基于暂态关键特征逻辑推理的复杂电网响应驱动暂态稳定性判别OA北大核心CSTPCD

Real-time transient stability assessment is the core of response-driven stability control.With renewable energy and DC transmissions connecting to power systems,the complexity of grid's structure,operation and characteristics increases significantly,which have challenged the effectiveness and accuracy of existing stability assessment methods.For real-time stability assessment in the complex power system including renewable energy and DC transmissions,an adaptive network based fuzzy inference system(ANFIS)based model-free and interpretable stability assessment method was proposed by using measurable transient energy features.First,based on the energy relation of generator motion equation and the Tellegen's theorem,the transient energy function using the response information of the complex grid was constructed and its energy conservation was also validated.Then,according to the conversion characteristic between kinetic energy and potential energy,the stability prediction factor was defined,which was combined with maximum angle deviation to obtain the key features of the stability judgment.A mapping model between the key features and stability status based on ANFIS adaptive logic reasoning was proposed to realize real-time prediction of transient stability.Finally,the quantitative analysis of the relationship between the key features and system stability status was illustrated in a simple system,and the effectiveness and generalization of the proposed method were verified in the modified IEEE 1 0-machine 39-bus system with new energies and DC transmissions. Firstly,based on the energy relation of the generator rotor movement and the Tellegen's theorem,a refined energy function model was developed by using the response information of a complex grid,and the energy conservation property of the model was proven.The energy function model can depict the energy response characteristics of various components in the system.By introducing the virtual impedance branch,the potential energy of each component can be conveniently calculated using the pre-fault equilibrium point of power system,which does not depend on the component model and its parameters,showing good generality.Secondly,the conversion characteristics between kinetic energy and potential energy were analyzed under stable and unstable conditions,and then a stability prediction factor(SPF)were defined at the maximum potential energy point.Together with the maximum angle deviation(MAD)at the same point,the critical features SPF and MAD for stability assessment were obtained,which have strong representational ability to indicate the stable/unstable status of the system.Finally,a response-driven stability discrimination model was constructed with the key features SPF and MAD.The stability discrimination model was established based on logical reasoning ANFIS,which can obtain an interpretable mapping model between the key features and the stable/unstable status of the system.This model enables real-time transient stability assessment with interpretability. This paper quantitatively analyzed the relationship between the key features(SPF and MAD)and system stability status in a modified simple IEEE 9-bus system,and evaluated the effectiveness and generalization of the proposed stability discrimination model in a modified IEEE 1 0-machine 39-bus system with new energy sources and DC transmissions.The simulation results demonstrated that the proposed key features SPF and MAD,showing strong physical attributes associated with the system stability,can effectively reflect the system stability status.Compared to other AI stability discrimination models,the proposed data-driven ANFIS stability discrimination model had a higher accuracy of stability discrimination in a complex power grid.Thus,it can be effectively applied in subsequent emergency response-driven stability control. The conclusions of this paper are given as follows:(1)The key features SPF and MAD,extracted from the constructed energy function model,have a strong correlation with the system stable/unstable status.They can directly characterize the system stability condition compared to other response features.(2)The data-driven ANFIS based stability discrimination model establishes the if-then logical inference process between the input features(SPF and MAD)and the system stable/unstable status.It can detect the unstable condition in real-time and show higher accuracy of stability discrimination in a complex power grid compared to other AI stability discrimination methods.(3)The proposed stability analysis method maintains high accuracy even in the presence of changes in the power grid topology,showing good generalization performance.