated security. From these dimensions, the representative security simulation testbeds are classified and summarized. Moreover, the challenges and development trends of power system security simulation technologies are explored.
With the high integration of computing units and physical objects, cyber and physical systems are gradually coupled into cyber-physical systems (CPSs). According to the laws of physical systems and operation flow in CPSs, unknown CPS data can be inferred from other known data. The inferred data leakage threat is triggered when an accurate inference path connects between low-and high-security domain data. In this paper, by analyzing CPS data leakage accidents caused by data inference, paradigms of data leakage threats are proposed from two dimensions: data theft and data inference. Data inference is classified into three problem types: state estimation, parameter identification, and blind source separation. The algorithms for data inference are categorized as model-driven, data-driven, and data-model-driven methods. In the case of an electricity market, the process of inferring key parameters of a power system from public electricity price data is demonstrated, verifying that data inference can cause severe CPS data leakage threats. Meanwhile, the challenges of existing data protection methods are investigated. Additionally, the future research of CPS data inference defense and data security governance is discussed.
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