Trust evaluation model of power terminal based on equipment portrait

As the number of power terminals continues to increase and their usage becomes more widespread,the security of power systems is under great threat.In response to the lack of effective trust evaluation methods for terminals,we propose a trust evaluation model based on equipment portraits for power terminals.First,we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic.Second,we propose an exception evaluation method based on syntax and semantics.The key fields of each message are extracted,and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal.Thus,by combining the network flow order,syntax,and semantic analysis,an equipment portrait can be constructed to guarantee security of the power network terminals.We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time.Finally,the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate,as well as a higher real-time performance,which is more suitable for power terminals.

Confidence Estimation Transformer for Long-Term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching

Expansion of renewable energy could help realize the goals of peaking carbon dioxide emissions and carbon neutralization.Some existing grid dispatching methods integrating short-term renewable energy prediction and reinforcement learning(RL)have been proven to alleviate the adverse impact of energy fluctuations risk.However,these methods omit long-term output prediction,which leads to stability and security problems on optimal power flow.This paper proposes a confidence estimation Transformer for long-term renewable energy forecasting in reinforcement learning-based power grid dispatching(Conformer-RLpatching).Conformer-RLpatching predicts long-term active output of each renewable energy generator with an enhanced Transformer to ensure stable operation of the hybrid energy grid and improve the utilization rate of renewable energy,thus boosting dispatching performance.Furthermore,a confidence estimation method is proposed to reduce the prediction error of renewable energy.Meanwhile,a dispatching necessity evaluation mechanism is put forward to decide whether the active output of a generator needs to be adjusted.Experiments carried out on the SG-126 power grid simulator show that Conformer-RLpatching achieves great improvement over the second best algorithm DDPG in security score by 25.8%and achieves a better total reward compared with the golden medal team in the power grid dispatching competition sponsored by State Grid Corporation of China under the same simulation environment.Codes are outsourced in https://github.com/BUPT-ANTlab/Conformer-RLpatching.